fanwuyang/neural-enhance-super-resolution
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Experiment with recursive super-resolution and weight reuse, mixed results.

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This commit is contained in:
Alex J. Champandard
2016-10-28 04:28:30 +02:00
parent 87304c93a6
commit c456221cb5
1 changed files with 34 additions and 9 deletions
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enhance.py
+34 -9
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@@ -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)
prelu = lasagne.layers.ParametricRectifierLayer(conv, alpha=lasagne.init.Constant(alpha))
orig = '1.'+''.join(name.split('.')[1:])
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
self.make_layer('iter.0', input, units, filter_size=(5,5), pad=(2,2))
units_iter = extend(args.generator_filters)
units = next(units_iter)
self.make_layer('iter.0-A', input, units, filter_size=(5,5), pad=(2,2))
self.make_layer('iter.0-B', self.last_layer(), units, filter_size=(5,5), pad=(2,2))
self.network['iter.0'] = self.last_layer()
for i in range(0, args.generator_blocks):
self.network['iter.%i'%(i+1)] = self.make_block('iter.%i'%(i+1), self.last_layer(), units)
for i in range(0, args.generator_iters):
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(args.scales, 0, -1):
u = units // 2**(args.scales-i)
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)