def v(depth, nPlanes):
m = scn.Sequential()
if depth == 1:
for _ in range(reps):
res(m, nPlanes, nPlanes, dropout_p)
else:
m = scn.Sequential()
for _ in range(reps):
res(m, nPlanes, nPlanes, dropout_p)
if dropout_width:
m.add(scn.ConcatTable().add(scn.Identity()).add(
scn.Sequential().add(scn.BatchNormReLU(nPlanes)).add(
#In place of Maxpooling
scn.Convolution(
dimension, nPlanes, nPlanes, 2, 2,
False)).add(scn.Dropout(dropout_p)).add(
v(depth - 1, nPlanes)).add(
scn.BatchNormReLU(nPlanes)).add(
scn.Deconvolution(
dimension, nPlanes, nPlanes, 2, 2,
False))))
else:
m.add(scn.ConcatTable().add(scn.Identity()).add(
scn.Sequential().add(scn.BatchNormReLU(nPlanes)).add(
scn.Convolution(dimension, nPlanes, nPlanes, 2, 2,
False)).add(v(depth - 1, nPlanes)).add(
scn.BatchNormReLU(nPlanes)).add(
scn.Deconvolution(
dimension, nPlanes,
nPlanes, 2, 2, False))))
m.add(scn.JoinTable())
for i in range(reps):
res(m, 2 * nPlanes if i == 0 else nPlanes, nPlanes, dropout_p)
return m
def __init__(self, cfg, name='yresnet_encoder'):
super(YResNetEncoder, self).__init__(cfg, name='network_base')
self.model_config = cfg[name]
# YResNet Configurations
# Conv block repetition factor
self.reps = self.model_config.get('reps', 2)
self.kernel_size = self.model_config.get('kernel_size', 2)
self.num_strides = self.model_config.get('num_strides', 5)
self.num_filters = self.model_config.get('filters', 16)
self.nPlanes = [
i * self.num_filters for i in range(1, self.num_strides + 1)
]
# [filter size, filter stride]
self.downsample = [self.kernel_size, 2]
dropout_prob = self.model_config.get('dropout_prob', 0.5)
# Define Sparse YResNet Encoder
self.encoding_block = scn.Sequential()
self.encoding_conv = scn.Sequential()
for i in range(self.num_strides):
m = scn.Sequential()
for _ in range(self.reps):
self._resnet_block(m, self.nPlanes[i], self.nPlanes[i])
self.encoding_block.add(m)
m = scn.Sequential()
if i < self.num_strides - 1:
m.add(
scn.BatchNormLeakyReLU(self.nPlanes[i], leakiness=self.leakiness)).add(
scn.Convolution(self.dimension, self.nPlanes[i], self.nPlanes[i+1], \
self.downsample[0], self.downsample[1], self.allow_bias)).add(
scn.Dropout(p=dropout_prob))
self.encoding_conv.add(m)
def __init__(self, cfg, name='yresnet_decoder'):
super(YResNetDecoder, self).__init__(cfg, name='network_base')
self.model_config = cfg[name]
self.reps = self.model_config.get('reps',
2) # Conv block repetition factor
self.kernel_size = self.model_config.get('kernel_size', 2)
self.num_strides = self.model_config.get('num_strides', 5)
self.num_filters = self.model_config.get('filters', 16)
self.nPlanes = [
i * self.num_filters for i in range(1, self.num_strides + 1)
]
self.downsample = [self.kernel_size, 2] # [filter size, filter stride]
self.concat = scn.JoinTable()
self.add = scn.AddTable()
dropout_prob = self.model_config.get('dropout_prob', 0.5)
self.encoder_num_filters = self.model_config.get(
'encoder_num_filters', None)
if self.encoder_num_filters is None:
self.encoder_num_filters = self.num_filters
self.encoder_nPlanes = [
i * self.encoder_num_filters
for i in range(1, self.num_strides + 1)
]
# Define Sparse YResNet Decoder.
self.decoding_block = scn.Sequential()
self.decoding_conv = scn.Sequential()
for idx, i in enumerate(list(range(self.num_strides - 2, -1, -1))):
if idx == 0:
m = scn.Sequential().add(
scn.BatchNormLeakyReLU(self.encoder_nPlanes[i + 1],
leakiness=self.leakiness)).add(
scn.Deconvolution(
self.dimension,
self.encoder_nPlanes[i + 1],
self.nPlanes[i],
self.downsample[0],
self.downsample[1],
self.allow_bias))
else:
m = scn.Sequential().add(
scn.BatchNormLeakyReLU(
self.nPlanes[i + 1], leakiness=self.leakiness)).add(
scn.Deconvolution(self.dimension,
self.nPlanes[i + 1],
self.nPlanes[i],
self.downsample[0],
self.downsample[1],
self.allow_bias)).add(
scn.Dropout(p=dropout_prob))
self.decoding_conv.add(m)
m = scn.Sequential()
for j in range(self.reps):
self._resnet_block(m, self.nPlanes[i] + (self.encoder_nPlanes[i] \
if j == 0 else 0), self.nPlanes[i])
self.decoding_block.add(m)
def _res_dropout(m, a, b, p):
m.add(scn.ConcatTable()
.add(scn.Identity() if a == b else scn.NetworkInNetwork(a, b, False))
.add(scn.Sequential()
.add(scn.BatchNormReLU(a))
.add(Dropout(p))
.add(scn.SubmanifoldConvolution(dimension, a, b, 3, False))
.add(scn.BatchNormReLU(b))
.add(scn.Dropout(p))
.add(scn.SubmanifoldConvolution(dimension, b, b, 3, False))))\
.add(scn.AddTable())