fanwuyang/neural-enhance-super-resolution
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Remove reflection layer, important but not the bottleneck right now and quite slower.

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Alex J. Champandard
2016-11-04 14:29:15 +01:00
parent 9bba9850d5
commit da354f3e55
1 changed files with 1 additions and 30 deletions
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enhance.py
+1 -30
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@@ -242,34 +242,6 @@ class SubpixelReshuffleLayer(lasagne.layers.Layer):
return out return out
class ReflectLayer(lasagne.layers.Layer):
"""Based on more code by ajbrock: https://gist.github.com/ajbrock/a3858c26282d9731191901b397b3ce9f
"""
def __init__(self, incoming, pad, batch_ndim=2, **kwargs):
super(ReflectLayer, self).__init__(incoming, **kwargs)
self.pad = (pad, pad)
self.batch_ndim = batch_ndim
def get_output_shape_for(self, input_shape):
output_shape = list(input_shape)
for k, p in enumerate(self.pad):
if output_shape[k + self.batch_ndim] is None: continue
l, r = p, p
output_shape[k + self.batch_ndim] += l + r
return tuple(output_shape)
def get_output_for(self, x, **kwargs):
out = T.zeros(self.get_output_shape_for(x.shape))
p0, p1 = self.pad
out = T.set_subtensor(out[:,:,:p0,p1:-p1], x[:,:,p0:0:-1,:])
out = T.set_subtensor(out[:,:,-p0:,p1:-p1], x[:,:,-2:-(2+p0):-1,:])
out = T.set_subtensor(out[:,:,p0:-p0,p1:-p1], x)
out = T.set_subtensor(out[:,:,:,:p1], out[:,:,:,(2*p1):p1:-1])
out = T.set_subtensor(out[:,:,:,-p1:], out[:,:,:,-(p1+2):-(2*p1+2):-1])
return out
class Model(object): class Model(object):
def __init__(self): def __init__(self):
@@ -296,8 +268,7 @@ class Model(object):
return list(self.network.values())[-1] return list(self.network.values())[-1]
def make_layer(self, name, input, units, filter_size=(3,3), stride=(1,1), pad=(1,1), alpha=0.25): def make_layer(self, name, input, units, filter_size=(3,3), stride=(1,1), pad=(1,1), alpha=0.25):
reflected = ReflectLayer(input, pad=pad[0]) if pad[0] > 0 else input conv = ConvLayer(input, units, filter_size, stride=stride, pad=(0,0), nonlinearity=None)
conv = ConvLayer(reflected, units, filter_size, stride=stride, pad=(0,0), nonlinearity=None)
prelu = lasagne.layers.ParametricRectifierLayer(conv, alpha=lasagne.init.Constant(alpha)) prelu = lasagne.layers.ParametricRectifierLayer(conv, alpha=lasagne.init.Constant(alpha))
self.network[name+'x'] = conv self.network[name+'x'] = conv
self.network[name+'>'] = prelu self.network[name+'>'] = prelu