def test_checkerboard_cuda_cudaNot0():
gaussian3d = Gaussian([2, 4, 4])
x3d = gaussian3d(3).cuda(maxGPU // 2)
netStructure = [[3, 2, 1, 1], [4, 2, 1, 1], [3, 2, 1, 0], [1, 2, 1, 0]]
sList3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
tList3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
realNVP3d = RealNVP([2, 4, 4], sList3d, tList3d,
gaussian3d).cuda(maxGPU // 2)
mask3d = realNVP3d.createMask(["checkerboard"] * 4, cuda=maxGPU // 2)
z3d = realNVP3d.generate(x3d)
zp3d = realNVP3d.inference(z3d)
print(realNVP3d.logProbability(z3d))
assert_array_almost_equal(x3d.cpu().data.numpy(), zp3d.cpu().data.numpy())
def test_sample():
gaussian3d = Gaussian([2, 4, 4])
x3d = gaussian3d(3)
netStructure = [[3, 2, 1, 1], [4, 2, 1, 1], [3, 2, 1, 0], [1, 2, 1, 0]]
sList3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
tList3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
realNVP3d = RealNVP([2, 4, 4], sList3d, tList3d, gaussian3d,
"checkerboard")
z3d = realNVP3d.sample(100, True)
zp3d = realNVP3d.sample(100, False)
print(realNVP3d.logProbability(z3d))
def test_3d():
gaussian3d = Gaussian([2, 4, 4])
x3d = gaussian3d(3)
#z3dp = z3d[:,0,:,:].view(10,-1,4,4)
#print(z3dp)
#print(x)
netStructure = [[3, 2, 1, 1], [4, 2, 1, 1], [3, 2, 1, 0],
[1, 2, 1, 0]] # [channel, filter_size, stride, padding]
sList3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
tList3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
realNVP3d = RealNVP([2, 4, 4], sList3d, tList3d,
gaussian3d) #,maskType = "checkerboard")
print(realNVP3d.mask)
#mask3d = realNVP3d.createMask()
assert realNVP3d.mask.shape[0] == 4
assert realNVP3d.mask.shape[1] == 2
assert realNVP3d.mask.shape[2] == 4
assert realNVP3d.mask.shape[3] == 4
print("test high dims")
print("Testing 3d")
print("3d original:")
#print(x3d)
z3d = realNVP3d.generate(x3d)
print("3d forward:")
#print(z3d)
zp3d = realNVP3d.inference(z3d)
print("Backward")
#print(zp3d)
print("3d logProbability")
print(realNVP3d.logProbability(z3d))
saveDict3d = realNVP3d.saveModel({})
torch.save(saveDict3d, './saveNet3d.testSave')
# realNVP.loadModel({})
sListp3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
tListp3d = [
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2),
CNN(netStructure, inchannel=2)
]
realNVPp3d = RealNVP([2, 4, 4], sListp3d, tListp3d, gaussian3d)
saveDictp3d = torch.load('./saveNet3d.testSave')
realNVPp3d.loadModel(saveDictp3d)
zz3d = realNVPp3d.generate(x3d)
print("3d Forward after restore")
#print(zz3d)
assert_array_almost_equal(x3d.data.numpy(), zp3d.data.numpy())
assert_array_almost_equal(zz3d.data.numpy(), z3d.data.numpy())
