Experiment with recursive super-resolution and weight reuse, mixed results.

enhance.py

@@ -85,6 +85,8 @@ def warn(message, *lines):
     string = "\n{}WARNING: " + message + "{}\n" + "\n".join(lines) + "{}\n"
     print(string.format(ansi.YELLOW_B, ansi.YELLOW, ansi.ENDC))
 
+def extend(lst): return itertools.chain(lst, itertools.repeat(lst[-1]))
+
 print("""{}   {}Super Resolution for images and videos powered by Deep Learning!{}
   - Code licensed as AGPLv3, models under CC BY-NC-SA.{}""".format(ansi.CYAN_B, __doc__, ansi.CYAN, ansi.ENDC))
 
@@ -231,8 +233,23 @@ 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):
-        conv = ConvLayer(input, units, filter_size=filter_size, stride=stride, pad=pad, nonlinearity=None)
+        orig = '1.'+''.join(name.split('.')[1:])
-        prelu = lasagne.layers.ParametricRectifierLayer(conv, alpha=lasagne.init.Constant(alpha))
+        if orig+'x' in self.network:
+            print('reused', orig, 'for', name)
+            l = self.network[orig +'x']
+            extra = {'W': l.W, 'b': l.b}
+        else:
+            extra = {}
+
+        conv = ConvLayer(input, units, filter_size=filter_size, stride=stride, pad=pad, nonlinearity=None, **extra)
+
+        alpha = lasagne.init.Constant(alpha)
+        if orig +'>' in self.network:
+            l = self.network[orig +'>']
+            alpha = l.alpha
+
+        prelu = lasagne.layers.ParametricRectifierLayer(conv, alpha=alpha)
+
         self.network[name+'x'] = conv
         self.network[name+'>'] = prelu
         return prelu
@@ -244,17 +261,25 @@ class Model(object):
 
     def setup_generator(self, input, config):
         for k, v in config.items(): setattr(args, k, v)
-        units = args.generator_filters
+        units_iter = extend(args.generator_filters)
-        self.make_layer('iter.0', input, units, filter_size=(5,5), pad=(2,2))
+        units = next(units_iter)
-
+        self.make_layer('iter.0-A', input, units, filter_size=(5,5), pad=(2,2))
-        for i in range(0, args.generator_blocks):
+        self.make_layer('iter.0-B', self.last_layer(), units, filter_size=(5,5), pad=(2,2))
-            self.network['iter.%i'%(i+1)] = self.make_block('iter.%i'%(i+1), self.last_layer(), units)
+        self.network['iter.0'] = self.last_layer()
-
+
-        for i in range(args.scales, 0, -1):
+        for i in range(0, args.generator_iters):
-            u = units // 2**(args.scales-i)
+            base = self.last_layer()
+            for j in range(0, args.generator_blocks):
+                self.make_block('%i.iter-%i'%(i+1, j), self.last_layer(), units)
+                print('iter.%i-%i'%(i+1, j))
+            # self.network['iter.%i'%(i+1)] = DropPathLayer([base, self.last_layer()])
+
+        for i in range(0, args.scales):
+            u = next(units_iter)
             self.make_layer('scale%i.3'%i, self.last_layer(), u*4)
             self.network['scale%i.2'%i] = SubpixelReshuffleLayer(self.last_layer(), u, 2)
-            self.make_layer('scale%i.1'%i, self.network['scale%i.2'%i], u)
+            self.make_layer('scale%i.1'%i, self.last_layer(), u)
+
 
         self.network['out'] = ConvLayer(self.last_layer(), 3, filter_size=(5,5), stride=(1,1), pad=(2,2),
                                                               nonlinearity=lasagne.nonlinearities.tanh)