mirror of
https://github.com/iperov/DeepFaceLab.git
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179 lines
7.3 KiB
Python
179 lines
7.3 KiB
Python
import numpy as np
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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, FUNIT
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from samplelib import *
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class TrueFaceModel(ModelBase):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs,
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ask_sort_by_yaw=False,
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ask_random_flip=False,
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ask_src_scale_mod=False)
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#override
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def onInitializeOptions(self, is_first_run, ask_override):
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default_resolution = 128
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default_face_type = 'f'
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if is_first_run:
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resolution = self.options['resolution'] = io.input_int(f"Resolution ( 64-256 ?:help skip:{default_resolution}) : ", 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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else:
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self.options['resolution'] = self.options.get('resolution', default_resolution)
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if is_first_run:
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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="").lower()
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else:
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self.options['face_type'] = self.options.get('face_type', default_face_type)
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#override
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def onInitialize(self, batch_size=-1, **in_options):
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exec(nnlib.code_import_all, locals(), globals())
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self.set_vram_batch_requirements({2:1,3:1,4:4,5:8,6:16})
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resolution = self.options['resolution']
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face_type = self.face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF
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self.model = FUNIT( face_type_str=FaceType.toString(face_type),
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batch_size=self.batch_size,
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encoder_nf=64,
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encoder_downs=2,
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encoder_res_blk=2,
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class_downs=4,
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class_nf=64,
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class_latent=64,
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mlp_nf=256,
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mlp_blks=2,
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dis_nf=64,
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dis_res_blks=10,
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num_classes=2,
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subpixel_decoder=True,
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initialize_weights=self.is_first_run(),
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is_training=self.is_training_mode
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)
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if not self.is_first_run():
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self.load_weights_safe(self.model.get_model_filename_list())
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t = SampleProcessor.Types
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face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF
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if self.is_training_mode:
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output_sample_types=[ {'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'resolution':resolution, 'normalize_tanh':True},
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]
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self.set_training_data_generators ([
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SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=True),
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output_sample_types=output_sample_types ),
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SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=True),
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output_sample_types=output_sample_types )
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])
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else:
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generator = SampleGeneratorFace(self.training_data_src_path, batch_size=1,
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sample_process_options=SampleProcessor.Options(),
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output_sample_types=[ {'types': (t.IMG_SOURCE, face_type, t.MODE_BGR), 'resolution':resolution, 'normalize_tanh':True} ] )
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io.log_info("Calculating average src face style...")
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codes = []
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for i in io.progress_bar_generator(range(generator.get_total_sample_count())):
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codes += self.model.get_average_class_code( generator.generate_next() )
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self.average_class_code = np.mean ( np.array(codes), axis=0 )[None,...]
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#override
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def get_model_filename_list(self):
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return self.model.get_model_filename_list()
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#override
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def onSave(self):
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self.save_weights_safe(self.model.get_model_filename_list())
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#override
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def onTrainOneIter(self, generators_samples, generators_list):
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bs = self.batch_size
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lbs = bs // 2
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hbs = bs - lbs
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src, = generators_samples[0]
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dst, = generators_samples[1]
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xa = np.concatenate ( [src[0:lbs], dst[0:lbs]], axis=0 )
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la = np.concatenate ( [ np.array ([0]*lbs, np.int32),
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np.array ([1]*lbs, np.int32) ] )
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xb = np.concatenate ( [src[lbs:], dst[lbs:]], axis=0 )
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lb = np.concatenate ( [ np.array ([0]*hbs, np.int32),
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np.array ([1]*hbs, np.int32) ] )
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rnd_list = np.arange(lbs*2)
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np.random.shuffle(rnd_list)
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xa = xa[rnd_list,...]
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la = la[rnd_list,...]
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la = la[...,None]
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rnd_list = np.arange(hbs*2)
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np.random.shuffle(rnd_list)
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xb = xb[rnd_list,...]
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lb = lb[rnd_list,...]
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lb = lb[...,None]
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G_loss, D_loss = self.model.train(xa,la,xb,lb)
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return ( ('G_loss', G_loss), ('D_loss', D_loss), )
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#override
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def onGetPreview(self, generators_samples):
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xa = generators_samples[0][0]
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xb = generators_samples[1][0]
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view_samples = min(4, xa.shape[0])
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s_xa_mean = self.model.get_average_class_code([xa])[0][None,...]
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s_xb_mean = self.model.get_average_class_code([xb])[0][None,...]
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s_xab_mean = self.model.get_average_class_code([ np.concatenate( [xa,xb], axis=0) ])[0][None,...]
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lines = []
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for i in range(view_samples):
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xaxa, = self.model.convert ([ xa[i:i+1], s_xa_mean ] )
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xbxb, = self.model.convert ([ xb[i:i+1], s_xb_mean ] )
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xbxa, = self.model.convert ([ xb[i:i+1], s_xa_mean ] )
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xa_i,xb_i,xaxa,xbxb,xbxa = [ np.clip(x/2+0.5, 0, 1) for x in [xa[i], xb[i], xaxa[0],xbxb[0],xbxa[0]] ]
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lines += [ np.concatenate( (xa_i, xaxa, xb_i, xbxb, xbxa), axis=1) ]
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r = np.concatenate ( lines, axis=0 )
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return [ ('TrueFace', r ) ]
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def predictor_func (self, face=None, dummy_predict=False):
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if dummy_predict:
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self.model.convert ([ np.zeros ( (1, self.options['resolution'], self.options['resolution'], 3), dtype=np.float32 ), self.average_class_code ])
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else:
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bgr, = self.model.convert ([ face[np.newaxis,...]*2-1, self.average_class_code ])
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return bgr[0] / 2 + 0.5
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#override
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def get_ConverterConfig(self):
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import converters
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return self.predictor_func, (self.options['resolution'], self.options['resolution'], 3), converters.ConverterConfigMasked(face_type=self.face_type,
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default_mode = 1,
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clip_hborder_mask_per=0.0625 if (self.face_type == FaceType.FULL) else 0,
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)
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Model = TrueFaceModel
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