Remove reflection layer, important but not the bottleneck right now and quite slower.

enhance.py

@@ -242,34 +242,6 @@ class SubpixelReshuffleLayer(lasagne.layers.Layer):
         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):
 
     def __init__(self):
@@ -296,8 +268,7 @@ class Model(object):
         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):
-        reflected = ReflectLayer(input, pad=pad[0]) if pad[0] > 0 else input
-        conv = ConvLayer(reflected, units, filter_size, stride=stride, pad=(0,0), nonlinearity=None)
+        conv = ConvLayer(input, units, filter_size, stride=stride, pad=(0,0), nonlinearity=None)
         prelu = lasagne.layers.ParametricRectifierLayer(conv, alpha=lasagne.init.Constant(alpha))
         self.network[name+'x'] = conv
         self.network[name+'>'] = prelu