from models import ModelBase from models import TrainingDataType import numpy as np from nnlib import DSSIMMaskLossClass from nnlib import conv from nnlib import upscale from facelib import FaceType class Model(ModelBase): encoderH5 = 'encoder.h5' decoder_srcH5 = 'decoder_src.h5' decoder_dstH5 = 'decoder_dst.h5' #override def onInitialize(self, **in_options): self.set_vram_batch_requirements( {2:2,3:4,4:8,5:16,6:32,7:32,8:32,9:48} ) ae_input_layer = self.keras.layers.Input(shape=(64, 64, 3)) mask_layer = self.keras.layers.Input(shape=(64, 64, 1)) #same as output self.encoder = self.Encoder(ae_input_layer, self.created_vram_gb) self.decoder_src = self.Decoder(self.created_vram_gb) self.decoder_dst = self.Decoder(self.created_vram_gb) if not self.is_first_run(): self.encoder.load_weights (self.get_strpath_storage_for_file(self.encoderH5)) self.decoder_src.load_weights (self.get_strpath_storage_for_file(self.decoder_srcH5)) self.decoder_dst.load_weights (self.get_strpath_storage_for_file(self.decoder_dstH5)) self.autoencoder_src = self.keras.models.Model([ae_input_layer,mask_layer], self.decoder_src(self.encoder(ae_input_layer))) self.autoencoder_dst = self.keras.models.Model([ae_input_layer,mask_layer], self.decoder_dst(self.encoder(ae_input_layer))) if self.is_training_mode: self.autoencoder_src, self.autoencoder_dst = self.to_multi_gpu_model_if_possible ( [self.autoencoder_src, self.autoencoder_dst] ) optimizer = self.keras.optimizers.Adam(lr=5e-5, beta_1=0.5, beta_2=0.999) dssimloss = DSSIMMaskLossClass(self.tf)([mask_layer]) self.autoencoder_src.compile(optimizer=optimizer, loss=[dssimloss, 'mae']) self.autoencoder_dst.compile(optimizer=optimizer, loss=[dssimloss, 'mae']) if self.is_training_mode: from models import TrainingDataGenerator f = TrainingDataGenerator.SampleTypeFlags self.set_training_data_generators ([ TrainingDataGenerator(TrainingDataType.FACE, self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size, output_sample_types=[ [f.WARPED_TRANSFORMED | f.HALF_FACE | f.MODE_BGR, 64], [f.TRANSFORMED | f.HALF_FACE | f.MODE_BGR, 64], [f.TRANSFORMED | f.HALF_FACE | f.MODE_M | f.MASK_FULL, 64] ], random_flip=True ), TrainingDataGenerator(TrainingDataType.FACE, self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, output_sample_types=[ [f.WARPED_TRANSFORMED | f.HALF_FACE | f.MODE_BGR, 64], [f.TRANSFORMED | f.HALF_FACE | f.MODE_BGR, 64], [f.TRANSFORMED | f.HALF_FACE | f.MODE_M | f.MASK_FULL, 64] ], random_flip=True ) ]) #override def onSave(self): self.save_weights_safe( [[self.encoder, self.get_strpath_storage_for_file(self.encoderH5)], [self.decoder_src, self.get_strpath_storage_for_file(self.decoder_srcH5)], [self.decoder_dst, self.get_strpath_storage_for_file(self.decoder_dstH5)]] ) #override def onTrainOneEpoch(self, sample): warped_src, target_src, target_src_full_mask = sample[0] warped_dst, target_dst, target_dst_full_mask = sample[1] loss_src = self.autoencoder_src.train_on_batch( [warped_src, target_src_full_mask], [target_src, target_src_full_mask] ) loss_dst = self.autoencoder_dst.train_on_batch( [warped_dst, target_dst_full_mask], [target_dst, target_dst_full_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] 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 [ ('H64', np.concatenate ( st, axis=0 ) ) ] def predictor_func (self, face): face_64_bgr = face[...,0:3] face_64_mask = np.expand_dims(face[...,3],-1) x, mx = self.autoencoder_src.predict ( [ np.expand_dims(face_64_bgr,0), np.expand_dims(face_64_mask,0) ] ) x, mx = x[0], mx[0] return np.concatenate ( (x,mx), -1 ) #override def get_converter(self, **in_options): from models import ConverterMasked if 'masked_hist_match' not in in_options.keys() or in_options['masked_hist_match'] is None: in_options['masked_hist_match'] = True if 'erode_mask_modifier' not in in_options.keys(): in_options['erode_mask_modifier'] = 0 in_options['erode_mask_modifier'] += 100 if 'blur_mask_modifier' not in in_options.keys(): in_options['blur_mask_modifier'] = 0 in_options['blur_mask_modifier'] += 100 return ConverterMasked(self.predictor_func, predictor_input_size=64, output_size=64, face_type=FaceType.HALF, **in_options) def Encoder(self, input_layer, created_vram_gb): x = input_layer if created_vram_gb >= 4: x = conv(self.keras, x, 128) x = conv(self.keras, x, 256) x = conv(self.keras, x, 512) x = conv(self.keras, x, 1024) x = self.keras.layers.Dense(1024)(self.keras.layers.Flatten()(x)) x = self.keras.layers.Dense(4 * 4 * 1024)(x) x = self.keras.layers.Reshape((4, 4, 1024))(x) x = upscale(self.keras, x, 512) else: x = conv(self.keras, x, 128 ) x = conv(self.keras, x, 256 ) x = conv(self.keras, x, 512 ) x = conv(self.keras, x, 768 ) x = self.keras.layers.Dense(512)(self.keras.layers.Flatten()(x)) x = self.keras.layers.Dense(4 * 4 * 512)(x) x = self.keras.layers.Reshape((4, 4, 512))(x) x = upscale(self.keras, x, 256) return self.keras.models.Model(input_layer, x) def Decoder(self, created_vram_gb): if created_vram_gb >= 4: input_ = self.keras.layers.Input(shape=(8, 8, 512)) else: input_ = self.keras.layers.Input(shape=(8, 8, 256)) x = input_ x = upscale(self.keras, x, 256) x = upscale(self.keras, x, 128) x = upscale(self.keras, x, 64) y = input_ #mask decoder y = upscale(self.keras, y, 256) y = upscale(self.keras, y, 128) y = upscale(self.keras, y, 64) x = self.keras.layers.convolutional.Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(x) y = self.keras.layers.convolutional.Conv2D(1, kernel_size=5, padding='same', activation='sigmoid')(y) return self.keras.models.Model(input_, [x,y])