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@ -46,11 +46,12 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh GDAS
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```
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### Training Logs
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Some training logs can be found in `./data/logs/`, and some pre-trained models can be found in [Google Driver](https://drive.google.com/open?id=1Ofhc49xC1PLIX4O708gJZ1ugzz4td_RJ).
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You can find some training logs in [`./data/logs/`](https://github.com/D-X-Y/GDAS/tree/master/data/logs).
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You can also find some pre-trained models in [Google Driver](https://drive.google.com/open?id=1Ofhc49xC1PLIX4O708gJZ1ugzz4td_RJ).
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### Experimental Results
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<img src="data/imagenet-results.png" width="700">
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Figure 2. Top-1 and top-5 errors on ImageNet.
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Figure-2. Top-1 and top-5 errors on ImageNet.
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### Citation
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If you find that this project (GDAS) helps your research, please cite the paper:
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data/logs/GDAS_V1-imagenet-seed-3993.txt → data/logs/GDAS-V1-imagenet-seed-3993.txt
Executable file → Normal file
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data/logs/GDAS_V1-imagenet-seed-3993.txt → data/logs/GDAS-V1-imagenet-seed-3993.txt
Executable file → Normal file
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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# python ./exps-nas/cvpr-vis.py --save_dir ./snapshots/NAS-VIS/
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import os, sys, time, glob, random, argparse
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import numpy as np
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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# For evaluating the learned model
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import os, sys, time, glob, random, argparse
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import numpy as np
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from copy import deepcopy
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, time, glob, random, argparse
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import numpy as np
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from copy import deepcopy
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, time
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from copy import deepcopy
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import torch
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, time
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from copy import deepcopy
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import torch
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, gc, sys, math, time, glob, random, argparse
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import numpy as np
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from copy import deepcopy
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# Modified from https://github.com/quark0/darts
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import os, gc, sys, time, math
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import numpy as np
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from copy import deepcopy
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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from .MetaBatchSampler import MetaBatchSampler
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from .TieredImageNet import TieredImageNet
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from .LanguageDataset import Corpus
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, torch
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import os.path as osp
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import torchvision.datasets as dset
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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from .model_search import Network
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# acceleration model
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from .CifarNet import NetworkCIFAR
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from .ImageNet import NetworkImageNet
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from .CifarNet import NetworkCIFAR
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from .ImageNet import NetworkImageNet
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# genotypes
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from .genotypes import model_types
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from .genotypes import model_types
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from .construct_utils import return_alphas_str
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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from .utils import load_config
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from .scheduler import MultiStepLR, obtain_scheduler
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import torch
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from bisect import bisect_right
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# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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#
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, json
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from pathlib import Path
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from collections import namedtuple
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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from .utils import AverageMeter, RecorderMeter, convert_secs2time
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from .utils import time_file_str, time_string
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from .utils import test_imagenet_data
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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# modified from https://github.com/warmspringwinds/pytorch-segmentation-detection/blob/master/pytorch_segmentation_detection/utils/flops_benchmark.py
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import copy, torch
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import torch
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import os, sys
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import os.path as osp
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, time
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import numpy as np
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import random
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