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Refine lib -> xautodl

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D-X-Y 2021-05-19 07:19:20 +00:00
parent bda202ce87
commit 5b9a028e60
19 changed files with 46 additions and 46 deletions
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6
docs/NeurIPS-2019-TAS.md
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@ -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="https://d-x-y.github.com/resources/videos/NeurIPS-2019-TAS/TAS-arch.gif?raw=true" width="180px"/>
<img src="http://xuanyidong.com/resources/paper-icon/NIPS-2019-TAS.png" width="680px"/>
<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.

4
exps/LFNA/lfna.py
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@ -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):

4
exps/LFNA/lfna_utils.py
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@ -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):

4
exps/NATS-Bench/main-tss.py
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@ -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,

11
exps/prepare.py → exps/TAS/prepare.py
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@ -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:

13
scripts/TAS/prepare.sh Normal file
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@ -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

13
scripts/prepare.sh
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@ -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

2
xautodl/models/cell_infers/cells.py
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@ -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

1
xautodl/models/cell_infers/nasnet_cifar.py
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@ -4,6 +4,7 @@
import torch
import torch.nn as nn
from copy import deepcopy
from .cells import NASNetInferCell as InferCell, AuxiliaryHeadCIFAR

10
xautodl/models/xcore.py
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@ -9,11 +9,11 @@ import torch
__all__ = ["get_model"]
from xlayers.super_core import SuperSequential
from xlayers.super_core import SuperLinear
from xlayers.super_core import SuperDropout
from xlayers.super_core import super_name2norm
from xlayers.super_core import super_name2activation
from xautodl.xlayers.super_core import SuperSequential
from xautodl.xlayers.super_core import SuperLinear
from xautodl.xlayers.super_core import SuperDropout
from xautodl.xlayers.super_core import super_name2norm
from xautodl.xlayers.super_core import super_name2activation
def get_model(config: Dict[Text, Any], **kwargs):

3
xautodl/procedures/advanced_main.py
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@ -7,8 +7,7 @@ import os, sys, time, torch
from typing import Optional, Text, Callable
# modules in AutoDL
from log_utils import AverageMeter
from log_utils import time_string
from xautodl.log_utils import AverageMeter, time_string
from .eval_funcs import obtain_accuracy

3
xautodl/procedures/basic_main.py
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@ -4,8 +4,7 @@
import os, sys, time, torch
# modules in AutoDL
from log_utils import AverageMeter
from log_utils import time_string
from xautodl.log_utils import AverageMeter, time_string
from .eval_funcs import obtain_accuracy

2
xautodl/procedures/eval_funcs.py
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@ -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

2
xautodl/procedures/funcs_nasbench.py
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@ -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

1
xautodl/procedures/q_exps.py
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@ -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

5
xautodl/procedures/search_main.py
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@ -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

4
xautodl/procedures/search_main_v2.py
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@ -4,8 +4,8 @@
import os, sys, time, torch
# modules in AutoDL
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

2
xautodl/procedures/simple_KD_main.py
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@ -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

2
xautodl/procedures/starts.py
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@ -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))