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old SAE model will not work with this update.

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Fixed bug when SAE can be collapsed during a time.

SAE: removed CA weights and encoder/decoder dims.

added new options:

Encoder dims per channel (21-85 ?:help skip:%d)
More encoder dims help to recognize more facial features, but require more VRAM. You can fine-tune model size to fit your GPU.

Decoder dims per channel (11-85 ?:help skip:%d)
More decoder dims help to get better details, but require more VRAM. You can fine-tune model size to fit your GPU.

Add residual blocks to decoder? (y/n, ?:help skip:n) :
These blocks help to get better details, but require more computing time.

Remove gray border? (y/n, ?:help skip:n) :
Removes gray border of predicted face, but requires more computing resources.
This commit is contained in:
iperov 2019-03-24 15:35:02 +04:00
parent 4f4447d719
commit 37505d88e3
12 changed files with 264 additions and 47242 deletions
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14
nnlib/nnlib.py
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@ -608,6 +608,20 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
return K.tf.pad(x, [[0,0], [h_pad,h_pad], [w_pad,w_pad], [0,0] ], 'REFLECT')
elif backend == "plaidML":
return TileOP_ReflectionPadding2D.function(x, self.padding[0], self.padding[1])
else:
if K.image_data_format() == 'channels_last':
if x.shape.ndims == 4:
w = K.concatenate ([ x[:,:,w_pad:0:-1,:],
x,
x[:,:,-2:-w_pad-2:-1,:] ], axis=2 )
h = K.concatenate ([ w[:,h_pad:0:-1,:,:],
w,
w[:,-2:-h_pad-2:-1,:,:] ], axis=1 )
return h
else:
raise NotImplemented
else:
raise NotImplemented
nnlib.ReflectionPadding2D = ReflectionPadding2D