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SAE: dssim kernel size now depends on resolution

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iperov 2019-03-12 09:49:40 +04:00
parent fd3b9add2f
commit 46ff33bf89
3 changed files with 12 additions and 8 deletions
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4
nnlib/nnlib.py
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@ -276,7 +276,7 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
return func
nnlib.style_loss = style_loss
def dssim(k1=0.01, k2=0.03, max_value=1.0):
def dssim(kernel_size=11, k1=0.01, k2=0.03, max_value=1.0):
# port of tf.image.ssim to pure keras in order to work on plaidML backend.
def func(y_true, y_pred):
@ -295,7 +295,7 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
g = K.tile (g, (1,1,ch,1))
return g
kernel = _fspecial_gauss(11,1.5)
kernel = _fspecial_gauss(kernel_size,1.5)
def reducer(x):
return K.depthwise_conv2d(x, kernel, strides=(1, 1), padding='valid')