Refine lib -> xautodl

exps/LFNA/lfna.py

@@ -27,8 +27,8 @@ from xautodl.datasets.synthetic_core import get_synthetic_env, EnvSampler
 from xautodl.models.xcore import get_model
 from xautodl.xlayers import super_core, trunc_normal_
 
-from xautodl.lfna_utils import lfna_setup, train_model, TimeData
-from xautodl.lfna_meta_model import LFNA_Meta
+from lfna_utils import lfna_setup, train_model, TimeData
+from lfna_meta_model import LFNA_Meta
 
 
 def epoch_train(loader, meta_model, base_model, optimizer, criterion, device, logger):

exps/LFNA/lfna_utils.py

@@ -4,8 +4,8 @@
 import copy
 import torch
 from tqdm import tqdm
-from procedures import prepare_seed, prepare_logger
-from datasets.synthetic_core import get_synthetic_env
+from xautodl.procedures import prepare_seed, prepare_logger
+from xautodl.datasets.synthetic_core import get_synthetic_env
 
 
 def lfna_setup(args):

exps/NATS-Bench/main-tss.py

@@ -665,7 +665,7 @@ if __name__ == "__main__":
                     len(args.datasets), len(args.xpaths), len(args.splits)
                 )
             )
-        if args.workers <= 0:
+        if args.workers < 0:
             raise ValueError("invalid number of workers : {:}".format(args.workers))
 
         target_indexes = filter_indexes(
@@ -675,7 +675,7 @@ if __name__ == "__main__":
         assert torch.cuda.is_available(), "CUDA is not available."
         torch.backends.cudnn.enabled = True
         torch.backends.cudnn.deterministic = True
-        torch.set_num_threads(args.workers)
+        torch.set_num_threads(args.workers if args.workers > 0 else 1)
 
         main(
             save_dir,

exps/TAS/prepare.py

@@ -1,6 +1,10 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.01 #
+#####################################################
 # python exps/prepare.py --name cifar10     --root $TORCH_HOME/cifar.python --save ./data/cifar10.split.pth
 # python exps/prepare.py --name cifar100    --root $TORCH_HOME/cifar.python --save ./data/cifar100.split.pth
 # python exps/prepare.py --name imagenet-1k --root $TORCH_HOME/ILSVRC2012   --save ./data/imagenet-1k.split.pth
+#####################################################
 import sys, time, torch, random, argparse
 from collections import defaultdict
 import os.path as osp
@@ -12,9 +16,6 @@ from pathlib import Path
 import torchvision
 import torchvision.datasets as dset
 
-lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
-if str(lib_dir) not in sys.path:
-    sys.path.insert(0, str(lib_dir))
 parser = argparse.ArgumentParser(
     description="Prepare splits for searching",
     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
@@ -35,9 +36,9 @@ def main():
     print("torchvision version : {:}".format(torchvision.__version__))
 
     if name == "cifar10":
-        dataset = dset.CIFAR10(args.root, train=True)
+        dataset = dset.CIFAR10(args.root, train=True, download=True)
     elif name == "cifar100":
-        dataset = dset.CIFAR100(args.root, train=True)
+        dataset = dset.CIFAR100(args.root, train=True, download=True)
     elif name == "imagenet-1k":
         dataset = dset.ImageFolder(osp.join(args.root, "train"))
     else: