mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-03-12 20:42:45 -07:00
158 lines
6.2 KiB
Python
158 lines
6.2 KiB
Python
import os
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import pickle
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from functools import partial
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from pathlib import Path
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import cv2
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import numpy as np
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from interact import interact as io
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from nnlib import nnlib
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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, load_weights=True, weights_file_root=None, training=False):
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exec( nnlib.import_all(), locals(), globals() )
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self.model = TernausNet.BuildModel(resolution, ngf=64)
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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.h5' % (name, resolution, face_type_str) )
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if load_weights:
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self.model.load_weights (str(self.weights_path))
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else:
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if training:
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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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d = pickle.loads (f.read())
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for i in [0,3,6,8,11,13,16,18]:
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s = 'features.%d' % i
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self.model.get_layer (s).set_weights ( d[s] )
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except:
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io.log_err("Unable to load VGG11 pretrained weights from vgg11_enc_weights.npy")
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conv_weights_list = []
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for layer in self.model.layers:
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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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if training:
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inp_t = Input ( (resolution, resolution, 3) )
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real_t = Input ( (resolution, resolution, 1) )
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out_t = self.model(inp_t)
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loss = K.mean(10*K.binary_crossentropy(real_t,out_t) )
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out_t_diff1 = out_t[:, 1:, :, :] - out_t[:, :-1, :, :]
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out_t_diff2 = out_t[:, :, 1:, :] - out_t[:, :, :-1, :]
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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] )
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opt = Adam(lr=0.0001, beta_1=0.5, beta_2=0.999, tf_cpu_mode=2)
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self.train_func = K.function ( [inp_t, real_t], [K.mean(loss)], opt.get_updates( [loss], self.model.trainable_weights) )
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def __enter__(self):
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return self
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def __exit__(self, exc_type=None, exc_value=None, traceback=None):
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return False #pass exception between __enter__ and __exit__ to outter level
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def save_weights(self):
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self.model.save_weights (str(self.weights_path))
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def train(self, inp, real):
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loss, = self.train_func ([inp, real])
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return loss
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def extract (self, input_image, is_input_tanh=False):
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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( nnlib.import_all(), 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( nnlib.import_all(), 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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