import numpy as np from nnlib import nnlib from models import ModelBase from facelib import FaceType from samplelib import * from interact import interact as io class Model(ModelBase): #override def onInitializeOptions(self, is_first_run, ask_override): if is_first_run or ask_override: def_pixel_loss = self.options.get('pixel_loss', False) self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", 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.") else: self.options['pixel_loss'] = self.options.get('pixel_loss', False) #override def onInitialize(self): exec(nnlib.import_all(), locals(), globals()) self.set_vram_batch_requirements( {4.5:4} ) ae_input_layer = Input(shape=(128, 128, 3)) mask_layer = Input(shape=(128, 128, 1)) #same as output self.encoder, self.decoder_src, self.decoder_dst = self.Build(ae_input_layer) if not self.is_first_run(): weights_to_load = [ [self.encoder , 'encoder.h5'], [self.decoder_src, 'decoder_src.h5'], [self.decoder_dst, 'decoder_dst.h5'] ] self.load_weights_safe(weights_to_load) rec_src = self.decoder_src(self.encoder(ae_input_layer)) rec_dst = self.decoder_dst(self.encoder(ae_input_layer)) self.autoencoder_src = Model([ae_input_layer,mask_layer], rec_src) self.autoencoder_dst = Model([ae_input_layer,mask_layer], rec_dst) self.autoencoder_src.compile(optimizer=Adam(lr=5e-5, beta_1=0.5, beta_2=0.999), loss=[DSSIMMSEMaskLoss(mask_layer, is_mse=self.options['pixel_loss']), 'mse'] ) self.autoencoder_dst.compile(optimizer=Adam(lr=5e-5, beta_1=0.5, beta_2=0.999), loss=[DSSIMMSEMaskLoss(mask_layer, is_mse=self.options['pixel_loss']), 'mse'] ) self.convert = K.function([ae_input_layer], rec_src) if self.is_training_mode: t = SampleProcessor.Types output_sample_types=[ { 'types': (t.IMG_WARPED_TRANSFORMED, t.FACE_TYPE_FULL, t.MODE_BGR), 'resolution':128}, { 'types': (t.IMG_TRANSFORMED, t.FACE_TYPE_FULL, t.MODE_BGR), 'resolution':128}, { 'types': (t.IMG_TRANSFORMED, t.FACE_TYPE_FULL, t.MODE_M), 'resolution':128} ] 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 get_model_filename_list(self): return [[self.encoder, 'encoder.h5'], [self.decoder_src, 'decoder_src.h5'], [self.decoder_dst, 'decoder_dst.h5']] #override def onSave(self): self.save_weights_safe( self.get_model_filename_list() ) #override def onTrainOneIter(self, sample, generators_list): warped_src, target_src, target_src_mask = sample[0] warped_dst, target_dst, target_dst_mask = sample[1] loss_src = self.autoencoder_src.train_on_batch( [warped_src, target_src_mask], [target_src, target_src_mask] ) loss_dst = self.autoencoder_dst.train_on_batch( [warped_dst, target_dst_mask], [target_dst, target_dst_mask] ) return ( ('loss_src', loss_src[0]), ('loss_dst', loss_dst[0]) ) #override def onGetPreview(self, sample): test_A = sample[0][1][0:4] #first 4 samples test_A_m = sample[0][2][0:4] #first 4 samples test_B = sample[1][1][0:4] test_B_m = sample[1][2][0:4] AA, mAA = self.autoencoder_src.predict([test_A, test_A_m]) AB, mAB = self.autoencoder_src.predict([test_B, test_B_m]) BB, mBB = self.autoencoder_dst.predict([test_B, test_B_m]) mAA = np.repeat ( mAA, (3,), -1) mAB = np.repeat ( mAB, (3,), -1) mBB = np.repeat ( mBB, (3,), -1) st = [] for i in range(0, len(test_A)): st.append ( np.concatenate ( ( test_A[i,:,:,0:3], AA[i], #mAA[i], test_B[i,:,:,0:3], BB[i], #mBB[i], AB[i], #mAB[i] ), axis=1) ) return [ ('DF', np.concatenate ( st, axis=0 ) ) ] def predictor_func (self, face=None, dummy_predict=False): if dummy_predict: self.convert ([ np.zeros ( (1, 128, 128, 3), dtype=np.float32 ) ]) else: x, mx = self.convert ( [ face[np.newaxis,...] ] ) return x[0], mx[0][...,0] #override def get_ConverterConfig(self): import converters return self.predictor_func, (128,128,3), converters.ConverterConfigMasked(face_type=FaceType.FULL, default_mode=4) def Build(self, input_layer): exec(nnlib.code_import_all, locals(), globals()) def downscale (dim): def func(x): return LeakyReLU(0.1)(Conv2D(dim, 5, strides=2, padding='same')(x)) return func def upscale (dim): def func(x): return PixelShuffler()(LeakyReLU(0.1)(Conv2D(dim * 4, 3, strides=1, padding='same')(x))) return func def Encoder(input_layer): x = input_layer x = downscale(128)(x) x = downscale(256)(x) x = downscale(512)(x) x = downscale(1024)(x) x = Dense(512)(Flatten()(x)) x = Dense(8 * 8 * 512)(x) x = Reshape((8, 8, 512))(x) x = upscale(512)(x) return Model(input_layer, x) def Decoder(): input_ = Input(shape=(16, 16, 512)) x = input_ x = upscale(512)(x) x = upscale(256)(x) x = upscale(128)(x) y = input_ #mask decoder y = upscale(512)(y) y = upscale(256)(y) y = upscale(128)(y) x = Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(x) y = Conv2D(1, kernel_size=5, padding='same', activation='sigmoid')(y) return Model(input_, [x,y]) return Encoder(input_layer), Decoder(), Decoder()