DeYOEngine#

class torch_ttt.engine.deyo_engine.DeYOEngine(model: Module, optimization_parameters: Dict[str, Any] | None = None, e_margin: float = 3.4538776394910684, plpd_thresh: float = 0.2, ent_norm: float = 2.763102111592855, patch_len: int = 4, reweight_ent: float = 1.0, reweight_plpd: float = 1.0)[source]#

DeYO: Destroy Your Object – Test-Time Adaptation with PLPD.

DeYO adapts models at test-time by combining entropy minimization with Patch Label Preservation Deviation (PLPD). It filters uncertain and unstable samples using entropy and patch perturbation sensitivity, updating normalization layers with confident ones.

Parameters:

model (torch.nn.Module) – Model to be adapted at test-time.
optimization_parameters (dict, optional) – Optimizer configuration.
e_margin (float) – Entropy threshold for filtering uncertain samples.
plpd_thresh (float) – PLPD threshold to filter unstable predictions.
ent_norm (float) – Normalization constant for entropy weighting.
patch_len (int) – Number of patches per spatial dimension for input shuffling.
reweight_ent (float) – Scaling factor for entropy-based weighting.
reweight_plpd (float) – Scaling factor for PLPD-based weighting.

Example:

from torch_ttt.engine.deyo_engine import DeYOEngine

model = MyModel()
engine = DeYOEngine(model, {"lr": 1e-3})
optimizer = torch.optim.Adam(engine.parameters(), lr=1e-3)

# Training
engine.train()
for inputs, labels in train_loader:
    optimizer.zero_grad()
    outputs, loss_ttt = engine(inputs)
    loss = criterion(outputs, labels) + alpha * loss_ttt
    loss.backward()
    optimizer.step()

# Inference
engine.eval()
for inputs, labels in test_loader:
    output, loss_ttt = engine(inputs)

Reference:

“Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors”, Jonghyun Lee, Dahuin Jung, Saehyung Lee, Junsung Park, Juhyeon Shin, Uiwon Hwang, Sungroh Yoon

Paper link: PDF

ttt_forward(inputs: Tensor) → Tuple[Tensor, Tensor][source]#

Forward pass and loss computation for test-time adaptation.

Selects confident and stable samples using entropy and PLPD filtering. Computes a weighted entropy loss over reliable inputs for adaptation.

Parameters:: inputs (torch.Tensor) – Input tensor.
Returns:: The current model prediction and PLPD-based loss.

MemoEngine

IT3Engine