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fixed error "Failed to get convolution algorithm" on some systems

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fixed error "dll load failed" on some systems
Expanded eyebrows line of face masks. It does not affect mask of FAN-x converter mode.
This commit is contained in:
iperov 2019-08-11 11:17:22 +04:00
parent 582c974851
commit b72d5a3f9a
15 changed files with 367 additions and 222 deletions
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84
nnlib/nnlib.py
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@ -89,6 +89,8 @@ dssim = nnlib.dssim
PixelShuffler = nnlib.PixelShuffler
SubpixelUpscaler = nnlib.SubpixelUpscaler
Scale = nnlib.Scale
BlurPool = nnlib.BlurPool
SelfAttention = nnlib.SelfAttention
CAInitializerMP = nnlib.CAInitializerMP
@ -455,6 +457,51 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
nnlib.PixelShuffler = PixelShuffler
nnlib.SubpixelUpscaler = PixelShuffler
class BlurPool(KL.Layer):
"""
https://arxiv.org/abs/1904.11486 https://github.com/adobe/antialiased-cnns
"""
def __init__(self, filt_size=3, stride=2, **kwargs):
self.strides = (stride,stride)
self.filt_size = filt_size
self.padding = ( (int(1.*(filt_size-1)/2), int(np.ceil(1.*(filt_size-1)/2)) ), (int(1.*(filt_size-1)/2), int(np.ceil(1.*(filt_size-1)/2)) ) )
if(self.filt_size==1):
self.a = np.array([1.,])
elif(self.filt_size==2):
self.a = np.array([1., 1.])
elif(self.filt_size==3):
self.a = np.array([1., 2., 1.])
elif(self.filt_size==4):
self.a = np.array([1., 3., 3., 1.])
elif(self.filt_size==5):
self.a = np.array([1., 4., 6., 4., 1.])
elif(self.filt_size==6):
self.a = np.array([1., 5., 10., 10., 5., 1.])
elif(self.filt_size==7):
self.a = np.array([1., 6., 15., 20., 15., 6., 1.])
super(BlurPool, self).__init__(**kwargs)
def compute_output_shape(self, input_shape):
height = input_shape[1] // self.strides[0]
width = input_shape[2] // self.strides[1]
channels = input_shape[3]
return (input_shape[0], height, width, channels)
def call(self, x):
k = self.a
k = k[:,None]*k[None,:]
k = k / np.sum(k)
k = np.tile (k[:,:,None,None], (1,1,K.int_shape(x)[-1],1) )
k = K.constant (k, dtype=K.floatx() )
x = K.spatial_2d_padding(x, padding=self.padding)
x = K.depthwise_conv2d(x, k, strides=self.strides, padding='valid')
return x
nnlib.BlurPool = BlurPool
class Scale(KL.Layer):
"""
@ -487,6 +534,43 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
return dict(list(base_config.items()) + list(config.items()))
nnlib.Scale = Scale
class SelfAttention(KL.Layer):
def __init__(self, nc, squeeze_factor=8, **kwargs):
assert nc//squeeze_factor > 0, f"Input channels must be >= {squeeze_factor}, recieved nc={nc}"
self.nc = nc
self.squeeze_factor = squeeze_factor
super(SelfAttention, self).__init__(**kwargs)
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[1], input_shape[2], self.nc)
def call(self, inp):
x = inp
shape_x = x.get_shape().as_list()
f = Conv2D(self.nc//self.squeeze_factor, 1, kernel_regularizer=keras.regularizers.l2(1e-4))(x)
g = Conv2D(self.nc//self.squeeze_factor, 1, kernel_regularizer=keras.regularizers.l2(1e-4))(x)
h = Conv2D(self.nc, 1, kernel_regularizer=keras.regularizers.l2(1e-4))(x)
shape_f = f.get_shape().as_list()
shape_g = g.get_shape().as_list()
shape_h = h.get_shape().as_list()
flat_f = Reshape( (-1, shape_f[-1]) )(f)
flat_g = Reshape( (-1, shape_g[-1]) )(g)
flat_h = Reshape( (-1, shape_h[-1]) )(h)
s = Lambda(lambda x: K.batch_dot(x[0], keras.layers.Permute((2,1))(x[1]) ))([flat_g, flat_f])
beta = keras.layers.Softmax(axis=-1)(s)
o = Lambda(lambda x: K.batch_dot(x[0], x[1]))([beta, flat_h])
o = Reshape(shape_x[1:])(o)
o = Scale()(o)
out = Add()([o, inp])
return out
nnlib.SelfAttention = SelfAttention
class Adam(keras.optimizers.Optimizer):
"""Adam optimizer.