def test1D(self):
net = resunet3(1, 1, spatial=(32, ))
net(th.rand(1, 1, 16))
net = resunet3(1, 1, spatial=(32, ), normalizor='instance')
net(th.rand(1, 1, 16))
net = resunet3(1, 1, spatial=(32, ), normalizor='layer')
net(th.rand(1, 1, 16))
def testHyp1D(self):
net = resunet3(1, 1, spatial=(32, ), block=HyperBasic)
net(th.rand(1, 1, 16))
net = resunet3(1,
1,
spatial=(32, ),
normalizor='instance',
block=HyperBasic)
net(th.rand(1, 1, 16))
net = resunet3(1,
1,
spatial=(32, ),
normalizor='layer',
block=HyperBasic)
net(th.rand(1, 1, 16))
net = resunet3(1, 1, spatial=(32, ), block=HyperBottleneck)
net(th.rand(1, 1, 16))
net = resunet3(1,
1,
spatial=(32, ),
normalizor='instance',
block=HyperBottleneck)
net(th.rand(1, 1, 16))
net = resunet3(1,
1,
spatial=(32, ),
normalizor='layer',
block=HyperBottleneck)
net(th.rand(1, 1, 16))
def test2D(self):
resunet3(1, 1, spatial=(16, 16))
resunet3(1, 1, spatial=(16, 32))
resunet3(1, 1, spatial=(32, 16))
net = resunet3(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]])
net(th.rand(1, 1, 32, 16))
net = resunet3(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='instance')
net(th.rand(1, 1, 32, 16))
net = resunet3(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
normalizor='layer')
net(th.rand(1, 1, 32, 16))
def testHyp3D2(self):
net = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(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 = resunet3(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
block=HyperBasic)
net(th.rand(1, 1, 32, 16))
net = resunet3(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 = resunet3(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 = resunet3(1,
1,
spatial=(32, 16),
scales=[[0, -1], [0, -1], [0, -1], [0, -1]],
block=HyperBottleneck)
net(th.rand(1, 1, 32, 16))
net = resunet3(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 = resunet3(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))
