mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-11 15:47:01 -07:00
global refactoring and fixes,
removed support of extracted(aligned) PNG faces. Use old builds to convert from PNG to JPG. fanseg model file in facelib/ is renamed
This commit is contained in:
Colombo
parent
921b464d5b
commit
61472cdaf7
82 changed files with 3838 additions and 3812 deletions
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@ -9,106 +9,13 @@ import numpy as np
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from core.interact import interact as io
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from core.leras import nn
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"""
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Dataset used to train located in official DFL mega.nz folder
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https://mega.nz/#F!b9MzCK4B!zEAG9txu7uaRUjXz9PtBqg
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using https://github.com/ternaus/TernausNet
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TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
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"""
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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=None, load_weights=True, weights_file_root=None, training=False, place_model_on_cpu=False, data_format="NHWC"):
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def __init__ (self, name, resolution, load_weights=True, weights_file_root=None, training=False, place_model_on_cpu=False, run_on_cpu=False, optimizer=None, data_format="NHWC"):
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nn.initialize(data_format=data_format)
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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, base_ch):
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self.features_0 = nn.Conv2D (in_ch, base_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 (base_ch, base_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 (base_ch*2, base_ch*4, kernel_size=3, padding='SAME')
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self.features_8 = nn.Conv2D (base_ch*4, base_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 (base_ch*4, base_ch*8, kernel_size=3, padding='SAME')
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self.features_13 = nn.Conv2D (base_ch*8, base_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 (base_ch*8, base_ch*8, kernel_size=3, padding='SAME')
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self.features_18 = nn.Conv2D (base_ch*8, base_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 (base_ch*8, base_ch*8, kernel_size=3, padding='SAME')
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self.conv1_up = nn.Conv2DTranspose (base_ch*8, base_ch*4, kernel_size=3, padding='SAME')
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self.conv1 = nn.Conv2D (base_ch*12, base_ch*8, kernel_size=3, padding='SAME')
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self.conv2_up = nn.Conv2DTranspose (base_ch*8, base_ch*4, kernel_size=3, padding='SAME')
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self.conv2 = nn.Conv2D (base_ch*12, base_ch*8, kernel_size=3, padding='SAME')
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self.conv3_up = nn.Conv2DTranspose (base_ch*8, base_ch*2, kernel_size=3, padding='SAME')
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self.conv3 = nn.Conv2D (base_ch*6, base_ch*4, kernel_size=3, padding='SAME')
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self.conv4_up = nn.Conv2DTranspose (base_ch*4, base_ch, kernel_size=3, padding='SAME')
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self.conv4 = nn.Conv2D (base_ch*3, base_ch*2, kernel_size=3, padding='SAME')
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self.conv5_up = nn.Conv2DTranspose (base_ch*2, base_ch//2, kernel_size=3, padding='SAME')
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self.conv5 = nn.Conv2D (base_ch//2+base_ch, base_ch, kernel_size=3, padding='SAME')
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self.out_conv = nn.Conv2D (base_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 = 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 = 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.concat( [x,x4], nn.conv2d_ch_axis)
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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.concat( [x,x3], nn.conv2d_ch_axis)
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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.concat( [x,x2], nn.conv2d_ch_axis)
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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.concat( [x,x1], nn.conv2d_ch_axis)
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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.concat( [x,x0], nn.conv2d_ch_axis)
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x = tf.nn.relu(self.conv5(x))
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logits = self.out_conv(x)
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return logits, tf.nn.sigmoid(logits)
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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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@ -117,39 +24,42 @@ class TernausNet(object):
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with tf.device ('/CPU:0'):
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#Place holders on CPU
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self.input_t = tf.placeholder (nn.tf_floatx, nn.get4Dshape(resolution,resolution,3) )
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self.target_t = tf.placeholder (nn.tf_floatx, nn.get4Dshape(resolution,resolution,1) )
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self.input_t = tf.placeholder (nn.floatx, nn.get4Dshape(resolution,resolution,3) )
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self.target_t = tf.placeholder (nn.floatx, nn.get4Dshape(resolution,resolution,1) )
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# Initializing model classes
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with tf.device ('/CPU:0' if place_model_on_cpu else '/GPU:0'):
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self.net = Ternaus(3, 64, name='Ternaus')
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self.net = nn.Ternaus(3, 64, name='Ternaus')
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self.net_weights = self.net.get_weights()
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model_name = f'{name}_{resolution}'
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if face_type_str is not None:
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model_name += f'_{face_type_str}'
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model_name = f'{name}_{resolution}'
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self.model_filename_list = [ [self.net, f'{model_name}.npy'] ]
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if training:
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self.opt = nn.TFRMSpropOptimizer(lr=0.0001, name='opt')
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if optimizer is None:
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raise ValueError("Optimizer should be provided for traning mode.")
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self.opt = optimizer
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self.opt.initialize_variables (self.net_weights, vars_on_cpu=place_model_on_cpu)
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self.model_filename_list += [ [self.opt, f'{model_name}_opt.npy' ] ]
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else:
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_, pred = self.net([self.input_t])
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with tf.device ('/CPU:0' if run_on_cpu else '/GPU:0'):
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_, pred = self.net([self.input_t])
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def net_run(input_np):
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return nn.tf_sess.run ( [pred], feed_dict={self.input_t :input_np})[0]
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self.net_run = net_run
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# Loading/initializing all models/optimizers weights
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for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
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for model, filename in self.model_filename_list:
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do_init = not load_weights
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if not do_init:
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do_init = not model.load_weights( self.weights_file_root / filename )
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if do_init:
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model.init_weights()
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model.init_weights()
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if model == self.net:
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try:
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with open( Path(__file__).parent / 'vgg11_enc_weights.npy', 'rb' ) as f:
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@ -177,7 +87,7 @@ class TernausNet(object):
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return result
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"""
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"""
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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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