def test1D(self):
net = resnet(1, 1, spatial=(32,))
net(th.rand(1, 1, 32))
net = resnet(1, 1, spatial=(32,), normalizor='instance')
net(th.rand(1, 1, 32))
net = resnet(1, 1, spatial=(32,), normalizor='layer')
net(th.rand(1, 1, 32))
net(th.rand(2, 1, 32))
net = resnet(2, 1, spatial=(32,), ratio=0)
net(th.rand(1, 2, 32))
net(th.rand(2, 2, 32))
def testHyp3D(self):
net = resnet(1, 1, spatial=(4, 16, 32), scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1], [-1, -1, -1]], block=HyperBasic)
net(th.rand(1, 1, 4, 16, 32))
net = resnet(1, 1, spatial=(4, 16, 32), scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1], [-1, -1, -1]], normalizor='instance', block=HyperBasic)
net(th.rand(1, 1, 4, 16, 32))
net = resnet(1, 1, spatial=(4, 16, 32), scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1], [-1, -1, -1]], normalizor='layer', block=HyperBasic)
net(th.rand(1, 1, 4, 16, 32))
net = resnet(1, 1, spatial=(4, 16, 32), scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1], [-1, -1, -1]], block=HyperBottleneck)
net(th.rand(1, 1, 4, 16, 32))
net = resnet(1, 1, spatial=(4, 16, 32), scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1], [-1, -1, -1]], normalizor='instance', block=HyperBottleneck)
net(th.rand(1, 1, 4, 16, 32))
net = resnet(1, 1, spatial=(4, 16, 32), scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1], [-1, -1, -1]], normalizor='layer', block=HyperBottleneck)
net(th.rand(1, 1, 4, 16, 32))
net(th.rand(2, 1, 4, 16, 32))
def testHyp1D(self):
net = resnet(1, 1, spatial=(32,), block=HyperBasic)
net(th.rand(1, 1, 16))
net = resnet(1, 1, spatial=(32,), normalizor='instance', block=HyperBasic)
net(th.rand(1, 1, 16))
net = resnet(1, 1, spatial=(32,), normalizor='layer', block=HyperBasic)
net(th.rand(1, 1, 16))
net = resnet(1, 1, spatial=(32,), block=HyperBottleneck)
net(th.rand(1, 1, 16))
net = resnet(1, 1, spatial=(32,), normalizor='instance', block=HyperBottleneck)
net(th.rand(1, 1, 16))
net = resnet(1, 1, spatial=(32,), normalizor='layer', block=HyperBottleneck)
net(th.rand(1, 1, 16))
net(th.rand(2, 1, 16))
def test3D(self):
resnet(1, 1, spatial=(16, 16, 16))
resnet(1, 1, spatial=(32, 16, 16))
resnet(1, 1, spatial=(16, 32, 16))
resnet(1, 1, spatial=(16, 16, 32))
resnet(1, 1, spatial=(11, 16, 32))
net = resnet(1, 1, spatial=(4, 16, 32), scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1], [-1, -1, -1]])
net(th.rand(1, 1, 4, 16, 32))
net = resnet(1, 1, spatial=(4, 16, 32), scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1], [-1, -1, -1]], normalizor='instance')
net(th.rand(1, 1, 4, 16, 32))
net = resnet(1, 1, spatial=(4, 16, 32), scales=[[0, -1, -1], [-1, -1, -1], [0, -1, -1], [-1, -1, -1]], normalizor='layer')
net(th.rand(1, 1, 4, 16, 32))
net(th.rand(2, 1, 4, 16, 32))