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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-07 05:22:06 -07:00
Upgraded to TF version 1.13.2
Removed the wait at first launch for most graphics cards. Increased speed of training by 10-20%, but you have to retrain all models from scratch. SAEHD: added option 'use float16' Experimental option. Reduces the model size by half. Increases the speed of training. Decreases the accuracy of the model. The model may collapse or not train. Model may not learn the mask in large resolutions. true_face_training option is replaced by "True face power". 0.0000 .. 1.0 Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination. Comparison - https://i.imgur.com/czScS9q.png
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Colombo
parent
a3dfcb91b9
commit
76ca79216e
49 changed files with 1320 additions and 1297 deletions
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@ -20,117 +20,117 @@ TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentat
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class TernausNet(object):
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VERSION = 1
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def __init__ (self, name, resolution, face_type_str, load_weights=True, weights_file_root=None, training=False, place_model_on_cpu=False):
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nn.initialize()
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nn.initialize(data_format="NHWC")
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tf = nn.tf
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class Ternaus(nn.ModelBase):
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def on_build(self, in_ch, ch):
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self.features_0 = nn.Conv2D (in_ch, ch, kernel_size=3, padding='SAME')
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self.blurpool_0 = nn.BlurPool (filt_size=3)
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self.features_3 = nn.Conv2D (ch, ch*2, kernel_size=3, padding='SAME')
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self.blurpool_3 = nn.BlurPool (filt_size=3)
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self.features_6 = nn.Conv2D (ch*2, ch*4, kernel_size=3, padding='SAME')
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self.features_8 = nn.Conv2D (ch*4, ch*4, kernel_size=3, padding='SAME')
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self.blurpool_8 = nn.BlurPool (filt_size=3)
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self.features_11 = nn.Conv2D (ch*4, ch*8, kernel_size=3, padding='SAME')
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self.features_13 = nn.Conv2D (ch*8, ch*8, kernel_size=3, padding='SAME')
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self.blurpool_13 = nn.BlurPool (filt_size=3)
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self.features_16 = nn.Conv2D (ch*8, ch*8, kernel_size=3, padding='SAME')
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self.features_18 = nn.Conv2D (ch*8, ch*8, kernel_size=3, padding='SAME')
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self.blurpool_18 = nn.BlurPool (filt_size=3)
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self.conv_center = nn.Conv2D (ch*8, ch*8, kernel_size=3, padding='SAME')
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self.conv1_up = nn.Conv2DTranspose (ch*8, ch*4, kernel_size=3, padding='SAME')
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self.conv1 = nn.Conv2D (ch*12, ch*8, kernel_size=3, padding='SAME')
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self.conv2_up = nn.Conv2DTranspose (ch*8, ch*4, kernel_size=3, padding='SAME')
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self.conv2 = nn.Conv2D (ch*12, ch*8, kernel_size=3, padding='SAME')
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self.conv3_up = nn.Conv2DTranspose (ch*8, ch*2, kernel_size=3, padding='SAME')
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self.conv3 = nn.Conv2D (ch*6, ch*4, kernel_size=3, padding='SAME')
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self.conv4_up = nn.Conv2DTranspose (ch*4, ch, kernel_size=3, padding='SAME')
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self.conv4 = nn.Conv2D (ch*3, ch*2, kernel_size=3, padding='SAME')
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self.conv5_up = nn.Conv2DTranspose (ch*2, ch//2, kernel_size=3, padding='SAME')
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self.conv5 = nn.Conv2D (ch//2+ch, ch, kernel_size=3, padding='SAME')
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self.out_conv = nn.Conv2D (ch, 1, kernel_size=3, padding='SAME')
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def forward(self, inp):
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x, = inp
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x = x0 = tf.nn.relu(self.features_0(x))
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x = self.blurpool_0(x)
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x = x1 = tf.nn.relu(self.features_3(x))
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x = self.blurpool_3(x)
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x = self.blurpool_3(x)
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x = tf.nn.relu(self.features_6(x))
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x = x2 = tf.nn.relu(self.features_8(x))
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x = self.blurpool_8(x)
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x = self.blurpool_8(x)
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x = tf.nn.relu(self.features_11(x))
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x = x3 = tf.nn.relu(self.features_13(x))
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x = self.blurpool_13(x)
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x = tf.nn.relu(self.features_16(x))
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x = x4 = tf.nn.relu(self.features_18(x))
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x = self.blurpool_18(x)
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x = self.conv_center(x)
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x = tf.nn.relu(self.conv1_up(x))
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x = tf.nn.relu(self.conv1_up(x))
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x = tf.concat( [x,x4], -1)
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x = tf.nn.relu(self.conv1(x))
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x = tf.nn.relu(self.conv2_up(x))
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x = tf.nn.relu(self.conv2_up(x))
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x = tf.concat( [x,x3], -1)
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x = tf.nn.relu(self.conv2(x))
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x = tf.nn.relu(self.conv3_up(x))
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x = tf.nn.relu(self.conv3_up(x))
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x = tf.concat( [x,x2], -1)
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x = tf.nn.relu(self.conv3(x))
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x = tf.nn.relu(self.conv4_up(x))
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x = tf.nn.relu(self.conv4_up(x))
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x = tf.concat( [x,x1], -1)
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x = tf.nn.relu(self.conv4(x))
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x = tf.nn.relu(self.conv5_up(x))
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x = tf.nn.relu(self.conv5_up(x))
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x = tf.concat( [x,x0], -1)
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x = tf.nn.relu(self.conv5(x))
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x = tf.nn.sigmoid(self.out_conv(x))
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return x
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return x
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if weights_file_root is not None:
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weights_file_root = Path(weights_file_root)
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else:
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weights_file_root = Path(__file__).parent
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self.weights_path = weights_file_root / ('%s_%d_%s.npy' % (name, resolution, face_type_str) )
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e = tf.device('/CPU:0') if place_model_on_cpu else None
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if e is not None: e.__enter__()
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self.net = Ternaus(3, 64, name='Ternaus')
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if load_weights:
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self.net.load_weights (self.weights_path)
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self.net = Ternaus(3, 64, name='Ternaus')
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if load_weights:
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self.net.load_weights (self.weights_path)
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else:
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self.net.init_weights()
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if e is not None: e.__exit__(None,None,None)
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self.net.build_for_run ( [(tf.float32, (resolution,resolution,3))] )
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if e is not None: e.__exit__(None,None,None)
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self.net.build_for_run ( [(tf.float32, nn.get4Dshape (resolution,resolution,3) )] )
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if training:
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raise Exception("training not supported yet")
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"""
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if training:
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try:
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@ -149,9 +149,9 @@ class TernausNet(object):
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if 'CA.' in layer.name:
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conv_weights_list += [layer.weights[0]] #Conv2D kernel_weights
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CAInitializerMP ( conv_weights_list )
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"""
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"""
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"""
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if training:
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inp_t = Input ( (resolution, resolution, 3) )
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@ -195,124 +195,3 @@ class TernausNet(object):
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result = result[0]
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return result
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"""
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self.weights_path = weights_file_root / ('%s_%d_%s.h5' % (name, resolution, face_type_str) )
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self.net.build()
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self.net.features_0.set_weights ( self.model.get_layer('features.0').get_weights() )
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self.net.features_3.set_weights ( self.model.get_layer('features.3').get_weights() )
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self.net.features_6.set_weights ( self.model.get_layer('features.6').get_weights() )
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self.net.features_8.set_weights ( self.model.get_layer('features.8').get_weights() )
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self.net.features_11.set_weights ( self.model.get_layer('features.11').get_weights() )
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self.net.features_13.set_weights ( self.model.get_layer('features.13').get_weights() )
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self.net.features_16.set_weights ( self.model.get_layer('features.16').get_weights() )
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self.net.features_18.set_weights ( self.model.get_layer('features.18').get_weights() )
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self.net.conv_center.set_weights ( self.model.get_layer('CA.1').get_weights() )
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self.net.conv1_up.set_weights ( self.model.get_layer('CA.2').get_weights() )
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self.net.conv1.set_weights ( self.model.get_layer('CA.3').get_weights() )
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self.net.conv2_up.set_weights ( self.model.get_layer('CA.4').get_weights() )
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self.net.conv2.set_weights ( self.model.get_layer('CA.5').get_weights() )
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self.net.conv3_up.set_weights ( self.model.get_layer('CA.6').get_weights() )
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self.net.conv3.set_weights ( self.model.get_layer('CA.7').get_weights() )
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self.net.conv4_up.set_weights ( self.model.get_layer('CA.8').get_weights() )
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self.net.conv4.set_weights ( self.model.get_layer('CA.9').get_weights() )
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self.net.conv5_up.set_weights ( self.model.get_layer('CA.10').get_weights() )
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self.net.conv5.set_weights ( self.model.get_layer('CA.11').get_weights() )
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self.net.out_conv.set_weights ( self.model.get_layer('CA.12').get_weights() )
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self.net.build_for_run ( [ (tf.float32, (resolution,resolution,3)) ])
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self.net.save_weights (self.weights_path2)
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def extract (self, input_image):
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input_shape_len = len(input_image.shape)
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if input_shape_len == 3:
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input_image = input_image[np.newaxis,...]
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result = np.clip ( self.model.predict( [input_image] ), 0, 1.0 )
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result[result < 0.1] = 0 #get rid of noise
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if input_shape_len == 3:
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result = result[0]
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return result
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@staticmethod
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def BuildModel ( resolution, ngf=64):
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exec( nn.initialize(), locals(), globals() )
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inp = Input ( (resolution,resolution,3) )
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x = inp
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x = TernausNet.Flow(ngf=ngf)(x)
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model = Model(inp,x)
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return model
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@staticmethod
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def Flow(ngf=64):
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exec( nn.initialize(), locals(), globals() )
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def func(input):
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x = input
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x0 = x = Conv2D(ngf, kernel_size=3, strides=1, padding='same', activation='relu', name='features.0')(x)
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x = BlurPool(filt_size=3)(x)
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x1 = x = Conv2D(ngf*2, kernel_size=3, strides=1, padding='same', activation='relu', name='features.3')(x)
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x = BlurPool(filt_size=3)(x)
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x = Conv2D(ngf*4, kernel_size=3, strides=1, padding='same', activation='relu', name='features.6')(x)
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x2 = x = Conv2D(ngf*4, kernel_size=3, strides=1, padding='same', activation='relu', name='features.8')(x)
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x = BlurPool(filt_size=3)(x)
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x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.11')(x)
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x3 = x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.13')(x)
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x = BlurPool(filt_size=3)(x)
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x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.16')(x)
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x4 = x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.18')(x)
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x = BlurPool(filt_size=3)(x)
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x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', name='CA.1')(x)
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x = Conv2DTranspose (ngf*4, 3, strides=2, padding='same', activation='relu', name='CA.2') (x)
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x = Concatenate(axis=3)([ x, x4])
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x = Conv2D (ngf*8, 3, strides=1, padding='same', activation='relu', name='CA.3') (x)
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x = Conv2DTranspose (ngf*4, 3, strides=2, padding='same', activation='relu', name='CA.4') (x)
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x = Concatenate(axis=3)([ x, x3])
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x = Conv2D (ngf*8, 3, strides=1, padding='same', activation='relu', name='CA.5') (x)
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x = Conv2DTranspose (ngf*2, 3, strides=2, padding='same', activation='relu', name='CA.6') (x)
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x = Concatenate(axis=3)([ x, x2])
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x = Conv2D (ngf*4, 3, strides=1, padding='same', activation='relu', name='CA.7') (x)
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x = Conv2DTranspose (ngf, 3, strides=2, padding='same', activation='relu', name='CA.8') (x)
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x = Concatenate(axis=3)([ x, x1])
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x = Conv2D (ngf*2, 3, strides=1, padding='same', activation='relu', name='CA.9') (x)
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x = Conv2DTranspose (ngf // 2, 3, strides=2, padding='same', activation='relu', name='CA.10') (x)
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x = Concatenate(axis=3)([ x, x0])
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x = Conv2D (ngf, 3, strides=1, padding='same', activation='relu', name='CA.11') (x)
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return Conv2D(1, 3, strides=1, padding='same', activation='sigmoid', name='CA.12')(x)
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return func
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"""
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