| import torch |
| from torch import nn |
| from typing import Any, Iterable, List |
|
|
|
|
| class Model(nn.Module): |
| """ |
| Template model for the leaderboard. |
| |
| Requirements: |
| - Must be instantiable with no arguments (called by the evaluator). |
| - Must implement `predict(batch)` which receives an iterable of inputs and |
| returns a list of predictions (labels). |
| - Must implement `eval()` to place the model in evaluation mode. |
| - If you use PyTorch, submit a state_dict to be loaded via `load_state_dict` |
| """ |
|
|
| def __init__(self, *args, **kwargs) -> None: |
| super().__init__(*args, **kwargs) |
| |
|
|
| def eval(self) -> nn.Module: |
| |
| return self |
|
|
| def predict(self, batch: Iterable[Any]) -> List[Any]: |
| """ |
| Implement your inference here. |
| Inputs: |
| batch: Iterable of preprocessed inputs (as produced by your preprocess.py) |
| Returns: |
| A list of predictions with the same length as `batch`. |
| """ |
| raise NotImplementedError("Implement predict(...) to return a list of labels.") |
|
|
|
|
| def get_model() -> Model: |
| """ |
| Factory function required by the evaluator. |
| Returns an uninitialized model instance. The evaluator may optionally load |
| weights (if provided) before calling predict(...). |
| """ |
| return Model() |
|
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|
