From 257f53a8aa5a8eb38dd0e8d7bc176f0ff1852d2d Mon Sep 17 00:00:00 2001
From: dcarron <daniel.carron@idiap.ch>
Date: Mon, 29 Jan 2024 12:03:44 +0100
Subject: [PATCH] [doc] Doc fixes
---
src/mednet/config/data/montgomery/datamodule.py | 1 -
src/mednet/engine/saliency/completeness.py | 10 ++++++++++
src/mednet/engine/saliency/generator.py | 14 +++++++++++++-
3 files changed, 23 insertions(+), 2 deletions(-)
diff --git a/src/mednet/config/data/montgomery/datamodule.py b/src/mednet/config/data/montgomery/datamodule.py
index edb2cd71..e2454827 100644
--- a/src/mednet/config/data/montgomery/datamodule.py
+++ b/src/mednet/config/data/montgomery/datamodule.py
@@ -48,7 +48,6 @@ class RawDataLoader(_BaseRawDataLoader):
where to find the image to be loaded, and an integer, representing the
sample label.
-
Returns
-------
The sample representation.
diff --git a/src/mednet/engine/saliency/completeness.py b/src/mednet/engine/saliency/completeness.py
index c9a961f7..6d9cf7b2 100644
--- a/src/mednet/engine/saliency/completeness.py
+++ b/src/mednet/engine/saliency/completeness.py
@@ -68,6 +68,7 @@ def _calculate_road_scores(
Returns
-------
+ tuple[float, float, float]
A 3-tuple containing floating point numbers representing the
most-relevant-first average score (``morf``), least-relevant-first
average score (``lerf``) and the combined value (``(lerf-morf)/2``).
@@ -143,6 +144,15 @@ def _process_sample(
A sequence of percentiles (percent x100) integer values indicating the
proportion of pixels to perturb in the original image to calculate both
MoRF and LeRF scores.
+
+ Returns
+ -------
+ list
+ A list containing the following items for a particular sample:
+ * The relative path to the sample.
+ * The label.
+ * An index to the specified target_class.
+ * The computed ROAD scores.
"""
name: str = sample[1]["name"][0]
diff --git a/src/mednet/engine/saliency/generator.py b/src/mednet/engine/saliency/generator.py
index 72b53d51..6ce13812 100644
--- a/src/mednet/engine/saliency/generator.py
+++ b/src/mednet/engine/saliency/generator.py
@@ -24,7 +24,19 @@ def _create_saliency_map_callable(
target_layers: list[torch.nn.Module] | None,
use_cuda: bool,
):
- """Creates a class activation map (CAM) instance for a given model."""
+ """Creates a class activation map (CAM) instance for a given model.
+
+ Parameters
+ ----------
+ algo_type
+ The algorithm to use for saliency map estimation.
+ model
+ Neural network model (e.g. pasa).
+ target_layers
+ The target layers to compute CAM for.
+ use_cuda
+ Whether to use cuda or not.
+ """
import pytorch_grad_cam
--
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