update

custom_datasets/custom_caption.py

@@ -49,7 +49,7 @@ class Caption_set(torch.utils.data.Dataset):
 
 
 class HRS_caption(Caption_set):
-    def __init__(self, prompts_path="/vision-nfs/torralba/projects/jomat/hui/stable_diffusion/clip_dissection/Style_captions/andre-derain_subset1/style_captions.csv", transform=None, delimiter=','):
+    def __init__(self, prompts_path="/data/vision/torralba/selfmanaged/torralba/projects/jomat/hui/stable_diffusion/clip_dissection/Style_captions/andre-derain_subset1/style_captions.csv", transform=None, delimiter=','):
         self.prompts = pd.read_csv(prompts_path, delimiter=delimiter)
         self.transform = transform
         self.caption_key = "original_prompts"
@@ -65,7 +65,7 @@ class HRS_caption(Caption_set):
         return ret
 
 class Laion_pop(torch.utils.data.Dataset):
-    def __init__(self, anno_file="/vision-nfs/torralba/projects/jomat/hui/stable_diffusion/custom_datasets/laion_pop500.csv",image_root="/vision-nfs/torralba/scratch/jomat/sam_dataset/laion_pop",transform=None):
+    def __init__(self, anno_file="/data/vision/torralba/selfmanaged/torralba/projects/jomat/hui/stable_diffusion/custom_datasets/laion_pop500.csv",image_root="/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/laion_pop",transform=None):
         self.transform = transform
         self.info = pd.read_csv(anno_file, delimiter=";")
         self.caption_key = "caption"

custom_datasets/filt/coco/filt.py

@@ -109,10 +109,10 @@ def main(args):
         filter.clip_filter = None
         torch.cuda.empty_cache()
 
-    # caption_folder_path = "/vision-nfs/torralba/scratch/jomat/sam_dataset/PixArt-alpha/captions"
-    # image_folder_path = "/vision-nfs/torralba/scratch/jomat/sam_dataset/images"
-    # id_dict_dir = "/vision-nfs/torralba/scratch/jomat/sam_dataset/images/id_dict"
-    # filt_dir = "/vision-nfs/torralba/scratch/jomat/sam_dataset/filt_result"
+    # caption_folder_path = "/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/PixArt-alpha/captions"
+    # image_folder_path = "/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/images"
+    # id_dict_dir = "/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/images/id_dict"
+    # filt_dir = "/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/filt_result"
 
     def collate_fn(examples):
         # {"image": image, "id":id}
@@ -142,7 +142,7 @@ def main(args):
 
 
 
-    save_root = f"/vision-nfs/torralba/scratch/jomat/sam_dataset/coco/filt/{args.split}"
+    save_root = f"/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/coco/filt/{args.split}"
     os.makedirs(save_root, exist_ok=True)
 
     if args.mode == "clip_feat":

custom_datasets/filt/sam_filt.py

@@ -31,9 +31,9 @@ def main(args):
         torch.cuda.empty_cache()
 
     caption_folder_path = "/afs/csail.mit.edu/u/h/huiren/code/diffusion/stable_diffusion/clip_dissection/SAM/subset/captions"
-    image_folder_path = "/vision-nfs/torralba/scratch/jomat/sam_dataset/nfs-data/sam/images"
-    id_dict_dir = "/vision-nfs/torralba/scratch/jomat/sam_dataset/sam_ids/8.16/id_dict"
-    filt_dir = "/vision-nfs/torralba/scratch/jomat/sam_dataset/filt_result"
+    image_folder_path = "/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/nfs-data/sam/images"
+    id_dict_dir = "/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/sam_ids/8.16/id_dict"
+    filt_dir = "/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/filt_result"
     def collate_fn(examples):
         # {"image": image, "id":id}
         ret = {}
@@ -71,7 +71,7 @@ def main(args):
                 id_dict = pickle.load(f)
             ids = list(id_dict.keys())
             dataset = SamDataset(image_folder_path, caption_folder_path, id_file=ids, id_dict_file=id_dict_file)
-            # dataset = SamDataset(image_folder_path, caption_folder_path, id_file=[10061410, 10076945, 10310013,1042012, 4487809, 4541052], id_dict_file="/vision-nfs/torralba/scratch/jomat/sam_dataset/images/id_dict/all_id_dict.pickle")
+            # dataset = SamDataset(image_folder_path, caption_folder_path, id_file=[10061410, 10076945, 10310013,1042012, 4487809, 4541052], id_dict_file="/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/images/id_dict/all_id_dict.pickle")
             dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=False, num_workers=8, collate_fn=collate_fn)
             clip_logits = None
             clip_logits_file = os.path.join(save_dir, "clip_logits_result.pickle")

custom_datasets/mypath.py

@@ -5,7 +5,7 @@ class MyPath(object):
     @staticmethod
     def db_root_dir(database=''):
         coco_root = "/data/vision/torralba/datasets/coco_2017"
-        sam_caption_root = "/vision-nfs/torralba/datasets/vision/sam/captions"
+        sam_caption_root = "/data/vision/torralba/selfmanaged/torralba/datasets/vision/sam/captions"
 
         root_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
         map={
@@ -13,7 +13,7 @@ class MyPath(object):
             "coco_caption_train": f"{coco_root}/annotations/captions_train2017.json",
             "coco_val": f"{coco_root}/val2017/",
             "coco_caption_val": f"{coco_root}/annotations/captions_val2017.json",
-            "sam_images": "/vision-nfs/torralba/datasets/vision/sam/images",
+            "sam_images": "/data/vision/torralba/selfmanaged/torralba/datasets/vision/sam/images",
             "sam_captions": sam_caption_root,
             "sam_whole_filtered_ids_train": "data/filtered_sam/all_remain_ids_train.pickle",
             "sam_whole_filtered_ids_val": "data/filtered_sam/all_remain_ids_val.pickle",

utils/art_filter.py

@@ -202,7 +202,7 @@ class Art_filter:
 
 if __name__ == "__main__":
     import pickle
-    with open("/vision-nfs/torralba/scratch/jomat/sam_dataset/filt_result/sa_000000/clip_logits_result.pickle","rb") as f:
+    with open("/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/filt_result/sa_000000/clip_logits_result.pickle","rb") as f:
         result=pickle.load(f)
     feat = result['clip_features']
     logits =Art_filter().clip_logit_by_feat(feat)