import os
import torch
import numpy as np

torch.manual_seed(20101014)
np.random.seed(20191001)

USE_MULTIPLE_GPUS = True

NUMBER_OF_CLASSES = 10
DATA_PREPROCESSING = "IMAGENET"

if USE_MULTIPLE_GPUS:
    os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3,4,5,6"
    DEVICE = torch.device("cuda")
    BATCH_SIZE = 500
else:
    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    BATCH_SIZE = 1
f4128b0c6fca8593c3715fb22bc84246142f2084
<mask token>

<mask token>
torch.manual_seed(20101014)
np.random.seed(20191001)
<mask token>
if USE_MULTIPLE_GPUS:
    os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5,6'
    DEVICE = torch.device('cuda')
    BATCH_SIZE = 500
else:
    DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    BATCH_SIZE = 1

<mask token>
torch.manual_seed(20101014)
np.random.seed(20191001)
USE_MULTIPLE_GPUS = True
NUMBER_OF_CLASSES = 10
DATA_PREPROCESSING = 'IMAGENET'
if USE_MULTIPLE_GPUS:
    os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5,6'
    DEVICE = torch.device('cuda')
    BATCH_SIZE = 500
else:
    DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    BATCH_SIZE = 1

import os
import torch
import numpy as np
torch.manual_seed(20101014)
np.random.seed(20191001)
USE_MULTIPLE_GPUS = True
NUMBER_OF_CLASSES = 10
DATA_PREPROCESSING = 'IMAGENET'
if USE_MULTIPLE_GPUS:
    os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5,6'
    DEVICE = torch.device('cuda')
    BATCH_SIZE = 500
else:
    DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    BATCH_SIZE = 1

import os
import torch
import numpy as np

torch.manual_seed(20101014)
np.random.seed(20191001)

USE_MULTIPLE_GPUS = True

NUMBER_OF_CLASSES = 10
DATA_PREPROCESSING = "IMAGENET"

if USE_MULTIPLE_GPUS:
    os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3,4,5,6"
    DEVICE = torch.device("cuda")
    BATCH_SIZE = 500
else:
    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    BATCH_SIZE = 1