a

dataset/make_nuswide.py

@@ -1,107 +0,0 @@
-import os
-import scipy.io as scio
-import numpy as np
-
-# mkdir mat
-# mv make_nuswide.py mat
-# python make_nuswide.py
-root_dir = "PATH/TO/YOUR/DOWNLOAD/DIR/"
-
-
-imageListFile = os.path.join(root_dir, "/Low-Level-Features/ImageList/Imagelist.txt")
-labelPath = os.path.join(root_dir, "/nuswide/Groundtruth/AllLabels")
-textFile = os.path.join(root_dir, "/Low-Level-Features/NUS_WID_Tags/All_Tags.txt")
-classIndexFile = os.path.join(root_dir, "/Low-Level-Features/Concepts81.txt")
-
-# you can use the image urls to download images
-imagePath = os.path.join(root_dir, "nuswide/Flickr")
-
-with open(imageListFile, "r") as f:
-    indexs = f.readlines()
-
-indexs = [os.path.join(imagePath, item.strip().replace("\\", "/")) for item in indexs]
-print("indexs length:", len(indexs))
-
-#class_index = {}
-#with open(classIndexFile, "r") as f:
-#    data = f.readlines()
-#
-#for i, item in enumerate(data):
-#    class_index.update({item.strip(): i})
-
-captions = []
-with open(textFile, "r") as f:
-    for line in f:
-        if len(line.strip()) == 0:
-            print("some line empty!")
-            continue
-        caption = line.split()[1:]
-        caption = " ".join(caption).strip()
-        if len(caption) == 0:
-             caption = "123456"
-        captions.append(caption)
-
-print("captions length:", len(captions))
-
-#labels = np.zeros([len(indexs), len(class_index)], dtype=np.int8)
-# label_lists = os.listdir(labelPath)
-with open(os.path.join(root_dir, "/nuswide/Groundtruth/used_label.txt")) as f:
-    label_lists = f.readlines()
-label_lists = [item.strip() for item in label_lists]
-
-class_index = {}
-for i, item in enumerate(label_lists):
-    class_index.update({item: i})
-
-labels = np.zeros([len(indexs), len(class_index)], dtype=np.int8)
-
-for item in label_lists:
-    path = os.path.join(labelPath, item)
-    class_label = item# .split(".")[0].split("_")[-1]
-
-    with open(path, "r") as f:
-        data = f.readlines()
-    for i, val in enumerate(data):
-        labels[i][class_index[class_label]] = 1 if val.strip() == "1" else 0
-print("labels sum:", labels.sum())
-
-not_used_id = []
-with open(os.path.join(root_dir, "/nuswide/Groundtruth/not_used_id.txt")) as f:
-    not_used_id = f.readlines()
-not_used_id = [int(item.strip()) for item in not_used_id]
-
-# for item in not_used_id:
-#     indexs.pop(item)
-#     captions.pop(item)
-#     labels = np.delete(labels, item, 0)
-ind = list(range(len(indexs)))
-for item in not_used_id:
-    ind.remove(item)
-    indexs[item] = ""
-    captions[item] = ""
-indexs = [item for item in indexs if item != ""]
-captions = [item for item in captions if item != ""]
-ind = np.asarray(ind)
-labels = labels[ind]
-# ind = range(len(indexs))
-
-print("indexs length:", len(indexs))
-print("captions length:", len(captions))
-print("labels shape:", labels.shape)
-
-indexs = {"index": indexs}
-captions = {"caption": captions}
-labels = {"category": labels}
-
-scio.savemat(os.path.join(root_dir, "/mat/index.mat"), indexs)
-# scio.savemat("caption.mat", captions)
-scio.savemat(os.path.join(root_dir, "/mat/label.mat"), labels)
-
-
-captions = [item + "\n" for item in captions["caption"]]
-
-with open(os.path.join(root_dir, "/mat/caption.txt"), "w") as f:
-    f.writelines(captions)
-
-print("finished!")
-

main.py

@@ -1,11 +1,11 @@
-# from train.text_train import Trainer
-from train.hash_train import Trainer
+from train.text_train import Trainer
+# from train.hash_train import Trainer
 
 if __name__ == "__main__":
 
     engine=Trainer()
     engine.test()
-    # engine.train_epoch()
+    engine.train_epoch()
     
     
     # engine.train()

run.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+set -e
+
+
+export https_proxy=http://127.0.0.1:7897 http_proxy=http://127.0.0.1:7897 all_proxy=socks5://127.0.0.1:7897
+# CUDA_VISIBLE_DEVICES=0 python main.py --method CSQ --bit 32
+# CUDA_VISIBLE_DEVICES=0 python main.py --method CSQ --bit 64
+CUDA_VISIBLE_DEVICES=0 python main.py --victim ViT-B/16 --output-dim 512
+CUDA_VISIBLE_DEVICES=0 python main.py --victim ViT-B/32 --output-dim 512
+CUDA_VISIBLE_DEVICES=0 python main.py --victim RN101 --output-dim 512

utils/get_args.py

@@ -15,12 +15,12 @@ def get_args():
     parser.add_argument("--label-file", type=str, default="label.mat")
     parser.add_argument("--similarity-function", type=str, default="euclidean", help="choise form [cosine, euclidean]")
     parser.add_argument("--loss-type", type=str, default="l2", help="choise form [l1, l2]")
-    parser.add_argument('--victim', default='RN50', choices=['ViT-L/14', 'ViT-B/16', 'ViT-B/32', 'RN50', 'RN101'])
+    parser.add_argument('--victim', default='ViT-B/16', choices=['ViT-L/14', 'ViT-B/16', 'ViT-B/32', 'RN50', 'RN101'])
     parser.add_argument("--text_encoder", type=str, default="bert-base-uncased")
     parser.add_argument("--topk", type=int, default=10)
     parser.add_argument("--num-perturbation", type=int, default=3)
     parser.add_argument("--txt-dim", type=int, default=1024)
-    parser.add_argument("--output-dim", type=int, default=1024)
+    parser.add_argument("--output-dim", type=int, default=512)
     parser.add_argument("--epochs", type=int, default=100)
     parser.add_argument("--max-words", type=int, default=77)
     parser.add_argument("--max-candidate", type=int, default=7)