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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-05 20:42:11 -07:00
AVATAR now 256
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iperov
parent
f3af962b1c
commit
d23c8ecfb9
1 changed files with 28 additions and 28 deletions
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@ -11,8 +11,8 @@ class Model(ModelBase):
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encoder64H5 = 'encoder64.h5'
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decoder64_srcH5 = 'decoder64_src.h5'
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decoder64_dstH5 = 'decoder64_dst.h5'
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encoder128H5 = 'encoder128.h5'
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decoder128_srcH5 = 'decoder128_src.h5'
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encoder256H5 = 'encoder256.h5'
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decoder256_srcH5 = 'decoder256_src.h5'
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#override
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def onInitialize(self, **in_options):
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@ -22,23 +22,23 @@ class Model(ModelBase):
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self.set_vram_batch_requirements( {4:8,5:16,6:20,7:24,8:32,9:48} )
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self.encoder64, self.decoder64_src, self.decoder64_dst, self.encoder128, self.decoder128_src = self.BuildAE()
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self.encoder64, self.decoder64_src, self.decoder64_dst, self.encoder256, self.decoder256_src = self.BuildAE()
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img_shape64 = (64,64,1)
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img_shape128 = (256,256,3)
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img_shape256 = (256,256,3)
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if not self.is_first_run():
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self.encoder64.load_weights (self.get_strpath_storage_for_file(self.encoder64H5))
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self.decoder64_src.load_weights (self.get_strpath_storage_for_file(self.decoder64_srcH5))
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self.decoder64_dst.load_weights (self.get_strpath_storage_for_file(self.decoder64_dstH5))
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self.encoder128.load_weights (self.get_strpath_storage_for_file(self.encoder128H5))
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self.decoder128_src.load_weights (self.get_strpath_storage_for_file(self.decoder128_srcH5))
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self.encoder256.load_weights (self.get_strpath_storage_for_file(self.encoder256H5))
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self.decoder256_src.load_weights (self.get_strpath_storage_for_file(self.decoder256_srcH5))
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if self.is_training_mode:
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self.encoder64, self.decoder64_src, self.decoder64_dst, self.encoder128, self.decoder128_src = self.to_multi_gpu_model_if_possible ( [self.encoder64, self.decoder64_src, self.decoder64_dst, self.encoder128, self.decoder128_src] )
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self.encoder64, self.decoder64_src, self.decoder64_dst, self.encoder256, self.decoder256_src = self.to_multi_gpu_model_if_possible ( [self.encoder64, self.decoder64_src, self.decoder64_dst, self.encoder256, self.decoder256_src] )
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input_src_64 = keras.layers.Input(img_shape64)
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input_src_target64 = keras.layers.Input(img_shape64)
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input_src_target128 = keras.layers.Input(img_shape128)
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input_src_target256 = keras.layers.Input(img_shape256)
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input_dst_64 = keras.layers.Input(img_shape64)
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input_dst_target64 = keras.layers.Input(img_shape64)
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@ -56,18 +56,18 @@ class Model(ModelBase):
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self.keras.optimizers.Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(total64_loss, self.encoder64.trainable_weights + self.decoder64_src.trainable_weights + self.decoder64_dst.trainable_weights)
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)
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src_code128 = self.encoder128(input_src_64)
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rec_src128 = self.decoder128_src(src_code128)
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src128_loss = tf_dssim(tf, input_src_target128, rec_src128)
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src_code256 = self.encoder256(input_src_64)
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rec_src256 = self.decoder256_src(src_code256)
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src256_loss = tf_dssim(tf, input_src_target256, rec_src256)
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self.ed128_train = K.function ([input_src_64, input_src_target128],[K.mean(src128_loss)],
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self.keras.optimizers.Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(src128_loss, self.encoder128.trainable_weights + self.decoder128_src.trainable_weights)
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self.ed256_train = K.function ([input_src_64, input_src_target256],[K.mean(src256_loss)],
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self.keras.optimizers.Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(src256_loss, self.encoder256.trainable_weights + self.decoder256_src.trainable_weights)
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)
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src_code128 = self.encoder128(rec_src64)
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rec_src128 = self.decoder128_src(src_code128)
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src_code256 = self.encoder256(rec_src64)
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rec_src256 = self.decoder256_src(src_code256)
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self.src128_view = K.function ([input_src_64], [rec_src128])
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self.src256_view = K.function ([input_src_64], [rec_src256])
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if self.is_training_mode:
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from models import TrainingDataGenerator
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@ -91,17 +91,17 @@ class Model(ModelBase):
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self.save_weights_safe( [[self.encoder64, self.get_strpath_storage_for_file(self.encoder64H5)],
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[self.decoder64_src, self.get_strpath_storage_for_file(self.decoder64_srcH5)],
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[self.decoder64_dst, self.get_strpath_storage_for_file(self.decoder64_dstH5)],
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[self.encoder128, self.get_strpath_storage_for_file(self.encoder128H5)],
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[self.decoder128_src, self.get_strpath_storage_for_file(self.decoder128_srcH5)],
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[self.encoder256, self.get_strpath_storage_for_file(self.encoder256H5)],
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[self.decoder256_src, self.get_strpath_storage_for_file(self.decoder256_srcH5)],
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] )
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#override
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def onTrainOneEpoch(self, sample):
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warped_src64, target_src64, target_src128, target_src_source64_G, target_src_source128_GGG = sample[0]
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warped_dst64, target_dst64, target_dst_source64_G, target_dst_source128_GGG = sample[1]
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warped_src64, target_src64, target_src256, target_src_source64_G, target_src_source256_GGG = sample[0]
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warped_dst64, target_dst64, target_dst_source64_G, target_dst_source256_GGG = sample[1]
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loss64, = self.ed64_train ([warped_src64, target_src64, warped_dst64, target_dst64])
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loss256, = self.ed128_train ([warped_src64, target_src128])
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loss256, = self.ed256_train ([warped_src64, target_src256])
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return ( ('loss64', loss64), ('loss256', loss256) )
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@ -109,19 +109,19 @@ class Model(ModelBase):
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def onGetPreview(self, sample):
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n_samples = 4
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test_B = sample[1][2][0:n_samples]
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test_B128 = sample[1][3][0:n_samples]
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test_B256 = sample[1][3][0:n_samples]
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BB, = self.src128_view ([test_B])
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BB, = self.src256_view ([test_B])
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st = []
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for i in range(n_samples // 2):
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st.append ( np.concatenate ( (
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test_B128[i*2+0], BB[i*2+0], test_B128[i*2+1], BB[i*2+1],
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test_B256[i*2+0], BB[i*2+0], test_B256[i*2+1], BB[i*2+1],
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), axis=1) )
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return [ ('AVATAR', np.concatenate ( st, axis=0 ) ) ]
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def predictor_func (self, img):
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x, = self.src128_view ([ np.expand_dims(img, 0) ])[0]
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x, = self.src256_view ([ np.expand_dims(img, 0) ])[0]
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return x
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#override
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@ -155,10 +155,10 @@ class Model(ModelBase):
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x = keras.layers.Dense (64)(x)
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return keras.models.Model (_input, x)
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encoder128 = Encoder( keras.layers.Input ( (64, 64, 1) ) )
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encoder256 = Encoder( keras.layers.Input ( (64, 64, 1) ) )
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encoder64 = Encoder( keras.layers.Input ( (64, 64, 1) ) )
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def decoder128_3(encoder):
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def decoder256_3(encoder):
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decoder_input = keras.layers.Input ( K.int_shape(encoder.outputs[0])[1:] )
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x = decoder_input
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x = self.keras.layers.Dense(16 * 16 * 720)(x)
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@ -181,7 +181,7 @@ class Model(ModelBase):
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x = keras.layers.convolutional.Conv2D(1, kernel_size=5, padding='same', activation='sigmoid')(x)
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return keras.models.Model(decoder_input, x)
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return encoder64, decoder64_1(encoder64), decoder64_1(encoder64), encoder128, decoder128_3(encoder128)
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return encoder64, decoder64_1(encoder64), decoder64_1(encoder64), encoder256, decoder256_3(encoder256)
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from models import ConverterBase
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from facelib import FaceType
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