From e49e5173895c41b96e545c2d0b329a5d3a2ba09c Mon Sep 17 00:00:00 2001
From: osimeoni <oriane.simeoni@valeo.com>
Date: Mon, 8 Nov 2021 10:58:06 +0000
Subject: [PATCH] changes of argument names (main_corloc_evaluation.py)
---
README.md | 6 +++---
main_corloc_evaluation.py | 19 +++++++++++--------
2 files changed, 14 insertions(+), 11 deletions(-)
diff --git a/README.md b/README.md
index 64e4ab6..7b69a38 100644
--- a/README.md
+++ b/README.md
@@ -141,9 +141,9 @@ In this work, we additionally use LOST predictions to train object detection mod
The predictions of the class-agnostic Faster R-CNN model trained using LOST boxes as pseudo-gt are stored in the folder `data/CAD_predictions`. In order to launch the corloc evaluation, please launch the following scripts. It is to be noted that in this evaluation, only the box with the highest confidence score is considered per image.
```
-python main_corloc_evaluation.py --dataset VOC07 --set trainval --type_predictions detectron --prediction_file data/CAD_predictions/LOST_plus_CAD_VOC07.json
-python main_corloc_evaluation.py --dataset VOC12 --set trainval --type_predictions detectron --prediction_file data/CAD_predictions/LOST_plus_CAD_VOC07.json
-python main_corloc_evaluation.py --dataset COCO --set train --type_predictions detectron --prediction_file data/CAD_predictions/LOST_plus_CAD_VOC07.json
+python main_corloc_evaluation.py --dataset VOC07 --set trainval --type_pred detectron --pred_file data/CAD_predictions/LOST_plus_CAD_VOC07.json
+python main_corloc_evaluation.py --dataset VOC12 --set trainval --type_pred detectron --pred_file data/CAD_predictions/LOST_plus_CAD_VOC12.json
+python main_corloc_evaluation.py --dataset COCO20k --set train --type_pred detectron --pred_file data/CAD_predictions/LOST_plus_CAD_COCO20k.json
```
The following table presents the obtained corloc results.
diff --git a/main_corloc_evaluation.py b/main_corloc_evaluation.py
index c426216..a4d9fb4 100755
--- a/main_corloc_evaluation.py
+++ b/main_corloc_evaluation.py
@@ -19,14 +19,14 @@ from datasets import Dataset, bbox_iou
if __name__ == "__main__":
parser = argparse.ArgumentParser("Visualize Self-Attention maps")
parser.add_argument(
- "--type_predictions",
+ "--type_pred",
default="boxes_OD",
choices=["boxes_OD", "detectron"],
type=str,
help="Type of predictions will inform on how to load",
)
parser.add_argument(
- "--prediction_file", default="", type=str, help="File location of predictions"
+ "--pred_file", default="", type=str, help="File location of predictions."
)
parser.add_argument(
"--dataset",
@@ -56,11 +56,14 @@ if __name__ == "__main__":
# -------------------------------------------------------------------------------------------------------
# Load predictions
- if args.type_predictions == "boxes_OD":
- with open(args.prediction_file, "rb") as f:
+ if not os.path.exists(args.pred_file):
+ raise ValueError(f"File {args.pred_file} does not exists.")
+
+ if args.type_pred == "boxes_OD":
+ with open(args.pred_file, "rb") as f:
predictions = pickle.load(f)
- elif args.type_predictions == "detectron":
- with open(args.prediction_file, "r") as f:
+ elif args.type_pred == "detectron":
+ with open(args.pred_file, "r") as f:
predictions = json.load(f)
cnt = 0
@@ -87,9 +90,9 @@ if __name__ == "__main__":
if gt_bbxs.shape[0] == 0 and args.no_hard:
continue
- if args.type_predictions == "boxes_OD":
+ if args.type_pred == "boxes_OD":
pred = np.asarray(predictions[im_name])
- elif args.type_predictions == "detectron":
+ elif args.type_pred == "detectron":
name_ind = im_name
if "VOC" in args.dataset:
name_ind = im_name[:-4]
--
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