mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-13 16:43:51 -07:00
DFL-2.0 initial branch commit
This commit is contained in:
Colombo
parent
52a67a61b3
commit
38b85108b3
154 changed files with 5251 additions and 9414 deletions
318
facelib/TernausNet.py
Normal file
318
facelib/TernausNet.py
Normal file
|
|
@ -0,0 +1,318 @@
|
|||
import os
|
||||
import pickle
|
||||
from functools import partial
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from core.interact import interact as io
|
||||
from core.leras import nn
|
||||
|
||||
"""
|
||||
Dataset used to train located in official DFL mega.nz folder
|
||||
https://mega.nz/#F!b9MzCK4B!zEAG9txu7uaRUjXz9PtBqg
|
||||
|
||||
using https://github.com/ternaus/TernausNet
|
||||
TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
|
||||
"""
|
||||
|
||||
class TernausNet(object):
|
||||
VERSION = 1
|
||||
def __init__ (self, name, resolution, face_type_str, load_weights=True, weights_file_root=None, training=False, place_model_on_cpu=False):
|
||||
nn.initialize()
|
||||
tf = nn.tf
|
||||
|
||||
class Ternaus(nn.ModelBase):
|
||||
def on_build(self, in_ch, ch):
|
||||
|
||||
self.features_0 = nn.Conv2D (in_ch, ch, kernel_size=3, padding='SAME')
|
||||
self.blurpool_0 = nn.BlurPool (filt_size=3)
|
||||
|
||||
self.features_3 = nn.Conv2D (ch, ch*2, kernel_size=3, padding='SAME')
|
||||
self.blurpool_3 = nn.BlurPool (filt_size=3)
|
||||
|
||||
self.features_6 = nn.Conv2D (ch*2, ch*4, kernel_size=3, padding='SAME')
|
||||
self.features_8 = nn.Conv2D (ch*4, ch*4, kernel_size=3, padding='SAME')
|
||||
self.blurpool_8 = nn.BlurPool (filt_size=3)
|
||||
|
||||
self.features_11 = nn.Conv2D (ch*4, ch*8, kernel_size=3, padding='SAME')
|
||||
self.features_13 = nn.Conv2D (ch*8, ch*8, kernel_size=3, padding='SAME')
|
||||
self.blurpool_13 = nn.BlurPool (filt_size=3)
|
||||
|
||||
self.features_16 = nn.Conv2D (ch*8, ch*8, kernel_size=3, padding='SAME')
|
||||
self.features_18 = nn.Conv2D (ch*8, ch*8, kernel_size=3, padding='SAME')
|
||||
self.blurpool_18 = nn.BlurPool (filt_size=3)
|
||||
|
||||
self.conv_center = nn.Conv2D (ch*8, ch*8, kernel_size=3, padding='SAME')
|
||||
|
||||
self.conv1_up = nn.Conv2DTranspose (ch*8, ch*4, kernel_size=3, padding='SAME')
|
||||
self.conv1 = nn.Conv2D (ch*12, ch*8, kernel_size=3, padding='SAME')
|
||||
|
||||
self.conv2_up = nn.Conv2DTranspose (ch*8, ch*4, kernel_size=3, padding='SAME')
|
||||
self.conv2 = nn.Conv2D (ch*12, ch*8, kernel_size=3, padding='SAME')
|
||||
|
||||
self.conv3_up = nn.Conv2DTranspose (ch*8, ch*2, kernel_size=3, padding='SAME')
|
||||
self.conv3 = nn.Conv2D (ch*6, ch*4, kernel_size=3, padding='SAME')
|
||||
|
||||
self.conv4_up = nn.Conv2DTranspose (ch*4, ch, kernel_size=3, padding='SAME')
|
||||
self.conv4 = nn.Conv2D (ch*3, ch*2, kernel_size=3, padding='SAME')
|
||||
|
||||
self.conv5_up = nn.Conv2DTranspose (ch*2, ch//2, kernel_size=3, padding='SAME')
|
||||
self.conv5 = nn.Conv2D (ch//2+ch, ch, kernel_size=3, padding='SAME')
|
||||
|
||||
self.out_conv = nn.Conv2D (ch, 1, kernel_size=3, padding='SAME')
|
||||
|
||||
def forward(self, inp):
|
||||
x, = inp
|
||||
|
||||
x = x0 = tf.nn.relu(self.features_0(x))
|
||||
x = self.blurpool_0(x)
|
||||
|
||||
x = x1 = tf.nn.relu(self.features_3(x))
|
||||
x = self.blurpool_3(x)
|
||||
|
||||
x = tf.nn.relu(self.features_6(x))
|
||||
x = x2 = tf.nn.relu(self.features_8(x))
|
||||
x = self.blurpool_8(x)
|
||||
|
||||
x = tf.nn.relu(self.features_11(x))
|
||||
x = x3 = tf.nn.relu(self.features_13(x))
|
||||
x = self.blurpool_13(x)
|
||||
|
||||
x = tf.nn.relu(self.features_16(x))
|
||||
x = x4 = tf.nn.relu(self.features_18(x))
|
||||
x = self.blurpool_18(x)
|
||||
|
||||
x = self.conv_center(x)
|
||||
|
||||
x = tf.nn.relu(self.conv1_up(x))
|
||||
x = tf.concat( [x,x4], -1)
|
||||
x = tf.nn.relu(self.conv1(x))
|
||||
|
||||
x = tf.nn.relu(self.conv2_up(x))
|
||||
x = tf.concat( [x,x3], -1)
|
||||
x = tf.nn.relu(self.conv2(x))
|
||||
|
||||
x = tf.nn.relu(self.conv3_up(x))
|
||||
x = tf.concat( [x,x2], -1)
|
||||
x = tf.nn.relu(self.conv3(x))
|
||||
|
||||
x = tf.nn.relu(self.conv4_up(x))
|
||||
x = tf.concat( [x,x1], -1)
|
||||
x = tf.nn.relu(self.conv4(x))
|
||||
|
||||
x = tf.nn.relu(self.conv5_up(x))
|
||||
x = tf.concat( [x,x0], -1)
|
||||
x = tf.nn.relu(self.conv5(x))
|
||||
|
||||
x = tf.nn.sigmoid(self.out_conv(x))
|
||||
return x
|
||||
|
||||
if weights_file_root is not None:
|
||||
weights_file_root = Path(weights_file_root)
|
||||
else:
|
||||
weights_file_root = Path(__file__).parent
|
||||
self.weights_path = weights_file_root / ('%s_%d_%s.npy' % (name, resolution, face_type_str) )
|
||||
|
||||
e = tf.device('/CPU:0') if place_model_on_cpu else None
|
||||
|
||||
if e is not None: e.__enter__()
|
||||
self.net = Ternaus(3, 64, name='Ternaus')
|
||||
if load_weights:
|
||||
self.net.load_weights (self.weights_path)
|
||||
else:
|
||||
self.net.init_weights()
|
||||
if e is not None: e.__exit__(None,None,None)
|
||||
|
||||
self.net.build_for_run ( [(tf.float32, (resolution,resolution,3))] )
|
||||
|
||||
if training:
|
||||
raise Exception("training not supported yet")
|
||||
|
||||
|
||||
"""
|
||||
if training:
|
||||
try:
|
||||
with open( Path(__file__).parent / 'vgg11_enc_weights.npy', 'rb' ) as f:
|
||||
d = pickle.loads (f.read())
|
||||
|
||||
for i in [0,3,6,8,11,13,16,18]:
|
||||
s = 'features.%d' % i
|
||||
|
||||
self.model.get_layer (s).set_weights ( d[s] )
|
||||
except:
|
||||
io.log_err("Unable to load VGG11 pretrained weights from vgg11_enc_weights.npy")
|
||||
|
||||
conv_weights_list = []
|
||||
for layer in self.model.layers:
|
||||
if 'CA.' in layer.name:
|
||||
conv_weights_list += [layer.weights[0]] #Conv2D kernel_weights
|
||||
CAInitializerMP ( conv_weights_list )
|
||||
"""
|
||||
|
||||
|
||||
"""
|
||||
if training:
|
||||
inp_t = Input ( (resolution, resolution, 3) )
|
||||
real_t = Input ( (resolution, resolution, 1) )
|
||||
out_t = self.model(inp_t)
|
||||
|
||||
loss = K.mean(10*K.binary_crossentropy(real_t,out_t) )
|
||||
|
||||
out_t_diff1 = out_t[:, 1:, :, :] - out_t[:, :-1, :, :]
|
||||
out_t_diff2 = out_t[:, :, 1:, :] - out_t[:, :, :-1, :]
|
||||
|
||||
total_var_loss = K.mean( 0.1*K.abs(out_t_diff1), axis=[1, 2, 3] ) + K.mean( 0.1*K.abs(out_t_diff2), axis=[1, 2, 3] )
|
||||
|
||||
opt = Adam(lr=0.0001, beta_1=0.5, beta_2=0.999, tf_cpu_mode=2)
|
||||
|
||||
self.train_func = K.function ( [inp_t, real_t], [K.mean(loss)], opt.get_updates( [loss], self.model.trainable_weights) )
|
||||
"""
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, exc_type=None, exc_value=None, traceback=None):
|
||||
return False #pass exception between __enter__ and __exit__ to outter level
|
||||
|
||||
def save_weights(self):
|
||||
self.net.save_weights (str(self.weights_path))
|
||||
|
||||
def train(self, inp, real):
|
||||
loss, = self.train_func ([inp, real])
|
||||
return loss
|
||||
|
||||
def extract (self, input_image):
|
||||
input_shape_len = len(input_image.shape)
|
||||
if input_shape_len == 3:
|
||||
input_image = input_image[np.newaxis,...]
|
||||
|
||||
result = np.clip ( self.net.run([input_image]), 0, 1.0 )
|
||||
result[result < 0.1] = 0 #get rid of noise
|
||||
|
||||
if input_shape_len == 3:
|
||||
result = result[0]
|
||||
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
"""
|
||||
self.weights_path = weights_file_root / ('%s_%d_%s.h5' % (name, resolution, face_type_str) )
|
||||
|
||||
|
||||
self.net.build()
|
||||
|
||||
|
||||
self.net.features_0.set_weights ( self.model.get_layer('features.0').get_weights() )
|
||||
self.net.features_3.set_weights ( self.model.get_layer('features.3').get_weights() )
|
||||
self.net.features_6.set_weights ( self.model.get_layer('features.6').get_weights() )
|
||||
self.net.features_8.set_weights ( self.model.get_layer('features.8').get_weights() )
|
||||
self.net.features_11.set_weights ( self.model.get_layer('features.11').get_weights() )
|
||||
self.net.features_13.set_weights ( self.model.get_layer('features.13').get_weights() )
|
||||
self.net.features_16.set_weights ( self.model.get_layer('features.16').get_weights() )
|
||||
self.net.features_18.set_weights ( self.model.get_layer('features.18').get_weights() )
|
||||
|
||||
self.net.conv_center.set_weights ( self.model.get_layer('CA.1').get_weights() )
|
||||
|
||||
self.net.conv1_up.set_weights ( self.model.get_layer('CA.2').get_weights() )
|
||||
self.net.conv1.set_weights ( self.model.get_layer('CA.3').get_weights() )
|
||||
|
||||
self.net.conv2_up.set_weights ( self.model.get_layer('CA.4').get_weights() )
|
||||
self.net.conv2.set_weights ( self.model.get_layer('CA.5').get_weights() )
|
||||
|
||||
self.net.conv3_up.set_weights ( self.model.get_layer('CA.6').get_weights() )
|
||||
self.net.conv3.set_weights ( self.model.get_layer('CA.7').get_weights() )
|
||||
|
||||
self.net.conv4_up.set_weights ( self.model.get_layer('CA.8').get_weights() )
|
||||
self.net.conv4.set_weights ( self.model.get_layer('CA.9').get_weights() )
|
||||
|
||||
self.net.conv5_up.set_weights ( self.model.get_layer('CA.10').get_weights() )
|
||||
self.net.conv5.set_weights ( self.model.get_layer('CA.11').get_weights() )
|
||||
|
||||
self.net.out_conv.set_weights ( self.model.get_layer('CA.12').get_weights() )
|
||||
|
||||
self.net.build_for_run ( [ (tf.float32, (resolution,resolution,3)) ])
|
||||
self.net.save_weights (self.weights_path2)
|
||||
|
||||
|
||||
def extract (self, input_image):
|
||||
input_shape_len = len(input_image.shape)
|
||||
if input_shape_len == 3:
|
||||
input_image = input_image[np.newaxis,...]
|
||||
|
||||
result = np.clip ( self.model.predict( [input_image] ), 0, 1.0 )
|
||||
result[result < 0.1] = 0 #get rid of noise
|
||||
|
||||
if input_shape_len == 3:
|
||||
result = result[0]
|
||||
|
||||
return result
|
||||
|
||||
|
||||
@staticmethod
|
||||
def BuildModel ( resolution, ngf=64):
|
||||
exec( nn.initialize(), locals(), globals() )
|
||||
inp = Input ( (resolution,resolution,3) )
|
||||
x = inp
|
||||
x = TernausNet.Flow(ngf=ngf)(x)
|
||||
model = Model(inp,x)
|
||||
return model
|
||||
|
||||
@staticmethod
|
||||
def Flow(ngf=64):
|
||||
exec( nn.initialize(), locals(), globals() )
|
||||
|
||||
def func(input):
|
||||
x = input
|
||||
|
||||
x0 = x = Conv2D(ngf, kernel_size=3, strides=1, padding='same', activation='relu', name='features.0')(x)
|
||||
x = BlurPool(filt_size=3)(x)
|
||||
|
||||
x1 = x = Conv2D(ngf*2, kernel_size=3, strides=1, padding='same', activation='relu', name='features.3')(x)
|
||||
x = BlurPool(filt_size=3)(x)
|
||||
|
||||
x = Conv2D(ngf*4, kernel_size=3, strides=1, padding='same', activation='relu', name='features.6')(x)
|
||||
x2 = x = Conv2D(ngf*4, kernel_size=3, strides=1, padding='same', activation='relu', name='features.8')(x)
|
||||
x = BlurPool(filt_size=3)(x)
|
||||
|
||||
x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.11')(x)
|
||||
x3 = x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.13')(x)
|
||||
x = BlurPool(filt_size=3)(x)
|
||||
|
||||
x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.16')(x)
|
||||
x4 = x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.18')(x)
|
||||
x = BlurPool(filt_size=3)(x)
|
||||
|
||||
x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', name='CA.1')(x)
|
||||
|
||||
x = Conv2DTranspose (ngf*4, 3, strides=2, padding='same', activation='relu', name='CA.2') (x)
|
||||
x = Concatenate(axis=3)([ x, x4])
|
||||
x = Conv2D (ngf*8, 3, strides=1, padding='same', activation='relu', name='CA.3') (x)
|
||||
|
||||
x = Conv2DTranspose (ngf*4, 3, strides=2, padding='same', activation='relu', name='CA.4') (x)
|
||||
x = Concatenate(axis=3)([ x, x3])
|
||||
x = Conv2D (ngf*8, 3, strides=1, padding='same', activation='relu', name='CA.5') (x)
|
||||
|
||||
x = Conv2DTranspose (ngf*2, 3, strides=2, padding='same', activation='relu', name='CA.6') (x)
|
||||
x = Concatenate(axis=3)([ x, x2])
|
||||
x = Conv2D (ngf*4, 3, strides=1, padding='same', activation='relu', name='CA.7') (x)
|
||||
|
||||
x = Conv2DTranspose (ngf, 3, strides=2, padding='same', activation='relu', name='CA.8') (x)
|
||||
x = Concatenate(axis=3)([ x, x1])
|
||||
x = Conv2D (ngf*2, 3, strides=1, padding='same', activation='relu', name='CA.9') (x)
|
||||
|
||||
x = Conv2DTranspose (ngf // 2, 3, strides=2, padding='same', activation='relu', name='CA.10') (x)
|
||||
x = Concatenate(axis=3)([ x, x0])
|
||||
x = Conv2D (ngf, 3, strides=1, padding='same', activation='relu', name='CA.11') (x)
|
||||
|
||||
return Conv2D(1, 3, strides=1, padding='same', activation='sigmoid', name='CA.12')(x)
|
||||
|
||||
|
||||
return func
|
||||
"""
|
||||
Loading…
Add table
Add a link
Reference in a new issue