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153
models/Model_DF/Model.py
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153
models/Model_DF/Model.py
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from models import ModelBase
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from models import TrainingDataType
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import numpy as np
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import cv2
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from nnlib import DSSIMMaskLossClass
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from nnlib import conv
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from nnlib import upscale
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from facelib import FaceType
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class Model(ModelBase):
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encoderH5 = 'encoder.h5'
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decoder_srcH5 = 'decoder_src.h5'
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decoder_dstH5 = 'decoder_dst.h5'
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#override
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def onInitialize(self, **in_options):
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self.set_vram_batch_requirements( {5:16,6:16,7:16,8:24,9:24,10:32,11:32,12:32,13:48} )
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ae_input_layer = self.keras.layers.Input(shape=(128, 128, 3))
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mask_layer = self.keras.layers.Input(shape=(128, 128, 1)) #same as output
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self.encoder = self.Encoder(ae_input_layer)
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self.decoder_src = self.Decoder()
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self.decoder_dst = self.Decoder()
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if not self.is_first_run():
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self.encoder.load_weights (self.get_strpath_storage_for_file(self.encoderH5))
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self.decoder_src.load_weights (self.get_strpath_storage_for_file(self.decoder_srcH5))
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self.decoder_dst.load_weights (self.get_strpath_storage_for_file(self.decoder_dstH5))
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self.autoencoder_src = self.keras.models.Model([ae_input_layer,mask_layer], self.decoder_src(self.encoder(ae_input_layer)))
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self.autoencoder_dst = self.keras.models.Model([ae_input_layer,mask_layer], self.decoder_dst(self.encoder(ae_input_layer)))
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if self.is_training_mode:
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self.autoencoder_src, self.autoencoder_dst = self.to_multi_gpu_model_if_possible ( [self.autoencoder_src, self.autoencoder_dst] )
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optimizer = self.keras.optimizers.Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
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dssimloss = DSSIMMaskLossClass(self.tf)([mask_layer])
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self.autoencoder_src.compile(optimizer=optimizer, loss=[dssimloss, 'mse'] )
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self.autoencoder_dst.compile(optimizer=optimizer, loss=[dssimloss, 'mse'] )
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if self.is_training_mode:
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from models import TrainingDataGenerator
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f = TrainingDataGenerator.SampleTypeFlags
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self.set_training_data_generators ([
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TrainingDataGenerator(TrainingDataType.FACE, self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size, output_sample_types=[ [f.WARPED_TRANSFORMED | f.FULL_FACE | f.MODE_BGR, 128], [f.TRANSFORMED | f.FULL_FACE | f.MODE_BGR, 128], [f.TRANSFORMED | f.FULL_FACE | f.MODE_M | f.MASK_FULL, 128] ], random_flip=True ),
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TrainingDataGenerator(TrainingDataType.FACE, self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, output_sample_types=[ [f.WARPED_TRANSFORMED | f.FULL_FACE | f.MODE_BGR, 128], [f.TRANSFORMED | f.FULL_FACE | f.MODE_BGR, 128], [f.TRANSFORMED | f.FULL_FACE | f.MODE_M | f.MASK_FULL, 128] ], random_flip=True )
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])
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#override
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def onSave(self):
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self.save_weights_safe( [[self.encoder, self.get_strpath_storage_for_file(self.encoderH5)],
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[self.decoder_src, self.get_strpath_storage_for_file(self.decoder_srcH5)],
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[self.decoder_dst, self.get_strpath_storage_for_file(self.decoder_dstH5)]] )
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#override
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def onTrainOneEpoch(self, sample):
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warped_src, target_src, target_src_mask = sample[0]
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warped_dst, target_dst, target_dst_mask = sample[1]
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loss_src = self.autoencoder_src.train_on_batch( [warped_src, target_src_mask], [target_src, target_src_mask] )
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loss_dst = self.autoencoder_dst.train_on_batch( [warped_dst, target_dst_mask], [target_dst, target_dst_mask] )
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return ( ('loss_src', loss_src[0]), ('loss_dst', loss_dst[0]) )
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#override
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def onGetPreview(self, sample):
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test_A = sample[0][1][0:4] #first 4 samples
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test_A_m = sample[0][2][0:4] #first 4 samples
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test_B = sample[1][1][0:4]
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test_B_m = sample[1][2][0:4]
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AA, mAA = self.autoencoder_src.predict([test_A, test_A_m])
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AB, mAB = self.autoencoder_src.predict([test_B, test_B_m])
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BB, mBB = self.autoencoder_dst.predict([test_B, test_B_m])
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mAA = np.repeat ( mAA, (3,), -1)
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mAB = np.repeat ( mAB, (3,), -1)
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mBB = np.repeat ( mBB, (3,), -1)
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st = []
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for i in range(0, len(test_A)):
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st.append ( np.concatenate ( (
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test_A[i,:,:,0:3],
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AA[i],
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#mAA[i],
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test_B[i,:,:,0:3],
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BB[i],
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#mBB[i],
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AB[i],
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#mAB[i]
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), axis=1) )
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return [ ('DF', np.concatenate ( st, axis=0 ) ) ]
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def predictor_func (self, face):
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face_128_bgr = face[...,0:3]
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face_128_mask = np.expand_dims(face[...,3],-1)
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x, mx = self.autoencoder_src.predict ( [ np.expand_dims(face_128_bgr,0), np.expand_dims(face_128_mask,0) ] )
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x, mx = x[0], mx[0]
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return np.concatenate ( (x,mx), -1 )
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#override
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def get_converter(self, **in_options):
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from models import ConverterMasked
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if 'masked_hist_match' not in in_options.keys() or in_options['masked_hist_match'] is None:
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in_options['masked_hist_match'] = True
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if 'erode_mask_modifier' not in in_options.keys():
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in_options['erode_mask_modifier'] = 0
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in_options['erode_mask_modifier'] += 30
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if 'blur_mask_modifier' not in in_options.keys():
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in_options['blur_mask_modifier'] = 0
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return ConverterMasked(self.predictor_func, predictor_input_size=128, output_size=128, face_type=FaceType.FULL, clip_border_mask_per=0.046875, **in_options)
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def Encoder(self, input_layer):
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x = input_layer
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x = conv(self.keras, x, 128)
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x = conv(self.keras, x, 256)
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x = conv(self.keras, x, 512)
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x = conv(self.keras, x, 1024)
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x = self.keras.layers.Dense(512)(self.keras.layers.Flatten()(x))
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x = self.keras.layers.Dense(8 * 8 * 512)(x)
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x = self.keras.layers.Reshape((8, 8, 512))(x)
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x = upscale(self.keras, x, 512)
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return self.keras.models.Model(input_layer, x)
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def Decoder(self):
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input_ = self.keras.layers.Input(shape=(16, 16, 512))
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x = input_
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x = upscale(self.keras, x, 512)
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x = upscale(self.keras, x, 256)
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x = upscale(self.keras, x, 128)
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y = input_ #mask decoder
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y = upscale(self.keras, y, 512)
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y = upscale(self.keras, y, 256)
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y = upscale(self.keras, y, 128)
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x = self.keras.layers.convolutional.Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(x)
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y = self.keras.layers.convolutional.Conv2D(1, kernel_size=5, padding='same', activation='sigmoid')(y)
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return self.keras.models.Model(input_, [x,y])
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