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 block(self, n_in, n_out):
m = scn.Sequential()
if self.residual_blocks: # ResNet style blocks
m.add(scn.ConcatTable().add(
scn.Identity() if n_in ==
n_out else scn.NetworkInNetwork(n_in, n_out, False)).add(
scn.Sequential().add(
scn.BatchNormLeakyReLU(
n_in, leakiness=self.leakiness)).add(
scn.SubmanifoldConvolution(
self.dimension, n_in, n_out, 3,
False)).add(
scn.BatchNormLeakyReLU(
n_out,
leakiness=self.leakiness)).add(
scn.SubmanifoldConvolution(
self.dimension, n_out,
n_out, 3, False))))
m.add(scn.AddTable())
else: # VGG style blocks
m.add(scn.BatchNormLeakyReLU(n_in, leakiness=self.leakiness))
m.add(
scn.SubmanifoldConvolution(self.dimension, n_in, n_out, 3,
False))
return m
def __init__(self, nf_in, nf, input_sparsetensor, return_sparsetensor,
max_data_size):
nn.Module.__init__(self)
data_dim = 3
self.nf_in = nf_in
self.nf = nf
self.input_sparsetensor = input_sparsetensor
self.return_sparsetensor = return_sparsetensor
self.max_data_size = max_data_size
if not self.input_sparsetensor:
self.p0 = scn.InputLayer(data_dim, self.max_data_size, mode=0)
self.p1 = scn.SubmanifoldConvolution(data_dim,
nf_in,
nf,
filter_size=FSIZE0,
bias=False)
self.p2 = scn.Sequential()
self.p2.add(scn.ConcatTable().add(scn.Identity()).add(
scn.Sequential().add(scn.BatchNormReLU(nf)).add(
scn.SubmanifoldConvolution(
data_dim, nf, nf, FSIZE0,
False)).add(scn.BatchNormReLU(nf)).add(
scn.SubmanifoldConvolution(data_dim, nf, nf, FSIZE0,
False)))).add(
scn.AddTable())
self.p2.add(scn.BatchNormReLU(nf))
# downsample space by factor of 2
self.p3 = scn.Sequential().add(
scn.Convolution(data_dim, nf, nf, FSIZE1, 2, False))
self.p3.add(scn.BatchNormReLU(nf))
if not self.return_sparsetensor:
self.p4 = scn.SparseToDense(data_dim, nf)
def _make_transpose(self, transblock, planes, blocks, stride=1):
upsample = None
if stride != 1:
upsample = scn.Sequential(
scn.SparseToDense(2,self.inplanes * transblock.expansion),
nn.ConvTranspose2d(self.inplanes * transblock.expansion, planes,
kernel_size=2, stride=stride, padding=0, bias=False),
scn.DenseToSparse(2),
scn.BatchNormalization(planes)
)
elif self.inplanes * transblock.expansion != planes:
upsample = scn.Sequential(
scn.NetworkInNetwork(self.inplanes * transblock.expansion, planes, False),
scn.BatchNormalization(planes)
)
layers = []
for i in range(1, blocks):
layers.append(transblock(self.inplanes, self.inplanes * transblock.expansion))
layers.append(transblock(self.inplanes, planes, stride, upsample))
self.inplanes = planes // transblock.expansion
return scn.Sequential(*layers)
def block(self,
m,
a,
b,
dimension=3,
residual_blocks=False,
leakiness=0,
kernel_size=3,
use_batch_norm=True): # default using residual_block
if use_batch_norm:
Activation = lambda channels: scn.BatchNormLeakyReLU(
channels, leakiness=leakiness)
else:
Activation = lambda channels: scn.LeakyReLU(leakiness)
if residual_blocks: #ResNet style blocks
m.add(scn.ConcatTable().add(scn.Identity(
) if a == b else scn.NetworkInNetwork(a, b, False)).add(
scn.Sequential().add(Activation(a)).add(
scn.SubmanifoldConvolution(dimension, a, b, kernel_size,
False)).add(Activation(b)).add(
scn.SubmanifoldConvolution(
dimension, b, b,
kernel_size,
False)))).add(
scn.AddTable())
else: #VGG style blocks
m.add(scn.Sequential().add(Activation(a)).add(
scn.SubmanifoldConvolution(dimension, a, b, kernel_size,
False)))
def block(self, nPlanes, n, reps, stride):
m = scn.Sequential()
for rep in range(reps):
if rep == 0:
m.add(scn.BatchNormReLU(nPlanes))
m.add(scn.ConcatTable().add(self.residual(
nPlanes, n, stride)).add(scn.Sequential().add(
scn.SubmanifoldConvolution(self.dimension, nPlanes, n,
3, False) if stride ==
1 else scn.Convolution(
self.dimension, nPlanes, n, 2, stride, False)).add(
scn.BatchNormReLU(n)).add(
scn.SubmanifoldConvolution(
self.dimension, n, n, 3, False))))
else:
m.add(scn.ConcatTable().add(scn.Sequential().add(
scn.BatchNormReLU(nPlanes)).add(
scn.SubmanifoldConvolution(
self.dimension, nPlanes, n, 3,
False)).add(scn.BatchNormReLU(n)).add(
scn.SubmanifoldConvolution(
self.dimension, n, n, 3,
False))).add(scn.Identity()))
m.add(scn.AddTable())
nPlanes = n
return m
def __init__(self, sgc_config):
nn.Module.__init__(self)
self.stage1 = scn.Sequential().add(
scn.ValidConvolution(3, 1, 16, 3, False))
self.stage1_2 = scn.MaxPooling(3, 2, 2)
self.stage2 = scn.Sequential()
res(self.stage2, 3, 16, 64)
self.stage2_2 = scn.MaxPooling(3, 2, 2)
self.stage3 = UNet6(3, nClasses, sgc_config=sgc_config)
self.densePred = scn.SparseToDense(3, nClasses)
self.sgc_config = sgc_config
def __init__(self):
super(CNN, self).__init__()
###############################
# Hardcoded settings
###############################
self._dimension = 3
reps = 2
kernel_size = 2
num_strides = 7
init_num_features = 8
nInputFeatures = 1
spatial_size = 128 #padding the rest for 169 PMTs
num_classes = 2 # good versus ghost
nPlanes = [(2**i) * init_num_features for i in range(0, num_strides)
] # every layer double the number of features
downsample = [kernel_size, 2]
leakiness = 0
#################################
# Input layer
#################################
self.input = scn.Sequential().add(
scn.InputLayer(self._dimension, spatial_size, mode=3)).add(
scn.SubmanifoldConvolution(self._dimension, nInputFeatures,
init_num_features, 3,
False)) # Kernel size 3, no bias
self.concat = scn.JoinTable()
#################################
# Encode layers
#################################\
self.encoding_conv = scn.Sequential()
for i in range(num_strides):
if i < 4: #hardcoded
self.encoding_conv.add(
scn.BatchNormLeakyReLU(
nPlanes[i], leakiness=leakiness)).add(
scn.Convolution(self._dimension, nPlanes[i],
nPlanes[i + 1], downsample[0],
downsample[1], False))
elif i < num_strides - 1:
self.encoding_conv.add(scn.MaxPooling(self._dimension, 2, 2))
self.output = scn.Sequential().add(
scn.SparseToDense(self._dimension, nPlanes[-1]))
###################################
# Final linear layer
###################################
self.deepest_layer_num_features = int(
nPlanes[-1] * np.power(spatial_size / (2**(num_strides - 1)), 3.))
self.classifier = torch.nn.Sequential(
torch.nn.ReLU(),
torch.nn.Linear(self.deepest_layer_num_features, 2),
)
def baz(depth, nPlanes):
if depth == 1:
return scn.Sequential().add(foo(nPlanes)).add(bar(nPlanes, True))
else:
return scn.Sequential().add(foo(nPlanes)).add(scn.ConcatTable().add(bar(nPlanes,False)).add(
scn.Sequential()\
.add(scn.BatchNormReLU(nPlanes))\
.add(scn.Convolution(dimension, nPlanes, l(nPlanes), 2, 2, False))\
.add(baz(depth-1,l(nPlanes)))\
.add(scn.UnPooling(dimension, 2, 2))
)).add(scn.AddTable())
def decoder_block(self, nPlanes, n, reps, stride):
m = scn.Sequential()
for rep in range(reps):
m.add(scn.ConcatTable().add(
scn.Sequential().add(scn.BatchNormReLU(nPlanes)).add(
scn.SubmanifoldConvolution(self.dimension, nPlanes, n, 3,
False))
# .add(scn.BatchNormReLU(n))
# .add(scn.SubmanifoldConvolution(dimension, n, n, 3, False))
).add(scn.Identity()))
m.add(scn.AddTable())
nPlanes = n
return m
def iter_unet(self, n_input_planes):
# different from scn implementation, which is a recursive function
enc_convs = scn.Sequential()
dec_convs = scn.Sequential()
for n_planes_in, n_planes_out in zip(self.n_planes[:-1],
self.n_planes[1:]):
# encode
conv1x1 = scn.Sequential()
for i in range(self.block_reps):
conv1x1.add(
self.block(
n_input_planes
if n_input_planes != -1 else n_planes_in, n_planes_in))
n_input_planes = -1
conv = scn.Sequential()
conv.add(
scn.BatchNormLeakyReLU(n_planes_in, leakiness=self.leakiness))
conv.add(
scn.Convolution(self.dimension, n_planes_in, n_planes_out,
self.downsample[0], self.downsample[1], False))
enc_conv = scn.Sequential()
enc_conv.add(conv1x1)
enc_conv.add(conv)
enc_convs.add(enc_conv)
# decode(corresponding stage of encode; symmetric with U)
b_join = scn.Sequential() # before_join
b_join.add(
scn.BatchNormLeakyReLU(n_planes_out, leakiness=self.leakiness))
b_join.add(
scn.Deconvolution(self.dimension, n_planes_out, n_planes_in,
self.downsample[0], self.downsample[1],
False))
join_table = scn.JoinTable()
a_join = scn.Sequential() # after_join
for i in range(self.block_reps):
a_join.add(
self.block(n_planes_in * (2 if i == 0 else 1),
n_planes_in))
dec_conv = scn.Sequential()
dec_conv.add(b_join)
dec_conv.add(join_table)
dec_conv.add(a_join)
dec_convs.add(dec_conv)
middle_conv = scn.Sequential()
for i in range(self.block_reps):
middle_conv.add(
self.block(
n_input_planes if n_input_planes != -1 else
self.n_planes[-1], self.n_planes[-1]))
n_input_planes = -1
return enc_convs, middle_conv, dec_convs
def block(self, m, a, b, dimension=3, residual_blocks=False, leakiness=0): # default using residual_block
if residual_blocks: #ResNet style blocks
m.add(scn.ConcatTable()
.add(scn.Identity() if a == b else scn.NetworkInNetwork(a, b, False))
.add(scn.Sequential()
.add(scn.BatchNormLeakyReLU(a,leakiness=leakiness))
.add(scn.SubmanifoldConvolution(dimension, a, b, 3, False))
.add(scn.BatchNormLeakyReLU(b,leakiness=leakiness))
.add(scn.SubmanifoldConvolution(dimension, b, b, 3, False)))
).add(scn.AddTable())
else: #VGG style blocks
m.add(scn.Sequential()
.add(scn.BatchNormLeakyReLU(a,leakiness=leakiness))
.add(scn.SubmanifoldConvolution(dimension, a, b, 3, False)))
def baz(nPlanes):
m=scn.Sequential()
foo(m,nPlanes[0])
if len(nPlanes)==1:
bar(m,nPlanes[0],True)
else:
a=scn.Sequential()
bar(a,nPlanes,False)
b=scn.Sequential(
scn.BatchNormLeakyReLU(nPlanes,leakiness=leakiness),
scn.Convolution(dimension, nPlanes[0], nPlanes[1], downsample[0], downsample[1], False),
baz(nPlanes[1:]),
scn.UnPooling(dimension, downsample[0], downsample[1]))
m.add(ConcatTable(a,b))
m.add(scn.AddTable())
def __init__(self,
is_3d,
num_strides=3,
base_num_outputs=16,
num_classes=3,
spatialSize=192):
nn.Module.__init__(self)
dimension = 3 if is_3d else 2
reps = 2 # Conv block repetition factor
kernel_size = 2 # Use input_spatial_size method for other values?
m = base_num_outputs # Unet number of features
nPlanes = [i * m for i in range(1, num_strides + 1)
] # UNet number of features per level
# nPlanes = [(2**i) * m for i in range(1, num_strides+1)] # UNet number of features per level
nInputFeatures = 1
self.sparseModel = scn.Sequential().add(
scn.InputLayer(dimension, spatialSize, mode=3)).add(
scn.SubmanifoldConvolution(
dimension, nInputFeatures, m, 3,
False)).add( # Kernel size 3, no bias
UNet(dimension,
reps,
nPlanes,
residual_blocks=True,
downsample=[kernel_size, 2])
).add( # downsample = [filter size, filter stride]
scn.BatchNormReLU(m)).add(scn.OutputLayer(dimension))
self.linear = nn.Linear(m, num_classes)
def __init__(self, flags):
import sparseconvnet as scn
super(UResNet, self).__init__()
self._flags = flags
dimension = flags.DATA_DIM
reps = 2 # Conv block repetition factor
kernel_size = 2 # Use input_spatial_size method for other values?
m = flags.URESNET_FILTERS # Unet number of features
nPlanes = [i * m for i in range(1, flags.URESNET_NUM_STRIDES + 1)
] # UNet number of features per level
# nPlanes = [(2**i) * m for i in range(1, num_strides+1)] # UNet number of features per level
nInputFeatures = 1
self.sparseModel = scn.Sequential().add(
scn.InputLayer(dimension, flags.SPATIAL_SIZE, mode=3)).add(
scn.SubmanifoldConvolution(
dimension, nInputFeatures, m, 3,
False)).add( # Kernel size 3, no bias
scn.UNet(dimension,
reps,
nPlanes,
residual_blocks=True,
downsample=[kernel_size, 2])
).add( # downsample = [filter size, filter stride]
scn.BatchNormReLU(m)).add(scn.OutputLayer(dimension))
self.linear = torch.nn.Linear(m, flags.NUM_CLASS)
def __init__(self, cfg):
import sparseconvnet as scn
super(UResNet, self).__init__()
self._model_config = cfg['modules']['uresnet']
self._dimension = self._model_config.get('data_dim', 3)
num_strides = self._model_config.get('num_strides', 5)
spatial_size = self._model_config.get('spatial_size', 512)
num_classes = self._model_config.get('num_classes', 5)
m = self._model_config.get('filters', 16) # Unet number of features
nInputFeatures = self._model_config.get('features', 1)
reps = 2 # Conv block repetition factor
kernel_size = 2 # Use input_spatial_size method for other values?
nPlanes = [i * m for i in range(1, num_strides + 1)
] # UNet number of features per level
# nPlanes = [(2**i) * m for i in range(1, num_strides+1)] # UNet number of features per level
self.sparseModel = scn.Sequential().add(
scn.InputLayer(self._dimension, spatial_size, mode=3)).add(
scn.SubmanifoldConvolution(
self._dimension, nInputFeatures, m, 3,
False)).add( # Kernel size 3, no bias
scn.UNet(self._dimension,
reps,
nPlanes,
residual_blocks=True,
downsample=[kernel_size, 2])
).add( # downsample = [filter size, filter stride]
scn.BatchNormReLU(m)).add(
scn.OutputLayer(self._dimension))
self.linear = torch.nn.Linear(m, num_classes)
