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update == 04.20.2019 == (#242)

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* superb improved fanseg

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* added FANseg extractor for src and dst faces to use it in training

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* update to 'partial' func

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* trained FANSeg_256_full_face.h5,
new experimental models: AVATAR, RecycleGAN

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* fix for TCC mode cards(tesla), was conflict with plaidML initialization.

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* update manuals

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iperov 2019-04-20 08:23:08 +04:00 • committed by GitHub
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facelib/FANSeg_256_full_face.h5
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facelib/FANSegmentator.py
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@ -1,15 +1,30 @@
import numpy as np
import os
import cv2
import pickle
from functools import partial
from pathlib import Path
from nnlib import nnlib
import cv2
import numpy as np
from interact import interact as io
from nnlib import nnlib
"""
FANSegmentator is designed to segment faces aligned by 2DFAN-4 landmarks extractor.
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 FANSegmentator(object):
VERSION = 1
def __init__ (self, resolution, face_type_str, load_weights=True, weights_file_root=None, training=False):
exec( nnlib.import_all(), locals(), globals() )
self.model = FANSegmentator.BuildModel(resolution, ngf=32)
self.model = FANSegmentator.BuildModel(resolution, ngf=64)
if weights_file_root:
weights_file_root = Path(weights_file_root)
@ -22,14 +37,21 @@ class FANSegmentator(object):
self.model.load_weights (str(self.weights_path))
else:
if training:
conv_weights_list = []
for layer in self.model.layers:
if type(layer) == keras.layers.Conv2D:
conv_weights_list += [layer.weights[0]] # Conv2D kernel_weights
CAInitializerMP(conv_weights_list)
if training:
self.model.compile(loss='mse', optimizer=Adam(tf_cpu_mode=2))
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")
if training:
#self.model.compile(loss='mse', optimizer=Adam(tf_cpu_mode=2))
self.model.compile(loss='binary_crossentropy', optimizer=Adam(tf_cpu_mode=2), metrics=['accuracy'])
def __enter__(self):
return self
@ -42,66 +64,76 @@ class FANSegmentator(object):
def train_on_batch(self, inp, outp):
return self.model.train_on_batch(inp, outp)
def extract_from_bgr (self, input_image):
return np.clip ( (self.model.predict(input_image) + 1) / 2.0, 0, 1.0 )
def extract (self, input_image, is_input_tanh=False):
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):
def BuildModel ( resolution, ngf=64, norm='', act='lrelu'):
exec( nnlib.import_all(), locals(), globals() )
inp = Input ( (resolution,resolution,3) )
x = inp
x = FANSegmentator.EncFlow(ngf=ngf)(x)
x = FANSegmentator.DecFlow(ngf=ngf)(x)
x = FANSegmentator.Flow(ngf=ngf, norm=norm, act=act)(x)
model = Model(inp,x)
return model
@staticmethod
def EncFlow(ngf=64, num_downs=4):
def Flow(ngf=64, num_downs=4, norm='', act='lrelu'):
exec( nnlib.import_all(), locals(), globals() )
use_bias = True
def XNormalization(x):
return InstanceNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x)
def downscale (dim):
def func(x):
return LeakyReLU(0.1)(XNormalization(Conv2D(dim, kernel_size=5, strides=2, padding='same', kernel_initializer=RandomNormal(0, 0.02))(x)))
return func
def func(input):
x = input
result = []
for i in range(num_downs):
x = downscale ( min(ngf*(2**i), ngf*8) )(x)
result += [x]
x0 = x = Conv2D(ngf, kernel_size=3, strides=1, padding='same', activation='relu', name='features.0')(x)
x = MaxPooling2D()(x)
x1 = x = Conv2D(ngf*2, kernel_size=3, strides=1, padding='same', activation='relu', name='features.3')(x)
x = MaxPooling2D()(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 = MaxPooling2D()(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 = MaxPooling2D()(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 = MaxPooling2D()(x)
x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same')(x)
x = Conv2DTranspose (ngf*4, 3, strides=2, padding='same', activation='relu') (x)
x = Concatenate(axis=3)([ x, x4])
x = Conv2D (ngf*8, 3, strides=1, padding='same', activation='relu') (x)
x = Conv2DTranspose (ngf*4, 3, strides=2, padding='same', activation='relu') (x)
x = Concatenate(axis=3)([ x, x3])
x = Conv2D (ngf*8, 3, strides=1, padding='same', activation='relu') (x)
x = Conv2DTranspose (ngf*2, 3, strides=2, padding='same', activation='relu') (x)
x = Concatenate(axis=3)([ x, x2])
x = Conv2D (ngf*4, 3, strides=1, padding='same', activation='relu') (x)
x = Conv2DTranspose (ngf, 3, strides=2, padding='same', activation='relu') (x)
x = Concatenate(axis=3)([ x, x1])
x = Conv2D (ngf*2, 3, strides=1, padding='same', activation='relu') (x)
x = Conv2DTranspose (ngf // 2, 3, strides=2, padding='same', activation='relu') (x)
x = Concatenate(axis=3)([ x, x0])
x = Conv2D (ngf, 3, strides=1, padding='same', activation='relu') (x)
return Conv2D(1, 3, strides=1, padding='same', activation='sigmoid')(x)
return result
return func
@staticmethod
def DecFlow(output_nc=1, ngf=64, activation='tanh'):
exec (nnlib.import_all(), locals(), globals())
use_bias = True
def XNormalization(x):
return InstanceNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x)
def Conv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=use_bias, kernel_initializer=RandomNormal(0, 0.02), bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None):
return keras.layers.Conv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint )
def upscale (dim):
def func(x):
return SubpixelUpscaler()( LeakyReLU(0.1)(XNormalization(Conv2D(dim, kernel_size=3, strides=1, padding='same', kernel_initializer=RandomNormal(0, 0.02))(x))))
return func
def func(input):
input_len = len(input)
x = input[input_len-1]
for i in range(input_len-1, -1, -1):
x = upscale( min(ngf* (2**i) *4, ngf*8 *4 ) )(x)
if i != 0:
x = Concatenate(axis=3)([ input[i-1] , x])
return Conv2D(output_nc, 3, 1, 'same', activation=activation)(x)
return func

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