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fixed mask editor

Browse source 
added FacesetEnhancer
4.2.other) data_src util faceset enhance best GPU.bat
4.2.other) data_src util faceset enhance multi GPU.bat

FacesetEnhancer greatly increases details in your source face set,
same as Gigapixel enhancer, but in fully automatic mode.
In OpenCL build it works on CPU only.

Please consider a donation.
This commit is contained in:
Colombo 2019-12-26 21:27:10 +04:00
parent 3be223a265
commit d46fb5cfd3
6 changed files with 476 additions and 6 deletions
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142
nnlib/nnlib.py
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@ -28,7 +28,8 @@ class nnlib(object):
tf = None
tf_sess = None
tf_sess_config = None
PML = None
PMLK = None
PMLTile= None
@ -105,6 +106,7 @@ PixelShuffler = nnlib.PixelShuffler
SubpixelUpscaler = nnlib.SubpixelUpscaler
SubpixelDownscaler = nnlib.SubpixelDownscaler
Scale = nnlib.Scale
BilinearInterpolation = nnlib.BilinearInterpolation
BlurPool = nnlib.BlurPool
FUNITAdain = nnlib.FUNITAdain
SelfAttention = nnlib.SelfAttention
@ -192,7 +194,8 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
config.gpu_options.force_gpu_compatible = True
config.gpu_options.allow_growth = device_config.allow_growth
nnlib.tf_sess_config = config
nnlib.tf_sess = tf.Session(config=config)
if suppressor is not None:
@ -710,6 +713,141 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
base_config = super(Scale, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
nnlib.Scale = Scale
"""
unable to work in plaidML, due to unimplemented ops
class BilinearInterpolation(KL.Layer):
def __init__(self, size=(2,2), **kwargs):
self.size = size
super(BilinearInterpolation, self).__init__(**kwargs)
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[1]*self.size[1], input_shape[2]*self.size[0], input_shape[3])
def call(self, X):
_,h,w,_ = K.int_shape(X)
X = K.concatenate( [ X, X[:,:,-2:-1,:] ],axis=2 )
X = K.concatenate( [ X, X[:,:,-2:-1,:] ],axis=2 )
X = K.concatenate( [ X, X[:,-2:-1,:,:] ],axis=1 )
X = K.concatenate( [ X, X[:,-2:-1,:,:] ],axis=1 )
X_sh = K.shape(X)
batch_size, height, width, num_channels = X_sh[0], X_sh[1], X_sh[2], X_sh[3]
output_h, output_w = (h*self.size[1]+4, w*self.size[0]+4)
x_linspace = np.linspace(-1. , 1. - 2/output_w, output_w)#
y_linspace = np.linspace(-1. , 1. - 2/output_h, output_h)#
x_coordinates, y_coordinates = np.meshgrid(x_linspace, y_linspace)
x_coordinates = K.flatten(K.constant(x_coordinates, dtype=K.floatx() ))
y_coordinates = K.flatten(K.constant(y_coordinates, dtype=K.floatx() ))
grid = K.concatenate([x_coordinates, y_coordinates, K.ones_like(x_coordinates)], 0)
grid = K.flatten(grid)
grids = K.tile(grid, ( batch_size, ) )
grids = K.reshape(grids, (batch_size, 3, output_h * output_w ))
x = K.cast(K.flatten(grids[:, 0:1, :]), dtype='float32')
y = K.cast(K.flatten(grids[:, 1:2, :]), dtype='float32')
x = .5 * (x + 1.0) * K.cast(width, dtype='float32')
y = .5 * (y + 1.0) * K.cast(height, dtype='float32')
x0 = K.cast(x, 'int32')
x1 = x0 + 1
y0 = K.cast(y, 'int32')
y1 = y0 + 1
max_x = int(K.int_shape(X)[2] -1)
max_y = int(K.int_shape(X)[1] -1)
x0 = K.clip(x0, 0, max_x)
x1 = K.clip(x1, 0, max_x)
y0 = K.clip(y0, 0, max_y)
y1 = K.clip(y1, 0, max_y)
pixels_batch = K.constant ( np.arange(0, batch_size) * (height * width), dtype=K.floatx() )
pixels_batch = K.expand_dims(pixels_batch, axis=-1)
base = K.tile(pixels_batch, (1, output_h * output_w ) )
base = K.flatten(base)
# base_y0 = base + (y0 * width)
base_y0 = y0 * width
base_y0 = base + base_y0
# base_y1 = base + (y1 * width)
base_y1 = y1 * width
base_y1 = base_y1 + base
indices_a = base_y0 + x0
indices_b = base_y1 + x0
indices_c = base_y0 + x1
indices_d = base_y1 + x1
flat_image = K.reshape(X, (-1, num_channels) )
flat_image = K.cast(flat_image, dtype='float32')
pixel_values_a = K.gather(flat_image, indices_a)
pixel_values_b = K.gather(flat_image, indices_b)
pixel_values_c = K.gather(flat_image, indices_c)
pixel_values_d = K.gather(flat_image, indices_d)
x0 = K.cast(x0, 'float32')
x1 = K.cast(x1, 'float32')
y0 = K.cast(y0, 'float32')
y1 = K.cast(y1, 'float32')
area_a = K.expand_dims(((x1 - x) * (y1 - y)), 1)
area_b = K.expand_dims(((x1 - x) * (y - y0)), 1)
area_c = K.expand_dims(((x - x0) * (y1 - y)), 1)
area_d = K.expand_dims(((x - x0) * (y - y0)), 1)
values_a = area_a * pixel_values_a
values_b = area_b * pixel_values_b
values_c = area_c * pixel_values_c
values_d = area_d * pixel_values_d
interpolated_image = values_a + values_b + values_c + values_d
new_shape = (batch_size, output_h, output_w, num_channels)
interpolated_image = K.reshape(interpolated_image, new_shape)
interpolated_image = interpolated_image[:,:-4,:-4,:]
return interpolated_image
def get_config(self):
config = {"size": self.size}
base_config = super(BilinearInterpolation, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
"""
class BilinearInterpolation(KL.Layer):
def __init__(self, size=(2,2), **kwargs):
self.size = size
super(BilinearInterpolation, self).__init__(**kwargs)
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[1]*self.size[1], input_shape[2]*self.size[0], input_shape[3])
def call(self, X):
_,h,w,_ = K.int_shape(X)
return K.cast( K.tf.image.resize_images(X, (h*self.size[1],w*self.size[0]) ), K.floatx() )
def get_config(self):
config = {"size": self.size}
base_config = super(BilinearInterpolation, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
nnlib.BilinearInterpolation = BilinearInterpolation
class SelfAttention(KL.Layer):
def __init__(self, nc, squeeze_factor=8, **kwargs):