core.modules.evaluator#
Copyright (c) Meta, Inc. and its affiliates.
This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.
Attributes#
Classes#
Functions#
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Module Contents#
- core.modules.evaluator.NONE#
- class core.modules.evaluator.Evaluator(task: str | None = None, eval_metrics: dict | None = None)#
- task_metrics: ClassVar[dict[str, str]]#
- task_primary_metric: ClassVar[dict[str, str | None]]#
- task#
- target_metrics#
- eval(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], prev_metrics=None)#
- update(key, stat, metrics)#
- core.modules.evaluator.forcesx_mae(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = NONE)#
- core.modules.evaluator.forcesx_mse(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = NONE)#
- core.modules.evaluator.forcesy_mae(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None)#
- core.modules.evaluator.forcesy_mse(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None)#
- core.modules.evaluator.forcesz_mae(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None)#
- core.modules.evaluator.forcesz_mse(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None)#
- core.modules.evaluator.energy_forces_within_threshold(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None) dict[str, float | int] #
- core.modules.evaluator.energy_within_threshold(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None) dict[str, float | int] #
- core.modules.evaluator.average_distance_within_threshold(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None) dict[str, float | int] #
- core.modules.evaluator.min_diff(pred_pos: torch.Tensor, dft_pos: torch.Tensor, cell: torch.Tensor, pbc: torch.Tensor)#
- core.modules.evaluator.cosine_similarity(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = NONE)#
- core.modules.evaluator.mae(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = NONE) dict[str, float | int] #
- core.modules.evaluator.mse(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = NONE) dict[str, float | int] #
- core.modules.evaluator.magnitude_error(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = NONE, p: int = 2) dict[str, float | int] #