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The straight forward TT-decomposition of a full tensor does not work properly for me.
Minimal example:
import tntorch as tn
import torch
import numpy as np
X, Y, Z = np.meshgrid(range(128), range(128), range(128))
full = torch.Tensor(
np.sqrt(np.sqrt(X) * (Y + Z) + Y * Z**2) * (X + np.sin(Y) * np.cos(Z))
) # Some analytical 3D function
print(full.shape)
t = tn.Tensor(full, ranks_tt=3, requires_grad=True) # You can also pass a list of ranks
def metrics():
print(t)
print(
"Compression ratio: {}/{} = {:g}".format(
full.numel(), t.numel(), full.numel() / t.numel()
)
)
print("Relative error:", tn.relative_error(full, t))
print("RMSE:", tn.rmse(full, t))
print("R^2:", tn.r_squared(full, t))
metrics()
Output:
torch.Size([128, 128, 128])
3D TT tensor:
128 128 128
| | |
(0) (1) (2)
/ \ / \ / \
1 3 3 1
Compression ratio: 2097152/2097152.0 = 1
Relative error: tensor(0.0005, grad_fn=<DivBackward0>)
RMSE: tensor(22.0728, grad_fn=<DivBackward0>)
R^2: tensor(1.0000, grad_fn=<RsubBackward1>)
The expected output would be the one given in the tutorial.
Especially, compression ratio should be
I experience this behavior both with python 3.9.6 and 3.12.2 on an M1 MacBook under macOS Sonoma 14.4.1
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