fanwuyang/neural-enhance-super-resolution
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Fix padding code, more reliable for specific upscale/downscale combinations.

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Alex J. Champandard
2016-11-02 13:03:00 +01:00
parent 3b2a6b9d8d
commit 90c0b7ea43
1 changed files with 11 additions and 13 deletions
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enhance.py
+10 -12
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@@ -45,7 +45,7 @@ add_arg('--train-jpeg', default=None, type=int, help='JPEG c
add_arg('--epochs', default=10, type=int, help='Total number of iterations in training.')
add_arg('--epoch-size', default=72, type=int, help='Number of batches trained in an epoch.')
add_arg('--save-every', default=10, type=int, help='Save generator after every training epoch.')
add_arg('--batch-shape', default=192, type=int, help='Resolution of images in training batch.')
add_arg('--batch-shape', default=256, type=int, help='Resolution of images in training batch.')
add_arg('--batch-size', default=10, type=int, help='Number of images per training batch.')
add_arg('--buffer-size', default=1500, type=int, help='Total image fragments kept in cache.')
add_arg('--buffer-similar', default=5, type=int, help='Fragments cached for each image loaded.')
@@ -171,16 +171,14 @@ class DataLoader(threading.Thread):
seed = orig.filter(PIL.ImageFilter.GaussianBlur(radius=args.train_blur)) if args.train_blur else orig
seed = seed.resize((orig.size[0]//args.zoom, orig.size[1]//args.zoom), resample=PIL.Image.LANCZOS)
seed = scipy.misc.fromimage(seed).astype(np.float32)
seed += scipy.random.normal(scale=args.train_noise, size=(seed.shape[0], seed.shape[1], 1)) if args.train_noise else 0.0
"""
if args.train_jpeg:
buffer = io.BytesIO()
scipy.misc.toimage(seed, cmin=0, cmax=255).save(buffer, format='jpeg', quality=args.train_jpeg)
with PIL.Image.open(buffer) as compressed:
img = scipy.misc.fromimage(compressed, mode='RGB')
"""
seed.save(buffer, format='jpeg', quality=args.train_jpeg+random.randrange(-15,+15))
seed = PIL.Image.open(buffer)
seed = scipy.misc.fromimage(seed, mode='RGB').astype(np.float32)
seed += scipy.random.normal(scale=args.train_noise, size=(seed.shape[0], seed.shape[1], 1)) if args.train_noise else 0.0
orig = scipy.misc.fromimage(orig).astype(np.float32)
@@ -251,7 +249,7 @@ class Model(object):
# Compute batch-size to take into account there's no zero-padding of generator convolution layers.
s = args.batch_shape // args.zoom
current = lasagne.layers.helper.get_output_shape(self.network['out'], {self.network['seed']: (1, 3, s, s)})
args.batch_shape = args.batch_shape * 2 - current[2]
args.batch_shape += int(args.batch_shape - current[2])
self.network['img'].shape = (args.batch_size, 3, args.batch_shape, args.batch_shape)
self.network['seed'].shape = (args.batch_size, 3, args.batch_shape // args.zoom, args.batch_shape // args.zoom)
@@ -311,8 +309,8 @@ class Model(object):
self.network['upscale%i.2'%i] = SubpixelReshuffleLayer(self.last_layer(), u, 2)
self.make_layer('upscale%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)
self.network['out'] = ConvLayer(self.last_layer(), 3, filter_size=(3,3), stride=(1,1),
pad=self.pad_override or (1,1), nonlinearity=lasagne.nonlinearities.tanh)
self.pad_override = None
def setup_perceptual(self, input):
@@ -495,7 +493,7 @@ class NeuralEnhancer(object):
if t_cur % args.learning_period == 0: l_r *= args.learning_decay
def train(self):
seed_size = int(args.batch_shape / args.zoom)
seed_size = args.batch_shape // args.zoom
images = np.zeros((args.batch_size, 3, args.batch_shape, args.batch_shape), dtype=np.float32)
seeds = np.zeros((args.batch_size, 3, seed_size, seed_size), dtype=np.float32)
learning_rate = self.decay_learning_rate()