Refine lib -> xautodl

docs/NeurIPS-2019-TAS.md

@@ -7,8 +7,8 @@ In this paper, we proposed a differentiable searching strategy for transformable
 You could see the highlight of our Transformable Architecture Search (TAS) at our [project page](https://xuanyidong.com/assets/projects/NeurIPS-2019-TAS.html).
 
 <p float="left">
-<img src="https://d-x-y.github.com/resources/paper-icon/NIPS-2019-TAS.png" width="680px"/>
+<img src="http://xuanyidong.com/resources/paper-icon/NIPS-2019-TAS.png" width="680px"/>
-<img src="https://d-x-y.github.com/resources/videos/NeurIPS-2019-TAS/TAS-arch.gif?raw=true" width="180px"/>
+<img src="http://xuanyidong.com/resources/videos/NeurIPS-2019-TAS/TAS-arch.gif?raw=true" width="180px"/>
 </p>
 
 
@@ -24,7 +24,7 @@ We provide some logs at [Google Drive](https://drive.google.com/open?id=1_qUY4DT
 
 ## Usage
 
-Use `bash ./scripts/prepare.sh` to prepare data splits for `CIFAR-10`, `CIFARR-100`, and `ILSVRC2012`.
+Use `bash ./scripts/TAS/prepare.sh` to prepare data splits for `CIFAR-10`, `CIFARR-100`, and `ILSVRC2012`.
 If you do not have `ILSVRC2012` data, please comment L12 in `./scripts/prepare.sh`.
 
 args: `cifar10` indicates the dataset name, `ResNet56` indicates the basemodel name, `CIFARX` indicates the searching hyper-parameters, `0.47/0.57` indicates the expected FLOP ratio, `-1` indicates the random seed.

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 lfna_utils import lfna_setup, train_model, TimeData
-from xautodl.lfna_meta_model import LFNA_Meta
+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 xautodl.procedures import prepare_seed, prepare_logger
-from datasets.synthetic_core import get_synthetic_env
+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:

scripts/TAS/prepare.sh

@@ -0,0 +1,13 @@
+#!/bin/bash
+# bash ./scripts/TAS/prepare.sh
+#datasets='cifar10 cifar100 imagenet-1k'
+#ratios='0.5 0.8 0.9'
+ratios='0.5'
+save_dir=./.latent-data/splits
+
+for ratio in ${ratios}
+do
+  python ./exps/TAS/prepare.py --name cifar10  --root $TORCH_HOME/cifar.python  --save ${save_dir}/cifar10-${ratio}.pth --ratio ${ratio}
+  python ./exps/TAS/prepare.py --name cifar100 --root $TORCH_HOME/cifar.python  --save ${save_dir}/cifar100-${ratio}.pth --ratio ${ratio}
+  python ./exps/TAS/prepare.py --name imagenet-1k --root $TORCH_HOME/ILSVRC2012 --save ${save_dir}/imagenet-1k-${ratio}.pth --ratio ${ratio}
+done

scripts/prepare.sh

@@ -1,13 +0,0 @@
-#!/bin/bash
-# bash ./scripts/prepare.sh
-#datasets='cifar10 cifar100 imagenet-1k'
-#ratios='0.5 0.8 0.9'
-ratios='0.5'
-save_dir=./.latent-data/splits
-
-for ratio in ${ratios}
-do
-  python ./exps/prepare.py --name cifar10  --root $TORCH_HOME/cifar.python  --save ${save_dir}/cifar10-${ratio}.pth --ratio ${ratio}
-  python ./exps/prepare.py --name cifar100 --root $TORCH_HOME/cifar.python  --save ${save_dir}/cifar100-${ratio}.pth --ratio ${ratio}
-  python ./exps/prepare.py --name imagenet-1k --root $TORCH_HOME/ILSVRC2012 --save ${save_dir}/imagenet-1k-${ratio}.pth --ratio ${ratio}
-done

xautodl/models/cell_infers/cells.py

@@ -6,7 +6,7 @@ import torch
 import torch.nn as nn
 from copy import deepcopy
 
-from models.cell_operations import OPS
+from xautodl.models.cell_operations import OPS
 
 
 # Cell for NAS-Bench-201

xautodl/models/cell_infers/nasnet_cifar.py

@@ -4,6 +4,7 @@
 import torch
 import torch.nn as nn
 from copy import deepcopy
+
 from .cells import NASNetInferCell as InferCell, AuxiliaryHeadCIFAR
 
 

xautodl/models/xcore.py

@@ -9,11 +9,11 @@ import torch
 __all__ = ["get_model"]
 
 
-from xlayers.super_core import SuperSequential
+from xautodl.xlayers.super_core import SuperSequential
-from xlayers.super_core import SuperLinear
+from xautodl.xlayers.super_core import SuperLinear
-from xlayers.super_core import SuperDropout
+from xautodl.xlayers.super_core import SuperDropout
-from xlayers.super_core import super_name2norm
+from xautodl.xlayers.super_core import super_name2norm
-from xlayers.super_core import super_name2activation
+from xautodl.xlayers.super_core import super_name2activation
 
 
 def get_model(config: Dict[Text, Any], **kwargs):

xautodl/procedures/advanced_main.py

@@ -7,8 +7,7 @@ import os, sys, time, torch
 from typing import Optional, Text, Callable
 
 # modules in AutoDL
-from log_utils import AverageMeter
+from xautodl.log_utils import AverageMeter, time_string
-from log_utils import time_string
 from .eval_funcs import obtain_accuracy
 
 

xautodl/procedures/basic_main.py

@@ -4,8 +4,7 @@
 import os, sys, time, torch
 
 # modules in AutoDL
-from log_utils import AverageMeter
+from xautodl.log_utils import AverageMeter, time_string
-from log_utils import time_string
 from .eval_funcs import obtain_accuracy
 
 

xautodl/procedures/eval_funcs.py

@@ -15,6 +15,6 @@ def obtain_accuracy(output, target, topk=(1,)):
 
     res = []
     for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+        correct_k = correct[:k].contiguous().view(-1).float().sum(0, keepdim=True)
         res.append(correct_k.mul_(100.0 / batch_size))
     return res

xautodl/procedures/funcs_nasbench.py

@@ -4,7 +4,7 @@
 import os, time, copy, torch, pathlib
 
 # modules in AutoDL
-import xautodl.datasets
+from xautodl import datasets
 from xautodl.config_utils import load_config
 from xautodl.procedures import prepare_seed, get_optim_scheduler
 from xautodl.log_utils import AverageMeter, time_string, convert_secs2time

xautodl/procedures/q_exps.py

@@ -8,7 +8,6 @@ import pprint
 import logging
 from copy import deepcopy
 
-from log_utils import pickle_load
 import qlib
 from qlib.utils import init_instance_by_config
 from qlib.workflow import R

xautodl/procedures/search_main.py

@@ -2,8 +2,9 @@
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
 ##################################################
 import os, sys, time, torch
-from log_utils import AverageMeter, time_string
+
-from models import change_key
+from xautodl.log_utils import AverageMeter, time_string
+from xautodl.models import change_key
 
 from .eval_funcs import obtain_accuracy
 

xautodl/procedures/search_main_v2.py

@@ -4,8 +4,8 @@
 import os, sys, time, torch
 
 # modules in AutoDL
-from log_utils import AverageMeter, time_string
+from xautodl.log_utils import AverageMeter, time_string
-from models import change_key
+from xautodl.models import change_key
 from .eval_funcs import obtain_accuracy
 
 

xautodl/procedures/simple_KD_main.py

@@ -5,7 +5,7 @@ import os, sys, time, torch
 import torch.nn.functional as F
 
 # modules in AutoDL
-from log_utils import AverageMeter, time_string
+from xautodl.log_utils import AverageMeter, time_string
 from .eval_funcs import obtain_accuracy
 
 

xautodl/procedures/starts.py

@@ -16,7 +16,7 @@ def prepare_seed(rand_seed):
 
 def prepare_logger(xargs):
     args = copy.deepcopy(xargs)
-    from log_utils import Logger
+    from xautodl.log_utils import Logger
 
     logger = Logger(args.save_dir, args.rand_seed)
     logger.log("Main Function with logger : {:}".format(logger))