From 5b9a028e60c7f029f9e12df65e602c50cdddc0a5 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Wed, 19 May 2021 07:19:20 +0000 Subject: [PATCH] Refine lib -> xautodl --- docs/NeurIPS-2019-TAS.md | 6 +++--- exps/LFNA/lfna.py | 4 ++-- exps/LFNA/lfna_utils.py | 4 ++-- exps/NATS-Bench/main-tss.py | 4 ++-- exps/{ => TAS}/prepare.py | 11 ++++++----- scripts/TAS/prepare.sh | 13 +++++++++++++ scripts/prepare.sh | 13 ------------- xautodl/models/cell_infers/cells.py | 2 +- xautodl/models/cell_infers/nasnet_cifar.py | 1 + xautodl/models/xcore.py | 10 +++++----- xautodl/procedures/advanced_main.py | 3 +-- xautodl/procedures/basic_main.py | 3 +-- xautodl/procedures/eval_funcs.py | 2 +- xautodl/procedures/funcs_nasbench.py | 2 +- xautodl/procedures/q_exps.py | 1 - xautodl/procedures/search_main.py | 5 +++-- xautodl/procedures/search_main_v2.py | 4 ++-- xautodl/procedures/simple_KD_main.py | 2 +- xautodl/procedures/starts.py | 2 +- 19 files changed, 46 insertions(+), 46 deletions(-) rename exps/{ => TAS}/prepare.py (89%) create mode 100644 scripts/TAS/prepare.sh delete mode 100644 scripts/prepare.sh diff --git a/docs/NeurIPS-2019-TAS.md b/docs/NeurIPS-2019-TAS.md index 12c6bf4..fb87976 100644 --- a/docs/NeurIPS-2019-TAS.md +++ b/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).

- - + +

@@ -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. diff --git a/exps/LFNA/lfna.py b/exps/LFNA/lfna.py index 189044e..593d133 100644 --- a/exps/LFNA/lfna.py +++ b/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): diff --git a/exps/LFNA/lfna_utils.py b/exps/LFNA/lfna_utils.py index f78727e..44489e1 100644 --- a/exps/LFNA/lfna_utils.py +++ b/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): diff --git a/exps/NATS-Bench/main-tss.py b/exps/NATS-Bench/main-tss.py index e3e1016..dd5ec15 100644 --- a/exps/NATS-Bench/main-tss.py +++ b/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, diff --git a/exps/prepare.py b/exps/TAS/prepare.py similarity index 89% rename from exps/prepare.py rename to exps/TAS/prepare.py index 6ac50a4..94ff723 100644 --- a/exps/prepare.py +++ b/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: diff --git a/scripts/TAS/prepare.sh b/scripts/TAS/prepare.sh new file mode 100644 index 0000000..005d552 --- /dev/null +++ b/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 diff --git a/scripts/prepare.sh b/scripts/prepare.sh deleted file mode 100644 index 3bf7150..0000000 --- a/scripts/prepare.sh +++ /dev/null @@ -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 diff --git a/xautodl/models/cell_infers/cells.py b/xautodl/models/cell_infers/cells.py index 40df57b..1fa2e98 100644 --- a/xautodl/models/cell_infers/cells.py +++ b/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 diff --git a/xautodl/models/cell_infers/nasnet_cifar.py b/xautodl/models/cell_infers/nasnet_cifar.py index bdef399..2109477 100644 --- a/xautodl/models/cell_infers/nasnet_cifar.py +++ b/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 diff --git a/xautodl/models/xcore.py b/xautodl/models/xcore.py index 819f272..e7e6b5b 100644 --- a/xautodl/models/xcore.py +++ b/xautodl/models/xcore.py @@ -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): diff --git a/xautodl/procedures/advanced_main.py b/xautodl/procedures/advanced_main.py index dfb32f9..854fe63 100644 --- a/xautodl/procedures/advanced_main.py +++ b/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 log_utils import time_string +from xautodl.log_utils import AverageMeter, time_string from .eval_funcs import obtain_accuracy diff --git a/xautodl/procedures/basic_main.py b/xautodl/procedures/basic_main.py index 94ca90f..1d74978 100644 --- a/xautodl/procedures/basic_main.py +++ b/xautodl/procedures/basic_main.py @@ -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 diff --git a/xautodl/procedures/eval_funcs.py b/xautodl/procedures/eval_funcs.py index 99b569b..006ba35 100644 --- a/xautodl/procedures/eval_funcs.py +++ b/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 diff --git a/xautodl/procedures/funcs_nasbench.py b/xautodl/procedures/funcs_nasbench.py index bd0682a..00566a9 100644 --- a/xautodl/procedures/funcs_nasbench.py +++ b/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 diff --git a/xautodl/procedures/q_exps.py b/xautodl/procedures/q_exps.py index 48a6b7e..22bf248 100644 --- a/xautodl/procedures/q_exps.py +++ b/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 diff --git a/xautodl/procedures/search_main.py b/xautodl/procedures/search_main.py index 8d2b209..920ddbd 100644 --- a/xautodl/procedures/search_main.py +++ b/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 diff --git a/xautodl/procedures/search_main_v2.py b/xautodl/procedures/search_main_v2.py index 03139c4..14ab16c 100644 --- a/xautodl/procedures/search_main_v2.py +++ b/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 models import change_key +from xautodl.log_utils import AverageMeter, time_string +from xautodl.models import change_key from .eval_funcs import obtain_accuracy diff --git a/xautodl/procedures/simple_KD_main.py b/xautodl/procedures/simple_KD_main.py index 211dce8..0d31431 100644 --- a/xautodl/procedures/simple_KD_main.py +++ b/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 diff --git a/xautodl/procedures/starts.py b/xautodl/procedures/starts.py index 1ae19c5..b315521 100644 --- a/xautodl/procedures/starts.py +++ b/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))