def __init__(self,
activation_constructor,
clusterInputSize,
Block,
Cstd,
DIM,
sigmoid=False):
super(TucodecEncode1D4L, self).__init__()
device = get_device()
encode = True
#downsamples and upsamples
downsample1 = DownUp1D.downsample1(activation_constructor, Cstd, Cstd)
upsample1 = DownUp1D.upsample1(activation_constructor, Cstd, Cstd)
downsample2 = DownUp1D.downsample2(activation_constructor, Cstd, Cstd)
upsample2 = DownUp1D.upsample2(activation_constructor, Cstd, Cstd)
# 4 Linear Layers
self.fc1 = nn.Linear(in_features=clusterInputSize, out_features=1024)
self.fc2 = nn.Linear(in_features=1024, out_features=512)
self.fc3 = nn.Linear(in_features=512, out_features=256)
self.fc4 = nn.Linear(in_features=256, out_features=128)
#main trunk first
self.conv1 = nn.Conv1d(1, Cstd, kernel_size=3, stride=2, padding=1)
self.gdn2 = GDN(Cstd, device, not encode)
self.conv3 = nn.Conv1d(Cstd, Cstd, kernel_size=2, stride=2, padding=0)
self.gdn4 = GDN(Cstd, device, not encode)
self.rnab5 = RAB1D(encode,
activation_constructor,
Cstd,
sigmoid,
Block,
downsample=downsample1,
upsample=upsample1)
self.conv6 = nn.Conv1d(Cstd, Cstd, kernel_size=3, stride=2)
self.gdn7 = GDN(Cstd, device, not encode)
self.conv8 = nn.Conv1d(Cstd, Cstd, kernel_size=3, stride=2, padding=1)
self.rnab9 = RAB1D(encode,
activation_constructor,
Cstd,
sigmoid,
Block,
downsample=downsample2,
upsample=upsample2)
#multi-res path
self.convA = nn.Conv1d(Cstd, Cstd, kernel_size=3, stride=8)
self.convB = nn.Conv1d(Cstd, Cstd, kernel_size=3, stride=4, padding=0)
self.convC = nn.Conv1d(Cstd, Cstd, kernel_size=3, stride=2, padding=1)
#final conv
self.conv10 = nn.Conv1d(4 * Cstd, Cstd, kernel_size=2, stride=2)
def __init__(self,
activation_constructor,
Block,
Cstd,
DIM,
sigmoid=False):
super(TucodecEncode1D0L, self).__init__()
device = get_device()
encode = True
#downsamples and upsamples
downsample1 = DownUp1D.downsample1(activation_constructor, Cstd, Cstd)
upsample1 = DownUp1D.upsample1(activation_constructor, Cstd, Cstd)
downsample2 = DownUp1D.downsample2(activation_constructor, Cstd, Cstd)
upsample2 = DownUp1D.upsample2(activation_constructor, Cstd, Cstd)
#main trunk first
self.conv1 = nn.Conv1d(1, Cstd, kernel_size=3, stride=2, padding=1)
self.gdn2 = GDN(Cstd, device, not encode)
self.conv3 = nn.Conv1d(Cstd, Cstd, kernel_size=2, stride=2, padding=0)
self.gdn4 = GDN(Cstd, device, not encode)
self.rnab5 = RAB1D(encode,
activation_constructor,
Cstd,
sigmoid,
Block,
downsample=downsample1,
upsample=upsample1)
self.conv6 = nn.Conv1d(Cstd, Cstd, kernel_size=3, stride=2)
self.gdn7 = GDN(Cstd, device, not encode)
self.conv8 = nn.Conv1d(Cstd, Cstd, kernel_size=3, stride=2, padding=1)
self.rnab9 = RAB1D(encode,
activation_constructor,
Cstd,
sigmoid,
Block,
downsample=downsample2,
upsample=upsample2)
#multi-res path
self.convA = nn.Conv1d(Cstd, Cstd, kernel_size=3, stride=8)
self.convB = nn.Conv1d(Cstd, Cstd, kernel_size=3, stride=4, padding=0)
self.convC = nn.Conv1d(Cstd, Cstd, kernel_size=3, stride=2, padding=1)
#final conv
self.conv10 = nn.Conv1d(4 * Cstd, Cstd, kernel_size=2, stride=2)
def __init__(self,
activation_constructor,
Block,
Cstd,
dim,
sigmoid=False):
super(TucodecDecode, self).__init__()
device = get_device()
encode = False
#downsamples and upsamples
downsample2 = DownUp.downsample2(activation_constructor, Cstd, Cstd)
upsample2 = DownUp.upsample2(activation_constructor, Cstd, Cstd)
downsample1 = DownUp.downsample1(activation_constructor, Cstd, Cstd)
upsample1 = DownUp.upsample1(activation_constructor, Cstd, Cstd)
#Keep numbering from Encoder
self.conv10 = nn.ConvTranspose3d(Cstd,
Cstd,
kernel_size=(2, 2, 2),
stride=2)
self.rb10a = Block(
encode,
activation_constructor,
Cstd,
)
self.rb10b = Block(
encode,
activation_constructor,
Cstd,
)
self.rnab9 = RAB(encode,
activation_constructor,
Cstd,
sigmoid,
Block,
downsample=downsample2,
upsample=upsample2)
self.conv8 = nn.ConvTranspose3d(Cstd,
Cstd,
kernel_size=(3, 2, 2),
stride=2,
padding=(1, 1, 0))
self.gdn7 = GDN(Cstd, device, encode)
self.conv6 = nn.ConvTranspose3d(
Cstd,
Cstd,
kernel_size=(3, 4, 2),
stride=2,
)
self.rnab5 = RAB(encode,
activation_constructor,
Cstd,
sigmoid,
Block,
downsample=downsample1,
upsample=upsample1)
self.gdn4 = GDN(Cstd, device, encode)
self.conv3 = nn.ConvTranspose3d(Cstd,
Cstd,
kernel_size=(2, 3, 2),
stride=2,
padding=(0, 1, 0))
self.gdn2 = GDN(Cstd, device, encode)
self.conv1 = nn.ConvTranspose3d(Cstd,
1,
kernel_size=(3, 3, 2),
stride=2,
padding=(1, 1, 0))
def __init__(self,
activation_constructor,
Block,
Cstd,
DIM,
sigmoid=False):
super(TucodecDecode1D0L, self).__init__()
device = get_device()
encode = False
#downsamples and upsamples
downsample2 = DownUp1D.downsample2(activation_constructor, Cstd, Cstd)
upsample2 = DownUp1D.upsample2(activation_constructor, Cstd, Cstd)
downsample1 = DownUp1D.downsample1(activation_constructor, Cstd, Cstd)
upsample1 = DownUp1D.upsample1(activation_constructor, Cstd, Cstd)
#Keep numbering from Encoder
self.conv10 = nn.ConvTranspose1d(Cstd, Cstd, kernel_size=2, stride=2)
self.rb10a = Block(
encode,
activation_constructor,
Cstd,
)
self.rb10b = Block(
encode,
activation_constructor,
Cstd,
)
self.rnab9 = RAB1D(encode,
activation_constructor,
Cstd,
sigmoid,
Block,
downsample=downsample2,
upsample=upsample2)
self.conv8 = nn.ConvTranspose1d(Cstd,
Cstd,
kernel_size=3,
stride=2,
padding=1,
output_padding=0)
self.gdn7 = GDN(Cstd, device, encode)
self.conv6 = nn.ConvTranspose1d(Cstd,
Cstd,
kernel_size=3,
stride=2,
output_padding=1)
self.rnab5 = RAB1D(encode,
activation_constructor,
Cstd,
sigmoid,
Block,
downsample=downsample1,
upsample=upsample1)
self.gdn4 = GDN(Cstd, device, encode)
self.conv3 = nn.ConvTranspose1d(Cstd,
Cstd,
kernel_size=2,
stride=2,
padding=0,
output_padding=1)
self.gdn2 = GDN(Cstd, device, encode)
self.conv1 = nn.ConvTranspose1d(Cstd,
1,
kernel_size=3,
stride=2,
padding=1,
output_padding=0)