def __init__(self):
super().__init__()
self.relu = nn.ReLU()
self.enc = resunet(10,
62,
block=HyperBottleneck,
relu=nn.ReLU(inplace=True),
layers=6,
ratio=-2,
vblks=[1, 1, 1, 1, 1, 1],
hblks=[3, 3, 3, 3, 3, 3],
scales=[-1, -1, -1, -1, -1, -1],
factors=[1, 1, 1, 1, 1, 1],
spatial=(64, 64))
self.dec = resunet(10,
10,
block=HyperBottleneck,
relu=CappingRelu(),
layers=6,
ratio=-3,
vblks=[1, 1, 1, 1, 1, 1],
hblks=[0, 0, 0, 0, 0, 0],
scales=[-1, -1, -1, -1, -1, -1],
factors=[1, 1, 1, 1, 1, 1],
spatial=(64, 64),
final_normalized=True)
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 testCmplxHyp1D2(self):
net = resunet(1,
1,
spatial=(32, ),
block=CmplxHyperBasic2,
ratio=2,
complex=True)
net(th.rand(1, 1, 16, dtype=th.cfloat).view(1, 1, 16))
net = resunet(1,
1,
spatial=(32, ),
block=CmplxHyperBottleneck2,
ratio=2,
complex=True)
net(th.rand(1, 1, 16, dtype=th.cfloat).view(1, 1, 16))
def testCmplxHyp2D2(self):
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
block=CmplxHyperBasic2,
ratio=2,
complex=True)
net(th.rand(1, 1, 32, 16, dtype=th.cfloat).view(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
block=CmplxHyperBottleneck2,
ratio=2,
complex=True)
net(th.rand(1, 1, 32, 16, dtype=th.cfloat).view(1, 1, 32, 16))
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):
super().__init__()
self.relu = nn.ReLU(inplace=True)
self.relu6 = nn.ReLU6(inplace=True)
self.oconv = nn.Conv2d(20, 10, kernel_size=3, padding=1)
self.dropout = nn.Dropout2d(p=0.5)
self.enc = resunet(10,
160,
block=HyperBottleneck,
layers=6,
ratio=-1,
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 testWP2D(self):
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
block=WarpBasicBlock,
attn=SELayer)
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=WarpBasicBlock,
attn=WarpLayer)
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=WarpBasicBlock,
attn=SELayer)
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
block=WarpBottleneck,
attn=SELayer)
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=WarpBottleneck,
attn=WarpLayer)
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=WarpBottleneck,
attn=SELayer)
net(th.rand(1, 1, 32, 16))
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 testCmplxHyp2D(self):
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
block=CmplxHyperBasic)
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=CmplxHyperBasic)
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=CmplxHyperBasic)
net(th.rand(1, 1, 32, 16))
net = resunet(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
block=CmplxHyperBottleneck)
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=CmplxHyperBottleneck)
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=CmplxHyperBottleneck)
net(th.rand(1, 1, 32, 16))