From 71536f422419ea7d7d118991e3901284a5852730 Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.dos.anjos@gmail.com>
Date: Thu, 19 Oct 2023 09:54:12 +0200
Subject: [PATCH] Documentation fixes
---
doc/references.rst | 7 ++++++-
src/ptbench/engine/saliency/completeness.py | 11 ++++++-----
src/ptbench/scripts/evaluate.py | 2 +-
src/ptbench/scripts/saliency_interpretability.py | 10 +++++-----
4 files changed, 18 insertions(+), 12 deletions(-)
diff --git a/doc/references.rst b/doc/references.rst
index c2712bc6..e40d47e7 100644
--- a/doc/references.rst
+++ b/doc/references.rst
@@ -62,10 +62,15 @@
Recognition, pages 2646–2655.
.. [TBX11K-SIMPLIFIED-2020] *Liu, Y., Wu, Y.-H., Ban, Y., Wang, H., and Cheng, M.-*,
- **Rethinking computer-aided tuberculosis diagnosis.**,
+ **Rethinking computer-aided tuberculosis diagnosis**,
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition, pages 2646–2655.
+.. [GRADCAM-2015] *B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A.
+ Torralba*, **Learning Deep Features for Discriminative Localization**, In
+ 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:
+ https://doi.org/10.1109/CVPR.2016.319.
+
.. [SCORECAM-2020] *H. Wang et al.*, **Score-CAM: Score-Weighted Visual
Explanations for Convolutional Neural Networks** 2020 IEEE/CVF Conference on
Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA,
diff --git a/src/ptbench/engine/saliency/completeness.py b/src/ptbench/engine/saliency/completeness.py
index f26e577f..2d42f21a 100644
--- a/src/ptbench/engine/saliency/completeness.py
+++ b/src/ptbench/engine/saliency/completeness.py
@@ -71,8 +71,8 @@ def _calculate_road_scores(
Returns
-------
A 3-tuple containing floating point numbers representing the
- most-relevant-first score (``morf``), least-relevant-first score
- (``lerf``) and the value (``(lerf-morf)/2``).
+ most-relevant-first average score (``morf``), least-relevant-first
+ average score (``lerf``) and the combined value (``(lerf-morf)/2``).
"""
saliency_map = saliency_map_callable(
input_tensor=images, targets=[ClassifierOutputTarget(output_num)]
@@ -245,10 +245,11 @@ def run(
* The model output number used for the ROAD analysis (0, for binary
classifers as there is typically only one output).
* ``morf``: ROAD most-relevant-first average of percentiles 20, 40, 60 and
- 80.
+ 80 (a.k.a. AOPC-MoRF).
* ``lerf``: ROAD least-relevant-first average of percentiles 20, 40, 60 and
- 80.
- * combined: ROAD combined score by evaluating ``(lerf-morf)/2``.
+ 80 (a.k.a. AOPC-LeRF).
+ * combined: Average ROAD combined score by evaluating ``(lerf-morf)/2``
+ (a.k.a. AOPC-Combined).
"""
from ...models.densenet import Densenet
diff --git a/src/ptbench/scripts/evaluate.py b/src/ptbench/scripts/evaluate.py
index 2e4fe626..72f6b306 100644
--- a/src/ptbench/scripts/evaluate.py
+++ b/src/ptbench/scripts/evaluate.py
@@ -49,7 +49,7 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
"--output-folder",
"-o",
help="Path where to store the analysis result (created if does not exist)",
- required=True,
+ required=False,
default="results",
type=click.Path(file_okay=False, dir_okay=True, path_type=pathlib.Path),
cls=ResourceOption,
diff --git a/src/ptbench/scripts/saliency_interpretability.py b/src/ptbench/scripts/saliency_interpretability.py
index 9281ed82..4d7599a8 100644
--- a/src/ptbench/scripts/saliency_interpretability.py
+++ b/src/ptbench/scripts/saliency_interpretability.py
@@ -40,7 +40,8 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
@click.option(
"--input-folder",
"-i",
- help="""Path where to load saliency maps from.""",
+ help="""Path where to load saliency maps from. You can generate saliency
+ maps with ``ptbench generate-saliencymaps``.""",
required=True,
type=click.Path(
exists=True,
@@ -106,10 +107,9 @@ def saliency_interpretability(
.. important::
The thresholding algorithm used to evaluate IoU and IoDA measures is
- based on the process done by the original CAM paper: "Learning Deep
- Features for Discriminative Localization" by Zhou et al. (2015),
- https://arxiv.org/abs/1512.04150. It keeps all points from the saliency
- map that are above the 20% of its maximum value.
+ based on the process done by the original CAM paper [GRADCAM-2015]_. It
+ keeps all points from the saliency map that are above the 20% of its
+ maximum value.
It then calculates a **single** bounding box for largest connected
component. This bounding box represents detected elements on the
--
GitLab