def test_bijective():
decimal = flow.ScalingNshifting(256, -128)
layerList = []
for i in range(4 * 2):
f = torch.nn.Sequential(torch.nn.Conv2d(9, 9, 3, padding=1),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(9, 9, 1, padding=0),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(9, 3, 3, padding=1))
layerList.append(f)
meanNNlist = []
scaleNNlist = []
meanNNlist.append(
torch.nn.Sequential(torch.nn.Conv2d(3, 9, 3, padding=1),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(9, 9, 1, padding=0),
torch.nn.ReLU(inplace=True)))
scaleNNlist.append(
torch.nn.Sequential(torch.nn.Conv2d(3, 9, 3, padding=1),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(9, 9, 1, padding=0),
torch.nn.ReLU(inplace=True)))
t = flow.SimpleMERA(8, layerList, meanNNlist, scaleNNlist, 2, None, 5,
decimal, utils.roundingWidentityGradient)
samples = torch.randint(0, 255, (100, 3, 8, 8)).float()
zSamples, _ = t.inverse(samples)
rcnSamples, _ = t.forward(zSamples)
prob = t.logProbability(samples)
assert_allclose(samples.detach().numpy(), rcnSamples.detach().numpy())
# Test the depth argument
t = flow.SimpleMERA(8, layerList, meanNNlist, scaleNNlist, 2, 2, 5,
decimal, utils.roundingWidentityGradient)
samples = torch.randint(0, 255, (100, 3, 8, 8)).float()
zSamples, _ = t.inverse(samples)
rcnSamples, _ = t.forward(zSamples)
#prob = t.logProbability(samples)
assert_allclose(samples.detach().numpy(), rcnSamples.detach().numpy())
#scaleNNlist.append(torch.nn.Sequential(torch.nn.Conv2d(3, hchnl, 3, padding=1), torch.nn.ReLU(inplace=True), torch.nn.Conv2d(hchnl, hchnl, 1, padding=0), torch.nn.ReLU(inplace=True), torch.nn.Conv2d(hchnl, 9, 3, padding=1)))
torch.nn.init.zeros_(meanNNlist[-1][-1].weight)
torch.nn.init.zeros_(meanNNlist[-1][-1].bias)
torch.nn.init.zeros_(scaleNNlist[-1][-1].weight)
torch.nn.init.zeros_(scaleNNlist[-1][-1].bias)
else:
meanNNlist = None
scaleNNlist = None
# Building MERA model
f = flow.SimpleMERA(blockLength,
layerList,
meanNNlist,
scaleNNlist,
repeat,
None,
nMixing,
decimal=decimal,
rounding=utils.roundingWidentityGradient,
clamp=clamp,
sameDetail=diffDetail).to(device)
# Define plot function
def plotfn(f, train, test, LOSS, VALLOSS):
# loss plot
lossfig = plt.figure(figsize=(8, 5))
lossax = lossfig.add_subplot(111)
epoch = len(LOSS)
lossax.plot(np.arange(epoch),
torch.nn.init.zeros_(scaleNNlist[-1][-1].weight)
torch.nn.init.zeros_(scaleNNlist[-1][-1].bias)
meanNNlist = meanNNlist * depth
scaleNNlist = scaleNNlist * depth
else:
meanNNlist = None
scaleNNlist = None
# Building MERA model
f = flow.SimpleMERA(blockLength,
layerList,
meanNNlist,
scaleNNlist,
repeat,
None,
nMixing,
decimal=decimal,
rounding=utils.roundingWidentityGradient,
clamp=clamp,
compatible=True).to(device)
# Define plot function
def plotfn(f, train, test, LOSS, VALLOSS):
# loss plot
lossfig = plt.figure(figsize=(8, 5))
lossax = lossfig.add_subplot(111)
epoch = len(LOSS)
lossax.plot(np.arange(epoch),
def test_saveload():
decimal = flow.ScalingNshifting(256, -128)
layerList = []
for i in range(4):
f = torch.nn.Sequential(torch.nn.Conv2d(9, 9, 3, padding=1),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(9, 9, 1, padding=0),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(9, 3, 3, padding=1))
layerList.append(f)
meanNNlist = []
scaleNNlist = []
meanNNlist.append(
torch.nn.Sequential(torch.nn.Conv2d(3, 9, 3, padding=1),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(9, 9, 1, padding=0),
torch.nn.ReLU(inplace=True)))
scaleNNlist.append(
torch.nn.Sequential(torch.nn.Conv2d(3, 9, 3, padding=1),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(9, 9, 1, padding=0),
torch.nn.ReLU(inplace=True)))
t = flow.SimpleMERA(8, layerList, meanNNlist, scaleNNlist, 1, None, 5,
decimal, utils.roundingWidentityGradient)
decimal = flow.ScalingNshifting(256, -128)
layerList = []
for i in range(4):
f = torch.nn.Sequential(torch.nn.Conv2d(9, 9, 3, padding=1),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(9, 9, 1, padding=0),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(9, 3, 3, padding=1))
layerList.append(f)
meanNNlist = []
scaleNNlist = []
meanNNlist.append(
torch.nn.Sequential(torch.nn.Conv2d(3, 9, 3, padding=1),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(9, 9, 1, padding=0),
torch.nn.ReLU(inplace=True)))
scaleNNlist.append(
torch.nn.Sequential(torch.nn.Conv2d(3, 9, 3, padding=1),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(9, 9, 1, padding=0),
torch.nn.ReLU(inplace=True)))
tt = flow.SimpleMERA(8, layerList, meanNNlist, scaleNNlist, 1, None, 5,
decimal, utils.roundingWidentityGradient)
samples = torch.randint(0, 255, (100, 3, 8, 8)).float()
torch.save(t.save(), "testsaving.saving")
tt.load(torch.load("testsaving.saving"))
tzSamples, _ = t.inverse(samples)
ttzSamples, _ = tt.inverse(samples)
rcnSamples, _ = t.forward(tzSamples)
ttrcnSamples, _ = tt.forward(ttzSamples)
assert_allclose(tzSamples.detach().numpy(), ttzSamples.detach().numpy())
assert_allclose(samples.detach().numpy(), rcnSamples.detach().numpy())
assert_allclose(rcnSamples.detach().numpy(), ttrcnSamples.detach().numpy())
prior = f.prior
prior.depth = int(math.log(targetSize[-1], 2))
if 'simplePrior_False' in name:
pass
else:
prior.priorList = torch.nn.ModuleList([
prior.priorList[0]
for _ in range(int(math.log(targetSize[-1], 2)) - 1)
] + [prior.priorList[-1]])
# Building MERA mode
if 'easyMera' in name:
f = flow.SimpleMERA(
blockLength,
layerList,
meanNNlist,
scaleNNlist,
repeat,
1,
nMixing,
decimal=decimal,
rounding=utils.roundingWidentityGradient).to(device)
elif '1to2Mera' in name:
f = flow.OneToTwoMERA(
blockLength,
layerList,
meanNNlist,
scaleNNlist,
repeat,
1,
nMixing,
decimal=decimal,
rounding=utils.roundingWidentityGradient).to(device)