├── Pytorch_code ├── densenet2 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── resnet101 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── Densenet │ ├── test.sh │ ├── base_tester.pyc │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── .main_allreduce.py.swp │ ├── __pycache__ │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── dense169 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── densenet.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── resnet152 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── dense161 │ ├── test.sh │ ├── train.sh │ ├── augmentation.pyc │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── densenet.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── drnD105 │ ├── test.sh │ ├── train.sh │ ├── augmentation.pyc │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── drn.cpython-36.pyc │ │ ├── model.cpython-36.pyc │ │ ├── densenet.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── resnet50_chair │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ └── memcached_dataset.py ├── dpn92 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── dpn.cpython-36.pyc │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── dpn98 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── dpn.cpython-36.pyc │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── dpn107 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── dpn.cpython-36.pyc │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── dpn131 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── dpn.cpython-36.pyc │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── nasnet │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── dpn.cpython-36.pyc │ │ ├── model.cpython-36.pyc │ │ ├── nasnet.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── senet.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ ├── inceptionresnetv2.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── senet154 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── dpn.cpython-36.pyc │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── senet.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ ├── inceptionresnetv2.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── xception │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── xception.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── inceptionv4.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ ├── inceptionresnetv2.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ └── memcached_dataset.py ├── inceptionv4 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── inceptionv4.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── seResnext50 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── dpn.cpython-36.pyc │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── senet.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ ├── inceptionresnetv2.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ ├── memcached_dataset.py │ └── distributed_utils.py ├── seresnet152 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── dpn.cpython-36.pyc │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── senet.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ ├── inceptionresnetv2.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ └── memcached_dataset.py ├── seresnext101 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── dpn.cpython-36.pyc │ │ ├── model.cpython-36.pyc │ │ ├── senet.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ ├── inceptionresnetv2.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ └── memcached_dataset.py ├── inceptionResnetV2 │ ├── test.sh │ ├── train.sh │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── dpn.cpython-36.pyc │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ ├── inceptionresnetv2.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ └── memcached_dataset.py ├── resnext101_32x4d │ ├── test.sh │ ├── train.sh │ ├── resnext_features │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── resnext101_32x4d_features.cpython-36.pyc │ │ │ └── resnext101_64x4d_features.cpython-36.pyc │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ │ ├── dpn.cpython-36.pyc │ │ ├── model.cpython-36.pyc │ │ ├── resnet.cpython-36.pyc │ │ ├── senet.cpython-36.pyc │ │ ├── resnext.cpython-36.pyc │ │ ├── base_tester.cpython-36.pyc │ │ ├── augmentation.cpython-36.pyc │ │ ├── distributed_utils.cpython-36.pyc │ │ ├── inceptionresnetv2.cpython-36.pyc │ │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ └── memcached_dataset.py └── resnext101_64x4d │ ├── test.sh │ ├── train.sh │ ├── resnext_features │ ├── __init__.py │ └── __pycache__ │ │ ├── __init__.cpython-36.pyc │ │ ├── resnext101_32x4d_features.cpython-36.pyc │ │ └── resnext101_64x4d_features.cpython-36.pyc │ ├── distributed_utils.pyc │ ├── memcached_dataset.pyc │ ├── __pycache__ │ ├── dpn.cpython-36.pyc │ ├── model.cpython-36.pyc │ ├── resnet.cpython-36.pyc │ ├── senet.cpython-36.pyc │ ├── resnext.cpython-36.pyc │ ├── base_tester.cpython-36.pyc │ ├── augmentation.cpython-36.pyc │ ├── distributed_utils.cpython-36.pyc │ ├── inceptionresnetv2.cpython-36.pyc │ └── memcached_dataset.cpython-36.pyc │ ├── augmentation.py │ ├── base_tester.py │ └── memcached_dataset.py ├── Rank.png ├── loss.png ├── loss1.png ├── Label_Info.docx ├── furniture - Youngkl_pytorch.pdf ├── .idea ├── deployment.xml ├── vcs.xml ├── inspectionProfiles │ └── profiles_settings.xml ├── modules.xml ├── furniture.iml ├── webServers.xml └── misc.xml ├── README.md ├── GenerateTestLabelFile.py ├── data ├── txtTest.py ├── validname.py ├── validnum.txt └── trainnum.txt ├── draw_nums.py ├── GenChairTrain.py ├── extra.txt ├── GetChairTest.py └── ConvertNpy2Csv.py /Pytorch_code/densenet2/test.sh: -------------------------------------------------------------------------------- 1 | srun -p AD1 -n1 --gres=gpu:1 --ntasks-per-node=1 python test.py -------------------------------------------------------------------------------- /Pytorch_code/resnet101/test.sh: -------------------------------------------------------------------------------- 1 | srun -p AD2 -n1 --gres=gpu:1 --ntasks-per-node=1 python test.py -------------------------------------------------------------------------------- /Pytorch_code/Densenet/test.sh: -------------------------------------------------------------------------------- 1 | srun -p AD2 -n1 --gres=gpu:1 --ntasks-per-node=1 python test.py 2 | -------------------------------------------------------------------------------- /Pytorch_code/dense169/test.sh: -------------------------------------------------------------------------------- 1 | srun -p AD2 -n1 --gres=gpu:1 --ntasks-per-node=1 python test.py 2 | -------------------------------------------------------------------------------- /Pytorch_code/resnet152/test.sh: -------------------------------------------------------------------------------- 1 | srun -p AD2 -n1 --gres=gpu:1 --ntasks-per-node=1 python test.py 2 | -------------------------------------------------------------------------------- /Pytorch_code/dense161/test.sh: -------------------------------------------------------------------------------- 1 | srun -p AD2 -n1 --gres=gpu:1 --ntasks-per-node=1 python -u test.py 2 | -------------------------------------------------------------------------------- /Pytorch_code/drnD105/test.sh: -------------------------------------------------------------------------------- 1 | srun -p AD2 -n1 --gres=gpu:1 --ntasks-per-node=1 python -u test.py 2 | -------------------------------------------------------------------------------- /Pytorch_code/resnet50_chair/test.sh: -------------------------------------------------------------------------------- 1 | srun -p AD2 -n1 --gres=gpu:1 --ntasks-per-node=1 python -u test.py 2 | -------------------------------------------------------------------------------- /Pytorch_code/dpn92/test.sh: -------------------------------------------------------------------------------- 1 | srun -p AD2 -n1 --gres=gpu:1 --ntasks-per-node=1 --job-name=test python -u test.py 2 | -------------------------------------------------------------------------------- /Pytorch_code/dpn98/test.sh: -------------------------------------------------------------------------------- 1 | srun -p AD2 -n1 --gres=gpu:1 --ntasks-per-node=1 --job-name=test python -u test.py 2 | 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--job-name=test python -u test.py 2 | -------------------------------------------------------------------------------- /loss1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Youngkl0726/kaggle_iMaterialist-Challenge-Furniture-at-FGVC5/HEAD/loss1.png -------------------------------------------------------------------------------- /Pytorch_code/inceptionv4/test.sh: -------------------------------------------------------------------------------- 1 | srun -p AD2 -n1 --gres=gpu:1 --ntasks-per-node=1 --job-name=test python -u test.py 2 | -------------------------------------------------------------------------------- /Pytorch_code/seResnext50/test.sh: -------------------------------------------------------------------------------- 1 | srun -p AD2 -n1 --gres=gpu:1 --ntasks-per-node=1 --job-name=test python -u test.py 2 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # kaggle_iMaterialist-Challenge-Furniture-at-FGVC5 2 | ## Competition: image classification with 128 classes. 3 | ### Link: https://www.kaggle.com/c/imaterialist-challenge-furniture-2018 4 | ## Results: 5 | ### Public Leaderboard score 0.11915 6 | ### Private Leaderboard score 0.12274 7 | ### Rank 4 8 | -------------------------------------------------------------------------------- /Pytorch_code/dpn92/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/dpn98/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/Densenet/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/dense161/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/dense169/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/dpn107/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/dpn131/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/drnD105/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/nasnet/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/resnet152/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/senet154/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/xception/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/inceptionv4/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/resnet50_chair/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/seResnext50/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/seresnet152/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/seresnext101/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/inceptionResnetV2/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/resnext101_32x4d/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /Pytorch_code/resnext101_64x4d/augmentation.py: -------------------------------------------------------------------------------- 1 | 2 | import torchvision.transforms.functional as F 3 | 4 | 5 | class HorizontalFlip(object): 6 | """Horizontally flip the given PIL Image.""" 7 | 8 | def __call__(self, img): 9 | """ 10 | Args: 11 | img (PIL Image): Image to be flipped. 12 | Returns: 13 | PIL Image: Flipped image. 14 | """ 15 | return F.hflip(img) -------------------------------------------------------------------------------- /GenerateTestLabelFile.py: -------------------------------------------------------------------------------- 1 | file = open('result_resnext101_32x4d_ck4_label.txt') 2 | test_label_txt = open('./data/test_label3.txt', 'wb') 3 | for i in xrange(12704): 4 | line = file.readline() 5 | line = line.strip() 6 | line = line.split(",") 7 | name = line[0] 8 | id = int(line[1])-1 9 | # print name, id 10 | print 'test/test/{}.jpg'.format(name)+' '+str(id)+'\n' 11 | test_label_txt.write('test/test/{}.jpg'.format(name)+' '+str(id)+'\n') -------------------------------------------------------------------------------- /.idea/furniture.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 12 | -------------------------------------------------------------------------------- /data/txtTest.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # In[1]: 5 | 6 | 7 | import os 8 | import sys 9 | 10 | 11 | # In[9]: 12 | 13 | 14 | basepath = 'test' 15 | test = os.listdir(basepath) 16 | A = [] 17 | for x in test: 18 | A.append(os.path.join(basepath,x)) 19 | dct = sorted(A,key=lambda x: x[0]) 20 | print(dct) 21 | 22 | 23 | # In[14]: 24 | 25 | 26 | with open("test.txt","wb") as fw: 27 | for x in dct: 28 | x = x.replace("\\","/") 29 | fw.write((x+' '+str(0)+'\r\n').encode('utf-8')) 30 | 31 | -------------------------------------------------------------------------------- /.idea/webServers.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 14 | 15 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 14 | 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /draw_nums.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | import csv 4 | 5 | filename = 'result_resnext101_32x4d_ck4.csv' 6 | nums = np.zeros(128) 7 | with open(filename) as f: 8 | reader = csv.reader(f) 9 | head_row = next(reader) 10 | for row in reader: 11 | nums[int(row[1])-1] += 1 12 | print(nums) 13 | 14 | plt.figure(figsize=(20, 5)) 15 | plt.plot(range(0,128),nums) 16 | plt.xlabel("Label") 17 | plt.ylabel("Sum") 18 | plt.title("Label-Sum") 19 | plt.show() 20 | 21 | plt.figure(figsize=(20, 5)) 22 | plt.hist(nums,128) 23 | plt.xlabel("Sum of a label") 24 | plt.ylabel("The numbers of the same") 25 | plt.title("data distribution") 26 | plt.show() 27 | 28 | -------------------------------------------------------------------------------- /Pytorch_code/densenet2/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | -------------------------------------------------------------------------------- /Pytorch_code/resnet101/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | -------------------------------------------------------------------------------- /GenChairTrain.py: -------------------------------------------------------------------------------- 1 | """ 2 | ori_id, chair_id 3 | 125, 0 4 | 28, 1 5 | 3, 2 6 | 62, 3 7 | 2, 4 8 | 25, 5 9 | 14, 6 10 | 22, 7 11 | 102, 8 12 | """ 13 | id_list =[125, 28, 3, 62, 2, 25, 14, 22, 102] 14 | train_txt = open("./data/valid.txt") 15 | chair_train_txt = open("./data/chair_valid.txt", 'wb') 16 | num = [] 17 | for i in xrange(9): 18 | num.append(0) 19 | for i in xrange(6309): 20 | line = train_txt.readline() 21 | line = line.strip() 22 | li = line.split(" ") 23 | id = int(li[1]) 24 | for j in xrange(9): 25 | if id == id_list[j]: 26 | num[j]+=1 27 | chair_train_txt.write(li[0]+" "+str(j)+"\n") 28 | chair_train_txt.close() 29 | print num 30 | # [1922, 1416, 1479, 1733, 2355, 1549, 1053, 1178, 2186] 31 | # [48, 49, 49, 50, 49, 47, 47, 49, 49] -------------------------------------------------------------------------------- /extra.txt: -------------------------------------------------------------------------------- 1 | 156.jpg 633.jpg 710.jpg 721.jpg 722.jpg 883.jpg 1045.jpg 1298.jpg 1419.jpg 1761.jpg 1783.jpg 1799.jpg 1844.jpg 2072.jpg 2282.jpg 2318.jpg 3042.jpg 3094.jpg 3176.jpg 3268.jpg 3319.jpg 3387.jpg 3513.jpg 3627.jpg 3645.jpg 3717.jpg 3911.jpg 3969.jpg 4075.jpg 4190.jpg 4250.jpg 4381.jpg 4532.jpg 4598.jpg 4656.jpg 4887.jpg 4959.jpg 5127.jpg 5141.jpg 5207.jpg 5313.jpg 5359.jpg 5733.jpg 5776.jpg 5815.jpg 6397.jpg 6398.jpg 6619.jpg 6646.jpg 6698.jpg 6824.jpg 6873.jpg 7080.jpg 7536.jpg 7615.jpg 7667.jpg 7829.jpg 7855.jpg 7876.jpg 8092.jpg 8161.jpg 8250.jpg 8407.jpg 8523.jpg 8535.jpg 8555.jpg 8568.jpg 8569.jpg 8598.jpg 8974.jpg 9023.jpg 9063.jpg 9193.jpg 9262.jpg 9433.jpg 9769.jpg 9796.jpg 9918.jpg 9981.jpg 10157.jpg 10167.jpg 10232.jpg 10534.jpg 10627.jpg 10687.jpg 10755.jpg 10969.jpg 11288.jpg 11624.jpg 11633.jpg 11701.jpg 11875.jpg 12023.jpg 12281.jpg 12514.jpg 12620.jpg 12786.jpg 2 | 3 | -------------------------------------------------------------------------------- /data/validname.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # In[2]: 5 | 6 | 7 | import os 8 | import sys 9 | 10 | 11 | # In[25]: 12 | 13 | 14 | basepath = 'valid' 15 | valid = os.listdir(basepath) 16 | dct = [] 17 | for label in valid: 18 | path = os.path.join(basepath,label) 19 | lst = os.listdir(path) 20 | A = [] 21 | for x in lst: 22 | A.append(os.path.join(path,x)) 23 | dct.append((int(label),A)) 24 | dct2 = sorted(dct,key=lambda x: x[0]) 25 | 26 | 27 | 28 | # In[27]: 29 | 30 | 31 | with open("valid.txt","wb") as fw: 32 | for x in dct2: 33 | label = str(x[0]-1) 34 | A = x[1] 35 | for y in A: 36 | fw.write((y+' '+label+'\r\n').encode('utf-8')) 37 | 38 | 39 | # In[28]: 40 | 41 | 42 | with open('validnum.txt','wb') as fw: 43 | for x in dct2: 44 | label = str(x[0]-1) 45 | A = x[1] 46 | fw.write((label+','+str(len(A))+'\r\n').encode('utf-8')) 47 | 48 | -------------------------------------------------------------------------------- /GetChairTest.py: -------------------------------------------------------------------------------- 1 | def get_name(filename): 2 | file = open(filename) 3 | res_line = [] 4 | for i in xrange(12704): 5 | line = file.readline() 6 | line = line.strip() 7 | line = line.split(" ") 8 | # print line[0] 9 | res_line.append(line[0]) 10 | return res_line 11 | 12 | fname = get_name('./data/test.txt') 13 | # print fname 14 | def get_test_pre(filename): 15 | file = open(filename) 16 | res_line = [] 17 | for i in xrange(12704): 18 | line = file.readline() 19 | line = line.strip() 20 | # line = line.split(" ") 21 | # print line[0] 22 | res_line.append(int(line)) 23 | return res_line 24 | test_pre = get_test_pre("result.txt") 25 | # print test_pre 26 | chair_test_file = open('chair_test.txt', 'wb') 27 | id_list =[125, 28, 3, 62, 2, 25, 14, 22, 102] 28 | for i in xrange(12704): 29 | for j in xrange(9): 30 | if test_pre[i] == id_list[j]: 31 | chair_test_file.write(fname[i]+" "+str(i)+'\n') 32 | break 33 | -------------------------------------------------------------------------------- /ConvertNpy2Csv.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import csv 3 | 4 | txt_file = open('result.txt', 'wb') 5 | npy_name = 'nasnet_ck2.npy' #change the npy_name 6 | print npy_name 7 | npy = np.load(npy_name) 8 | len = npy.shape[0] 9 | print len 10 | for j in xrange(len): 11 | res = int(np.where(npy[j] == np.max(npy[j]))[0][0]) 12 | txt_file.write(str(res)+'\n') 13 | txt_file.close() 14 | 15 | 16 | def get_name(filename): 17 | file = open(filename) 18 | res_line = [] 19 | for i in xrange(12704): 20 | line = file.readline() 21 | line = line.strip() 22 | line = line.split(" ") 23 | line = line[0] 24 | line = line.split("/") 25 | line = line[1] 26 | line = line.split(".") 27 | # print line[0] 28 | res_line.append(line[0]) 29 | return res_line 30 | 31 | fname = get_name("test.txt") 32 | add = [] 33 | 34 | csvfile = open("result_nasnet_ck2.csv", "w") #change the name of files you will save 35 | fileheader = ["id", "predicted"] 36 | writer = csv.writer(csvfile) 37 | writer.writerow(fileheader) 38 | res_file = open('result.txt') 39 | for i in xrange(12704): 40 | context = [] 41 | line = res_file.readline() 42 | line = line.strip() 43 | line = line.split(" ") 44 | context.append(fname[i]) 45 | context.append(int(line[0])+1) 46 | writer.writerow(context) 47 | csvfile.close() 48 | -------------------------------------------------------------------------------- /Pytorch_code/dpn107/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/dpn131/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/dpn92/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/dpn98/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/drnD105/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/nasnet/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/Densenet/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/dense161/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/dense169/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/inceptionv4/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/resnet152/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/seResnext50/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/senet154/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/seresnet152/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/seresnext101/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/xception/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/inceptionResnetV2/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/resnet50_chair/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/resnext101_32x4d/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /Pytorch_code/resnext101_64x4d/base_tester.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import torch 4 | import torchvision 5 | import numpy as np 6 | 7 | class BaseTester(): 8 | def __init__(self, model): 9 | self.model = model 10 | 11 | def extract(self, data_loader): 12 | self.model.eval() 13 | res_features = [] 14 | 15 | for batch_index, (data, label) in enumerate(data_loader): 16 | print("batch_index is: {}".format(batch_index)) 17 | data = data.cuda() 18 | data = torch.autograd.Variable(data, volatile=True) 19 | output = self.model(data) 20 | output = output.data.cpu() 21 | res_features.extend(output.numpy()) 22 | return np.array(res_features) 23 | 24 | 25 | class TenCropTester(): 26 | def __init__(self, model): 27 | self.model = model 28 | 29 | def extract(self, data_loader): 30 | self.model.eval() 31 | res_features = [] 32 | 33 | for batch_index, (data, label) in enumerate(data_loader): 34 | print("batch_index is: {}".format(batch_index)) 35 | data = data.cuda() 36 | data = torch.autograd.Variable(data, volatile=True) 37 | bs, ncrops, c, h, w = data.size() 38 | # print("ncrops is:{}".format(ncrops)) 39 | output = self.model(data.view(-1, c, h, w)) 40 | output = output.data.cpu() 41 | output_avg = output.view(bs, ncrops, -1).mean(1).view(bs,-1) 42 | res_features.extend(output_avg.numpy()) 43 | return np.array(res_features) -------------------------------------------------------------------------------- /data/validnum.txt: -------------------------------------------------------------------------------- 1 | 0,50 2 | 1,50 3 | 2,49 4 | 3,49 5 | 4,49 6 | 5,50 7 | 6,50 8 | 7,49 9 | 8,47 10 | 9,46 11 | 10,50 12 | 11,49 13 | 12,49 14 | 13,49 15 | 14,47 16 | 15,48 17 | 16,49 18 | 17,48 19 | 18,50 20 | 19,49 21 | 20,48 22 | 21,50 23 | 22,49 24 | 23,48 25 | 24,50 26 | 25,47 27 | 26,50 28 | 27,49 29 | 28,49 30 | 29,50 31 | 30,48 32 | 31,50 33 | 32,50 34 | 33,49 35 | 34,48 36 | 35,49 37 | 36,50 38 | 37,49 39 | 38,50 40 | 39,50 41 | 40,50 42 | 41,47 43 | 42,50 44 | 43,50 45 | 44,49 46 | 45,49 47 | 46,50 48 | 47,48 49 | 48,50 50 | 49,49 51 | 50,50 52 | 51,50 53 | 52,50 54 | 53,49 55 | 54,49 56 | 55,50 57 | 56,50 58 | 57,50 59 | 58,50 60 | 59,50 61 | 60,48 62 | 61,50 63 | 62,50 64 | 63,47 65 | 64,49 66 | 65,50 67 | 66,48 68 | 67,50 69 | 68,49 70 | 69,48 71 | 70,48 72 | 71,49 73 | 72,50 74 | 73,49 75 | 74,50 76 | 75,49 77 | 76,50 78 | 77,50 79 | 78,49 80 | 79,49 81 | 80,50 82 | 81,50 83 | 82,49 84 | 83,50 85 | 84,49 86 | 85,48 87 | 86,50 88 | 87,50 89 | 88,50 90 | 89,50 91 | 90,49 92 | 91,50 93 | 92,50 94 | 93,50 95 | 94,50 96 | 95,50 97 | 96,50 98 | 97,50 99 | 98,50 100 | 99,49 101 | 100,49 102 | 101,50 103 | 102,49 104 | 103,50 105 | 104,49 106 | 105,50 107 | 106,50 108 | 107,49 109 | 108,50 110 | 109,50 111 | 110,50 112 | 111,49 113 | 112,49 114 | 113,50 115 | 114,49 116 | 115,50 117 | 116,50 118 | 117,48 119 | 118,50 120 | 119,50 121 | 120,49 122 | 121,49 123 | 122,49 124 | 123,50 125 | 124,50 126 | 125,48 127 | 126,49 128 | 127,48 129 | -------------------------------------------------------------------------------- /data/trainnum.txt: -------------------------------------------------------------------------------- 1 | 0,1235 2 | 1,1495 3 | 2,2355 4 | 3,1479 5 | 4,1585 6 | 5,1092 7 | 6,1593 8 | 7,1353 9 | 8,473 10 | 9,1966 11 | 10,1734 12 | 11,2574 13 | 12,1599 14 | 13,1864 15 | 14,1053 16 | 15,1336 17 | 16,1462 18 | 17,1536 19 | 18,846 20 | 19,3937 21 | 20,2456 22 | 21,1392 23 | 22,1178 24 | 23,1761 25 | 24,526 26 | 25,1549 27 | 26,2170 28 | 27,1668 29 | 28,1416 30 | 29,1167 31 | 30,2070 32 | 31,1691 33 | 32,1226 34 | 33,716 35 | 34,638 36 | 35,1572 37 | 36,2242 38 | 37,2291 39 | 38,1103 40 | 39,2027 41 | 40,618 42 | 41,3913 43 | 42,1713 44 | 43,1447 45 | 44,2269 46 | 45,1483 47 | 46,733 48 | 47,1280 49 | 48,1853 50 | 49,1261 51 | 50,1008 52 | 51,1161 53 | 52,1279 54 | 53,1790 55 | 54,2137 56 | 55,1710 57 | 56,690 58 | 57,781 59 | 58,1396 60 | 59,1660 61 | 60,1911 62 | 61,675 63 | 62,1733 64 | 63,1938 65 | 64,1145 66 | 65,340 67 | 66,1058 68 | 67,2237 69 | 68,1235 70 | 69,1329 71 | 70,1227 72 | 71,1390 73 | 72,2244 74 | 73,602 75 | 74,1782 76 | 75,2116 77 | 76,542 78 | 77,1999 79 | 78,1501 80 | 79,1476 81 | 80,1578 82 | 81,1262 83 | 82,321 84 | 83,1255 85 | 84,617 86 | 85,661 87 | 86,1656 88 | 87,1219 89 | 88,2327 90 | 89,1227 91 | 90,2077 92 | 91,2646 93 | 92,2310 94 | 93,1275 95 | 94,1150 96 | 95,1294 97 | 96,1068 98 | 97,1830 99 | 98,1909 100 | 99,1367 101 | 100,1866 102 | 101,1336 103 | 102,2186 104 | 103,2014 105 | 104,830 106 | 105,865 107 | 106,1726 108 | 107,1498 109 | 108,742 110 | 109,811 111 | 110,1829 112 | 111,1752 113 | 112,1360 114 | 113,730 115 | 114,806 116 | 115,1868 117 | 116,2202 118 | 117,1756 119 | 118,1069 120 | 119,2033 121 | 120,432 122 | 121,2435 123 | 122,1964 124 | 123,411 125 | 124,2574 126 | 125,1922 127 | 126,728 128 | 127,1289 129 | -------------------------------------------------------------------------------- /Pytorch_code/dpn107/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/dpn131/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/dpn92/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/dpn98/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/nasnet/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/Densenet/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/dense161/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/dense169/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/densenet2/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/drnD105/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/inceptionv4/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/resnet101/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/resnet152/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/seResnext50/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/senet154/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/seresnet152/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/xception/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/resnet50_chair/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/resnext101_32x4d/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/resnext101_64x4d/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/seresnext101/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/inceptionResnetV2/memcached_dataset.py: -------------------------------------------------------------------------------- 1 | import mc 2 | from torch.utils.data import DataLoader, Dataset 3 | import numpy as np 4 | import io 5 | from PIL import Image 6 | 7 | def pil_loader(img_str): 8 | buff = io.BytesIO(img_str) 9 | with Image.open(buff) as img: 10 | img = img.convert('RGB') 11 | return img 12 | 13 | class McDataset(Dataset): 14 | def __init__(self, root_dir, meta_file, transform=None): 15 | self.root_dir = root_dir 16 | self.transform = transform 17 | with open(meta_file) as f: 18 | lines = f.readlines() 19 | print("building dataset from %s"%meta_file) 20 | self.num = len(lines) 21 | self.metas = [] 22 | for line in lines: 23 | path, cls = line.rstrip().split() 24 | self.metas.append((path, int(cls))) 25 | print("read meta done") 26 | 27 | def __len__(self): 28 | return self.num 29 | 30 | def __getitem__(self, idx): 31 | filename = self.root_dir + '/' + self.metas[idx][0] 32 | cls = self.metas[idx][1] 33 | ## memcached 34 | server_list_config_file = "/mnt/lustre/share/memcached_client/server_list.conf" 35 | client_config_file = "/mnt/lustre/share/memcached_client/client.conf" 36 | mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file) 37 | value = mc.pyvector() 38 | mclient.Get(filename, value) 39 | value_str = mc.ConvertBuffer(value) 40 | img = pil_loader(value_str) 41 | #img = np.zeros((350, 350, 3), dtype=np.uint8) 42 | #img = Image.fromarray(img) 43 | #cls = 0 44 | 45 | ## transform 46 | if self.transform is not None: 47 | img = self.transform(img) 48 | return img, cls 49 | -------------------------------------------------------------------------------- /Pytorch_code/dpn107/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/dpn131/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/dpn92/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/dpn98/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/nasnet/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/Densenet/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/dense161/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/dense169/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/densenet2/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/drnD105/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/inceptionv4/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/resnet101/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/resnet152/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/seResnext50/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | -------------------------------------------------------------------------------- /Pytorch_code/senet154/distributed_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.distributed as dist 4 | from torch.nn import Module 5 | import multiprocessing as mp 6 | 7 | class DistModule(Module): 8 | def __init__(self, module): 9 | super(DistModule, self).__init__() 10 | self.module = module 11 | broadcast_params(self.module) 12 | def forward(self, *inputs, **kwargs): 13 | return self.module(*inputs, **kwargs) 14 | def train(self, mode=True): 15 | super(DistModule, self).train(mode) 16 | self.module.train(mode) 17 | 18 | def average_gradients(model): 19 | """ average gradients """ 20 | for param in model.parameters(): 21 | if param.requires_grad: 22 | dist.all_reduce(param.grad.data) 23 | 24 | def broadcast_params(model): 25 | """ broadcast model parameters """ 26 | for p in model.state_dict().values(): 27 | dist.broadcast(p, 0) 28 | 29 | def dist_init(port): 30 | if mp.get_start_method(allow_none=True) != 'spawn': 31 | mp.set_start_method('spawn') 32 | proc_id = int(os.environ['SLURM_PROCID']) 33 | ntasks = int(os.environ['SLURM_NTASKS']) 34 | node_list = os.environ['SLURM_NODELIST'] 35 | num_gpus = torch.cuda.device_count() 36 | torch.cuda.set_device(proc_id%num_gpus) 37 | 38 | if '[' in node_list: 39 | beg = node_list.find('[') 40 | pos1 = node_list.find('-', beg) 41 | if pos1 < 0: 42 | pos1 = 1000 43 | pos2 = node_list.find(',', beg) 44 | if pos2 < 0: 45 | pos2 = 1000 46 | node_list = node_list[:min(pos1,pos2)].replace('[', '') 47 | addr = node_list[8:].replace('-', '.') 48 | print(addr) 49 | 50 | os.environ['MASTER_PORT'] = port 51 | os.environ['MASTER_ADDR'] = addr 52 | os.environ['WORLD_SIZE'] = str(ntasks) 53 | os.environ['RANK'] = str(proc_id) 54 | dist.init_process_group(backend='nccl') 55 | 56 | rank = dist.get_rank() 57 | world_size = dist.get_world_size() 58 | return rank, world_size 59 | --------------------------------------------------------------------------------