def __init__(self,
D_in,
D_out,
ks,
activation=F.elu,
fact=factorize,
resid=residual,
bn=True,
momentum=0.001):
nn.Module.__init__(self)
st = (1, 1, 1)
pd = layers.pad_size(ks, "same")
# conv layer constructor
conv_constr = layers.FactConv if fact else layers.Conv
bias = not bn
self.resid = resid
self.bn = bn
self.activation = activation
first_pd = layers.pad_size((1, ks[1], ks[2]), "same")
self.conv1 = conv_constr(D_in, D_out, (1, ks[1], ks[2]), st, first_pd,
bias)
self.conv2 = conv_constr(D_out, D_out, ks, st, pd, bias)
self.conv3 = conv_constr(D_out, D_out, ks, st, pd, bias)
if self.bn:
self.bn1 = nn.BatchNorm3d(D_out, momentum=momentum)
self.bn2 = nn.BatchNorm3d(D_out, momentum=momentum)
self.bn3 = nn.BatchNorm3d(D_out, momentum=momentum)
def __init__(self,
D_in,
D_out,
ks,
up=2,
activation=F.elu,
fact=factorize,
resid=residual,
bn=True,
momentum=0.001):
nn.Module.__init__(self)
#ConvT constructor
upsampling_constr = layers.ResizeConv
self.bn = bn
self.activation = activation
pd = layers.pad_size(ks, "same")
st = (1, 1, 1)
bias = not bn
self.upsampling = upsampling_constr(D_in,
D_out,
ks,
st=st,
pd=pd,
bias=bias,
mode="nearest")
if bn:
self.bn1 = nn.BatchNorm3d(D_out, momentum=momentum)
self.convmod = ConvMod(D_out, D_out, ks, fact=fact, resid=resid, bn=bn)
def __init__(self, D_in, D_out, ks, st=(1,1,1), activation = F.elu,
fact=factorize):
nn.Module.__init__(self)
pd = layers.pad_size(ks, "same")
conv_constr = layers.FactConv if fact else layers.Conv
self.activation = activation
self.conv = conv_constr(D_in, D_out, ks, st, pd, bias=True)
def __init__(self, D_in, outspec, ks=io_size, st=io_stride):
""" outspec should be an Ordered Dict """
nn.Module.__init__(self)
pd = layers.pad_size(ks, "same")
self.output_layers = []
for (name,d_out) in outspec.items():
setattr(self, name, layers.Conv(D_in, d_out, ks, st, pd, bias=True))
self.output_layers.append(name)
