added scripts for running and updated paths to be better, some paths are still hard coded

README.md

 # LOST 
+# ECE613 README
+Convert KITTI Labels to COCO version
+```
+cd tools/Kitti2Coco
+python kitti2coco-label-trans.py train $KITTI_ROOT
+python kitti2coco-label-trans.py val $KITTI_ROOT
+cd -
+
+pip install -r requirements.txt
+git clone https://github.com/facebookresearch/dino.git
+cd dino; 
+touch __init__.py
+echo -e "import sys\nfrom os.path import dirname, join\nsys.path.insert(0, join(dirname(__file__), '.'))" >> __init__.py;
+cd -
+
+```
+
+
+
+# Original README
 Pytorch implementation of the unsupervised object discovery method **LOST**. More details can be found in the paper:
 
 **Localizing Objects with Self-Supervised Transformers and no Labels**, BMVC 2021 [[arXiv](https://arxiv.org/abs/2109.14279)]  

main_lost.py

@@ -128,7 +128,7 @@ if __name__ == "__main__":
     if args.image_path is not None:
         dataset = ImageDataset(args.image_path)
     elif args.dataset == "KITTI":
-        dataset = ImageFolderDataset("/root/kitti/training/image_2/")
+        dataset = ImageFolderDataset("/root/kitti/training/image_2/") # TODO dont hard code
     else:
         dataset = Dataset(args.dataset, args.set, args.no_hard)
 
@@ -337,8 +337,6 @@ if __name__ == "__main__":
             continue
 
         # Compare prediction to GT boxes
-        ious = []
-            
         ious = bbox_iou(torch.from_numpy(pred), torch.from_numpy(np.asarray(gt_bbxs)))
 
         if torch.any(ious >= 0.50):
@@ -348,7 +346,6 @@ if __name__ == "__main__":
             if i >= 0.50:
                 corloc[im_id] += 1 
 
-
         cnt += len(inp[2])
         
         if cnt % 50 == 0:

scripts/run-single-image.sh

@@ -4,27 +4,24 @@ declare -a images=(
     #"000028" "000048" "000066" "004459" "000435" "003333" "000291"
     "000003"
 )
-DATASET_PATH=/root/kitti/training/image_2/
-DINO_PATH=/root/lost/dino
+
+DATASET_PATH=$KITTI_ROOT/training/image_2/
+LOST_PATH=~/lost
+DINO_PATH=$LOST_PATH/dino
+OUTPUT_PATH=$LOST_PATH/outputs/samples
 
 DINO_ARCH=vit_base
 LOST_FEATURES=k
 K_PATCHES=10
 PATCH_SIZE=16
 
-OUTPUT_PATH=/root/lost/outputs/samples
 rm -rf $OUTPUT_PATH
 
 for i in "${images[@]}"
 do
     echo evaluating $i
-
-    #mkdir -p $OUTPUT_PATH/$i/dino
     mkdir -p $OUTPUT_PATH/$i/lost
-    #mkdir -p $OUTPUT_PATH/$i/lost-dinoseg
-    #mkdir -p $OUTPUT_PATH/$i/images
-
-    cd /root/lost/
+    cd $LOST_PATH
     
     python main_lost.py \
         --image_path $DATASET_PATH/$i.png \
@@ -53,5 +50,4 @@ do
         --visualize seed_expansion \
         --num_init_seeds 1
 
-    echo 
 done
\ No newline at end of file

tools/Kitti2Coco/README.md

@@ -5,4 +5,6 @@ taken from https://github.com/packyan/Kitti2Coco
 modifications done by Spencer Delcore
 
 ## this this a tool to tranfrom these two datasets labels.
-use`kitti2coco-label-trans.py' to tansfrom labels.
+```
+python kitti2coco-label-trans.py train $PATH_TO_KITTI
+```

tools/Kitti2Coco/kitti2coco-label-trans.py

@@ -18,9 +18,13 @@ valid_labels = [
 'Tram',
 'Misc'
 ]
+
+KITTI_FOLDER_PATH = sys.argv[2]
+KITTI_FILE_NAME = sys.argv[1]
+
 #data_set path
-kitti_img_path = '/root/kitti/training/image_2/'
-kitti_label_path = '/root/kitti/training/label_2/'
+kitti_img_path = KITTI_FOLDER_PATH + '/training/image_2/'
+kitti_label_path = KITTI_FOLDER_PATH + '/training/label_2/'
 
 #transformed lables path
 kitti_label_tosave_path = 'labels2coco/'
@@ -34,8 +38,6 @@ kitti_data_real_path = kitti_img_path
 index = 0
 cvfont = cv2.FONT_HERSHEY_SIMPLEX
 
-KITTI_FILE_NAME = sys.argv[1]
-
 if KITTI_FILE_NAME != 'train' or KITTI_FILE_NAME != 'val':
     exit