| # SPDX-FileCopyrightText: 2025 Stanford University, ETH Zurich, and the project authors (see CONTRIBUTORS.md) | |
| # SPDX-FileCopyrightText: 2025 This source file is part of the OpenTSLM open-source project. | |
| # | |
| # SPDX-License-Identifier: MIT | |
| from abc import abstractmethod | |
| import torch | |
| import torch.nn as nn | |
| from opentslm.model_config import TRANSFORMER_INPUT_DIM, PATCH_SIZE | |
| class TimeSeriesDecoderBase(nn.Module): | |
| """ | |
| Base class for time series decoders. | |
| Decoders map from latent representations back to time series space. | |
| """ | |
| def __init__( | |
| self, | |
| input_dim: int = TRANSFORMER_INPUT_DIM, | |
| patch_size: int = PATCH_SIZE, | |
| dropout: float = 0.0, | |
| ): | |
| """ | |
| Args: | |
| input_dim: Dimension of input latent representation | |
| patch_size: Size of time series patches to reconstruct | |
| dropout: Dropout probability | |
| """ | |
| super().__init__() | |
| self.input_dim = input_dim | |
| self.patch_size = patch_size | |
| self.dropout = dropout | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| """ | |
| Decode latent representation to time series. | |
| Args: | |
| x: Latent representation from encoder | |
| Returns: | |
| Reconstructed time series | |
| """ | |
| pass |