"""High-level function that evaluates trained models."""
from __future__ import annotations
import logging
import pathlib
from .. import models
from ..common import validators
from .frame_classification import eval_frame_classification_model
from .parametric_umap import eval_parametric_umap_model
logger = logging.getLogger(__name__)
[docs]
def eval(
model_config: dict,
dataset_config: dict,
trainer_config: dict,
checkpoint_path: str | pathlib.Path,
output_dir: str | pathlib.Path,
num_workers: int,
labelmap_path: str | pathlib.Path | None = None,
batch_size: int | None = None,
split: str = "test",
spect_scaler_path: str | pathlib.Path = None,
post_tfm_kwargs: dict | None = None,
device: str | None = None,
) -> None:
"""Evaluate a trained model.
Parameters
----------
model_config : dict
Model configuration in a :class:`dict`.
Can be obtained by calling :meth:`vak.config.ModelConfig.asdict`.
dataset_config: dict
Dataset configuration in a :class:`dict`.
Can be obtained by calling :meth:`vak.config.DatasetConfig.asdict`.
trainer_config: dict
Configuration for :class:`lightning.pytorch.Trainer`.
Can be obtained by calling :meth:`vak.config.TrainerConfig.asdict`.
checkpoint_path : str, pathlib.Path
path to directory with checkpoint files saved by Torch, to reload model
output_dir : str, pathlib.Path
Path to location where .csv files with evaluation metrics should be saved.
num_workers : int
Number of processes to use for parallel loading of data.
Argument to torch.DataLoader. Default is 2.
labelmap_path : str, pathlib.Path, optional
Path to 'labelmap.json' file.
Optional, default is None.
batch_size : int, optional.
Number of samples per batch fed into model.
Optional, default is None.
split : str
split of dataset on which model should be evaluated.
One of {'train', 'val', 'test'}. Default is 'test'.
spect_scaler_path : str, pathlib.Path
path to a saved SpectScaler object used to normalize spectrograms.
If spectrograms were normalized and this is not provided, will give
incorrect results.
Default is None.
post_tfm_kwargs : dict
Keyword arguments to post-processing transform.
If None, then no additional clean-up is applied
when transforming labeled timebins to segments,
the default behavior. The transform used is
``vak.transforms.frame_labels.PostProcess`.
Valid keyword argument names are 'majority_vote'
and 'min_segment_dur', and should be appropriate
values for those arguments: Boolean for ``majority_vote``,
a float value for ``min_segment_dur``.
See the docstring of the transform for more details on
these arguments and how they work.
device : str
Device on which to work with model + data.
Defaults to 'cuda' if torch.cuda.is_available is True.
Notes
-----
Note that unlike ``core.predict``, this function
can modify ``labelmap`` so that metrics like edit distance
are correctly computed, by converting any string labels
in ``labelmap`` with multiple characters
to (mock) single-character labels,
with ``vak.labels.multi_char_labels_to_single_char``.
"""
# ---- pre-conditions ----------------------------------------------------------------------------------------------
for path, path_name in zip(
(checkpoint_path, labelmap_path, spect_scaler_path),
("checkpoint_path", "labelmap_path", "spect_scaler_path"),
):
if path is not None: # because `spect_scaler_path` is optional
if not validators.is_a_file(path):
raise FileNotFoundError(
f"value for ``{path_name}`` not recognized as a file: {path}"
)
dataset_path = pathlib.Path(dataset_config["path"])
if not dataset_path.exists() or not dataset_path.is_dir():
raise NotADirectoryError(
f"`dataset_path` not found or not recognized as a directory: {dataset_path}"
)
model_name = model_config["name"]
try:
model_family = models.registry.MODEL_FAMILY_FROM_NAME[model_name]
except KeyError as e:
raise ValueError(
f"No model family found for the model name specified: {model_name}"
) from e
if model_family == "FrameClassificationModel":
eval_frame_classification_model(
model_config=model_config,
dataset_config=dataset_config,
trainer_config=trainer_config,
checkpoint_path=checkpoint_path,
labelmap_path=labelmap_path,
output_dir=output_dir,
num_workers=num_workers,
split=split,
spect_scaler_path=spect_scaler_path,
post_tfm_kwargs=post_tfm_kwargs,
)
elif model_family == "ParametricUMAPModel":
eval_parametric_umap_model(
model_config=model_config,
dataset_config=dataset_config,
trainer_config=trainer_config,
checkpoint_path=checkpoint_path,
output_dir=output_dir,
batch_size=batch_size,
num_workers=num_workers,
split=split,
)
else:
raise ValueError(f"Model family not recognized: {model_family}")