diff --git a/src/ptbench/engine/predictor.py b/src/ptbench/engine/predictor.py
index 033e0829e2b060ec0351feb7f63adb9e0f690c35..49e5f5acdbd1866e3cd10b323720d2eda3f2d0c2 100644
--- a/src/ptbench/engine/predictor.py
+++ b/src/ptbench/engine/predictor.py
@@ -3,11 +3,11 @@
# SPDX-License-Identifier: GPL-3.0-or-later
import logging
-import pathlib
import lightning.pytorch
import torch.utils.data
+from ..models.typing import Prediction
from .device import DeviceManager
logger = logging.getLogger(__name__)
@@ -17,8 +17,12 @@ def run(
model: lightning.pytorch.LightningModule,
datamodule: lightning.pytorch.LightningDataModule,
device_manager: DeviceManager,
- output_folder: pathlib.Path,
-) -> list | list[list] | dict[str, list] | None:
+) -> (
+ list[Prediction]
+ | list[list[Prediction]]
+ | dict[str, list[Prediction]]
+ | None
+):
"""Runs inference on input data, outputs csv files with predictions.
Parameters
@@ -31,8 +35,6 @@ def run(
An internal device representation, to be used for training and
validation. This representation can be converted into a pytorch device
or a torch lightning accelerator setup.
- output_folder
- Directory in which the logs will be saved.
Returns
@@ -58,24 +60,13 @@ def run(
of the types described above.
"""
- from .loggers import CustomTensorboardLogger
-
- log_dir = "logs"
- tensorboard_logger = CustomTensorboardLogger(
- output_folder,
- log_dir,
- )
- # logger.info(
- # f"Monitor prediction with `tensorboard serve "
- # f"--logdir={output_folder}/{log_dir}/`. "
- # f"Then, open a browser on the printed address."
- # )
+ from lightning.pytorch.loggers.logger import DummyLogger
accelerator, devices = device_manager.lightning_accelerator()
trainer = lightning.pytorch.Trainer(
accelerator=accelerator,
devices=devices,
- logger=tensorboard_logger,
+ logger=DummyLogger(),
)
def _flatten(p: list[list]):
diff --git a/src/ptbench/models/separate.py b/src/ptbench/models/separate.py
index 522d5b94d2313a34c23cb6322c1d986dc3631217..febb0728e3db919b119ca76402dccc516ba41306 100644
--- a/src/ptbench/models/separate.py
+++ b/src/ptbench/models/separate.py
@@ -3,12 +3,32 @@
# SPDX-License-Identifier: GPL-3.0-or-later
"""Contains the inverse :py:func:`torch.utils.data.default_collate`."""
+import typing
+
import torch
from ..data.typing import Sample
+from .typing import Prediction
+
+
+def _as_predictions(samples: typing.Iterable[Sample]) -> list[Prediction]:
+ """Takes a list of separated batch predictions and transform into a list of
+ formal predictions.
+ Parameters
+ ----------
+ samples
+ A sequence of samples as returned by :py:func:`separate`.
+
+
+ Returns
+ -------
+ A list of typed predictions that can be saved to disk.
+ """
+ return [(v[1]["name"], v[1]["label"].item(), v[0].item()) for v in samples]
-def separate(batch: Sample) -> list[Sample]:
+
+def separate(batch: Sample) -> list[Prediction]:
"""Separates a collated batch reconstituting its samples.
This function implements the inverse of
@@ -19,6 +39,18 @@ def separate(batch: Sample) -> list[Sample]:
* :class:`torch.Tensor` -> :class:`torch.Tensor` (with a removed outer
dimension, via :py:func:`torch.flatten`)
* ``typing.Mapping[K, V[]]`` -> ``[dict[K, V_1], dict[K, V_2], ...]``
+
+
+ Parameters
+ ----------
+ batch
+ A batch, as output by torch model forwarding
+
+
+ Returns
+ -------
+ A list of predictions that contains the predictions and associated metadata
+ for each processed sample.
"""
# as of now, this is really simple - to be made more complex upon need.
@@ -26,4 +58,4 @@ def separate(batch: Sample) -> list[Sample]:
{key: value[i] for key, value in batch[1].items()}
for i in range(len(batch[0]))
]
- return list(zip(torch.flatten(batch[0]), metadata))
+ return _as_predictions(zip(torch.flatten(batch[0]), metadata))
diff --git a/src/ptbench/models/typing.py b/src/ptbench/models/typing.py
index 76c02c6d81b602472e3126a6698d2028b83dba53..3294cafdddc0d2415b787f8c1eb494ec31bdfdd6 100644
--- a/src/ptbench/models/typing.py
+++ b/src/ptbench/models/typing.py
@@ -5,5 +5,17 @@
import typing
-Checkpoint: typing.TypeAlias = dict[str, typing.Any]
+Checkpoint: typing.TypeAlias = typing.Mapping[str, typing.Any]
"""Definition of a lightning checkpoint."""
+
+
+Prediction: typing.TypeAlias = tuple[
+ str, int | typing.Sequence[int], float | typing.Sequence[float]
+]
+"""Prediction: the sample name, the target, and the predicted value."""
+
+
+PredictionSplit: typing.TypeAlias = typing.Mapping[
+ str, typing.Sequence[Prediction]
+]
+"""A series of predictions for different database splits."""
diff --git a/src/ptbench/scripts/predict.py b/src/ptbench/scripts/predict.py
index c13a4f242b783335f85072d88a6e951fb988a398..0b54d05146cbbb6fceffd5e58f6928e0580cb1d5 100644
--- a/src/ptbench/scripts/predict.py
+++ b/src/ptbench/scripts/predict.py
@@ -135,7 +135,7 @@ def predict(
logger.info(f"Loading checkpoint from `{weight}`...")
model = model.load_from_checkpoint(weight, strict=False)
- predictions = run(model, datamodule, DeviceManager(device), output.parent)
+ predictions = run(model, datamodule, DeviceManager(device))
output.parent.mkdir(parents=True, exist_ok=True)
if output.exists():
@@ -147,16 +147,5 @@ def predict(
shutil.copy(output, backup)
with output.open("w") as f:
- flat_predictions: dict[str, list[list]] = {}
- # creates a flat representation of predictions that is similar to our
- # own JSON split files
- for split_name, split_values in predictions.items(): # type: ignore
- flat_predictions.setdefault(
- split_name,
- [
- [v[1]["name"], v[1]["label"].item(), v[0].item()]
- for v in split_values
- ],
- )
- json.dump(flat_predictions, f, indent=2)
+ json.dump(predictions, f, indent=2)
logger.info(f"Predictions saved to `{str(output)}`")