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Converter: added new mask modes: FAN-prd, FAN-dst

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iperov 2019-03-21 21:19:03 +04:00
parent 9849bcc1e5
commit c4f41a7e76
5 changed files with 11426 additions and 11214 deletions
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49
facelib/FANSegmentator.py
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@ -8,16 +8,16 @@ from interact import interact as io
class FANSegmentator(object):
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)
if weights_file_root:
weights_file_root = Path(weights_file_root)
else:
weights_file_root = Path(__file__).parent
self.weights_path = weights_file_root / ('FANSeg_%d_%s.h5' % (resolution, face_type_str) )
if load_weights:
self.model.load_weights (str(self.weights_path))
else:
@ -33,19 +33,19 @@ class FANSegmentator(object):
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.model.save_weights (str(self.weights_path))
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 )
@staticmethod
def BuildModel ( resolution, ngf=64):
exec( nnlib.import_all(), locals(), globals() )
@ -55,7 +55,7 @@ class FANSegmentator(object):
x = FANSegmentator.DecFlow(ngf=ngf)(x)
model = Model(inp,x)
return model
@staticmethod
def EncFlow(ngf=64, num_downs=4):
exec( nnlib.import_all(), locals(), globals() )
@ -67,19 +67,19 @@ class FANSegmentator(object):
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):
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]
x = downscale ( min(ngf*(2**i), ngf*8) )(x)
result += [x]
return result
return func
@staticmethod
def DecFlow(output_nc=1, ngf=64, activation='tanh'):
exec (nnlib.import_all(), locals(), globals())
@ -87,23 +87,22 @@ class FANSegmentator(object):
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
return func
def func(input):
input_len = len(input)
x = input[input_len-1]
for i in range(input_len-1, -1, -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