def test_slice_cudaNo0():
gaussian3d = Gaussian([2, 4, 4])
x = gaussian3d(3).cuda(2)
netStructure = [[3, 2, 1, 1], [4, 2, 1, 1], [3, 2, 1, 0], [1, 2, 1, 0]]
sList3d = [
CNN(netStructure),
CNN(netStructure),
CNN(netStructure),
CNN(netStructure)
]
tList3d = [
CNN(netStructure),
CNN(netStructure),
CNN(netStructure),
CNN(netStructure)
]
realNVP = RealNVP([2, 4, 4], sList3d, tList3d, gaussian3d)
realNVP = realNVP.cuda(2)
z = realNVP._generateWithSlice(x, 0, True)
print(realNVP._logProbabilityWithSlice(z, 0))
zz = realNVP._inferenceWithSlice(z, 0, True)
assert_array_almost_equal(x.cpu().data.numpy(), zz.cpu().data.numpy())
assert_array_almost_equal(realNVP._generateLogjac.data.cpu().numpy(),
-realNVP._inferenceLogjac.data.cpu().numpy())
def test_parallel():
gaussian3d = Gaussian([2, 4, 4])
x = 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)
]
realNVP = RealNVP([2, 4, 4], sList3d, tList3d, gaussian3d)
z = realNVP(x)
print(z)
net = torch.nn.DataParallel(realNVP.cuda(0), device_ids=[0, 1])
output = net(x.cuda())
print(output)
assert_array_almost_equal(z.data.numpy(),
output.cpu().data.numpy(),
decimal=5)
def test_tempalte_contractionCNN_checkerboard_cuda():
gaussian3d = Gaussian([2, 4, 4])
x3d = gaussian3d(3).cuda()
netStructure = [[3, 2, 1, 1], [4, 2, 1, 1], [3, 2, 1, 0], [2, 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)
realNVP3d = realNVP3d.cuda()
mask3d = realNVP3d.createMask(["checkerboard"] * 4, ifByte=0, cuda=0)
z3d = realNVP3d._generate(x3d, realNVP3d.mask, realNVP3d.mask_, True)
zp3d = realNVP3d._inference(z3d, realNVP3d.mask, realNVP3d.mask_, True)
print(realNVP3d._logProbability(z3d, realNVP3d.mask, realNVP3d.mask_))
assert_array_almost_equal(x3d.cpu().data.numpy(), zp3d.cpu().data.numpy())
assert_array_almost_equal(realNVP3d._generateLogjac.data.cpu().numpy(),
-realNVP3d._inferenceLogjac.data.cpu().numpy())
def test_contraction_cuda_withDifferentMasks():
gaussian3d = Gaussian([2, 4, 4])
x = gaussian3d(3).cuda()
#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)
]
realNVP = RealNVP([2, 4, 4], sList3d, tList3d, gaussian3d)
realNVP = realNVP.cuda()
mask = realNVP.createMask(
["channel", "checkerboard", "channel", "checkerboard"], 1, cuda=0)
z = realNVP._generateWithContraction(x, realNVP.mask, realNVP.mask_, 2,
True)
print(
realNVP._logProbabilityWithContraction(z, realNVP.mask, realNVP.mask_,
2))
zz = realNVP._inferenceWithContraction(z, realNVP.mask, realNVP.mask_, 2,
True)
assert_array_almost_equal(x.cpu().data.numpy(), zz.cpu().data.numpy())
assert_array_almost_equal(realNVP._generateLogjac.data.cpu().numpy(),
-realNVP._inferenceLogjac.data.cpu().numpy())