def test1D(self):
net = resunet(1, 1, spatial=(32, ))
net(th.rand(1, 1, 16))
net = resunet(1, 1, spatial=(32, ), normalizor='instance')
net(th.rand(1, 1, 16))
net = resunet(1, 1, spatial=(32, ), normalizor='layer')
net(th.rand(1, 1, 16))
def __init__(self,
in_channels,
out_channels,
channels_per_step_in=1,
channels_per_step_out=4):
super().__init__()
self.in_steps = in_channels // channels_per_step_in
self.out_steps = (out_channels - 8) // channels_per_step_out // 4
self.channels_per_step_in = channels_per_step_in
self.channels_per_step_out = channels_per_step_out
self.out_channels = out_channels
self.lstm = ConvLSTM(1,
channels_per_step_out * 4,
kernel_size=3,
num_layers=1,
return_all_layers=True)
self.unet = resunet(16,
8,
block=HyperBottleneck,
relu=CappingRelu(),
ratio=-2,
layers=6,
vblks=[1, 1, 1, 1, 1, 1],
hblks=[1, 1, 1, 1, 1, 1],
scales=[-1, -1, -1, -1, -1, -1],
factors=[1, 1, 1, 1, 1, 1],
spatial=(64, 64))
self.lstm = ConvLSTM(1,
channels_per_step_out * 4,
kernel_size=3,
num_layers=1,
return_all_layers=False)
def __init__(self, setting):
super().__init__(setting)
self.name = 't850d3bg-rasp'
self.resunet = resunet(setting.input_span * len(setting.vars_in) + self.constant_size + 2, 1,
spatial=(32, 64+2), layers=5, ratio=-2,
vblks=[2, 2, 2, 2, 2], hblks=[1, 1, 1, 1, 1],
scales=[-1, -1, -1, -1, -1], factors=[1, 1, 1, 1, 1],
block=HyperBottleneck, relu=CappingRelu(), final_normalized=False)
def testHyp1D(self):
net = resunet(1, 1, spatial=(32, ), block=HyperBasic)
net(th.rand(1, 1, 16))
net = resunet(1,
1,
spatial=(32, ),
normalizor='instance',
block=HyperBasic)
net(th.rand(1, 1, 16))
net = resunet(1,
1,
spatial=(32, ),
normalizor='layer',
block=HyperBasic)
net(th.rand(1, 1, 16))
net = resunet(1, 1, spatial=(32, ), block=HyperBottleneck)
net(th.rand(1, 1, 16))
net = resunet(1,
1,
spatial=(32, ),
normalizor='instance',
block=HyperBottleneck)
net(th.rand(1, 1, 16))
net = resunet(1,
1,
spatial=(32, ),
normalizor='layer',
block=HyperBottleneck)
net(th.rand(1, 1, 16))
def test2D(self):
resunet(1, 1, spatial=(16, 16))
resunet(1, 1, spatial=(16, 32))
resunet(1, 1, spatial=(32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]])
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='instance')
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='layer')
net(th.rand(1, 1, 32, 16))
def __init__(self, setting):
super().__init__(setting)
tube = hyptub(1664, 832, 1664, encoder=linear, decoder=linear)
self.resunet = resunet(setting.input_span * (len(setting.vars) + 2) +
self.constant_size + 2,
1,
spatial=(32, 64 + 2),
layers=5,
ratio=-1,
vblks=[2, 2, 2, 2, 2],
hblks=[1, 1, 1, 1, 1],
scales=[-1, -1, -1, -1, -1],
factors=[1, 1, 1, 1, 1],
block=HyperBottleneck,
relu=CappingRelu(),
enhencer=tube,
final_normalized=False)
def __init__(self,
in_channels,
out_channels,
channels_per_step_in=1,
channels_per_step_out=4):
super().__init__()
self.unet = resunet(in_channels,
out_channels,
block=Basic,
relu=nn.ReLU(inplace=True),
ratio=0,
layers=6,
vblks=[1, 1, 1, 1, 1, 1],
hblks=[1, 1, 1, 1, 1, 1],
scales=[-1, -1, -1, -1, -1, -1],
factors=[1, 1, 1, 1, 1, 1],
spatial=(64, 64))
def testHyp3D2(self):
net = resunet(1,
1,
spatial=(4, 16, 32),
scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1],
[-1, -1, -1]],
block=HyperBasic2)
net(th.rand(1, 1, 4, 16, 32))
net = resunet(1,
1,
spatial=(4, 16, 32),
scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1],
[-1, -1, -1]],
normalizor='instance',
block=HyperBasic2)
net(th.rand(1, 1, 4, 16, 32))
net = resunet(1,
1,
spatial=(4, 16, 32),
scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1],
[-1, -1, -1]],
normalizor='layer',
block=HyperBasic2)
net(th.rand(1, 1, 4, 16, 32))
net = resunet(1,
1,
spatial=(4, 16, 32),
scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1],
[-1, -1, -1]],
block=HyperBottleneck2)
net(th.rand(1, 1, 4, 16, 32))
net = resunet(1,
1,
spatial=(4, 16, 32),
scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1],
[-1, -1, -1]],
normalizor='instance',
block=HyperBottleneck2)
net(th.rand(1, 1, 4, 16, 32))
net = resunet(1,
1,
spatial=(4, 16, 32),
scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1],
[-1, -1, -1]],
normalizor='layer',
block=HyperBottleneck2)
net(th.rand(1, 1, 4, 16, 32))
def testSE3D(self):
net = resunet(1,
1,
spatial=(4, 16, 32),
scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1],
[-1, -1, -1]],
block=SEBasicBlock)
net(th.rand(1, 1, 4, 16, 32))
net = resunet(1,
1,
spatial=(4, 16, 32),
scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1],
[-1, -1, -1]],
normalizor='instance',
block=SEBasicBlock)
net(th.rand(1, 1, 4, 16, 32))
net = resunet(1,
1,
spatial=(4, 16, 32),
scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1],
[-1, -1, -1]],
normalizor='layer',
block=SEBasicBlock)
net(th.rand(1, 1, 4, 16, 32))
net = resunet(1,
1,
spatial=(4, 16, 32),
scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1],
[-1, -1, -1]],
block=SEBottleneck)
net(th.rand(1, 1, 4, 16, 32))
net = resunet(1,
1,
spatial=(4, 16, 32),
scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1],
[-1, -1, -1]],
normalizor='instance',
block=SEBottleneck)
net(th.rand(1, 1, 4, 16, 32))
net = resunet(1,
1,
spatial=(4, 16, 32),
scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1],
[-1, -1, -1]],
normalizor='layer',
block=SEBottleneck)
net(th.rand(1, 1, 4, 16, 32))
def testHyp2DGroupNorm(self):
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='group',
block=HyperBasic)
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='group',
block=HyperBasic)
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='group',
block=HyperBasic)
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='group',
block=HyperBottleneck)
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='group',
block=HyperBottleneck)
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='group',
block=HyperBottleneck)
net(th.rand(1, 1, 32, 16))
def testHyp2D(self):
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
block=HyperBasic)
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='instance',
block=HyperBasic)
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='layer',
block=HyperBasic)
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
block=HyperBottleneck)
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='instance',
block=HyperBottleneck)
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='layer',
block=HyperBottleneck)
net(th.rand(1, 1, 32, 16))
