transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
test_dataset = datasets.ImageFolder(args.data_dir,
train=False,
download=False,
transform=test_transform)
train_pics = 121187
elif args.model == 'Abalone':
model = ResNetOnCifar10.abalone_model()
test_dataset = mldatasets.abalone(args.data_dir, False)
train_pics = 3342
elif args.model == "Bodyfat":
model = ResNetOnCifar10.bodyfat_model()
test_dataset = mldatasets.bodyfat(args.data_dir, False)
train_pics = 202
elif args.model == 'Housing':
model = ResNetOnCifar10.housing_model()
test_dataset = mldatasets.housing(args.data_dir, False)
train_pics = 405
else:
print('Model must be {} or {}!'.format('MnistCNN', 'AlexNet'))
sys.exit(-1)
train_bsz = args.train_bsz
train_bsz /= len(workers)
world_size = args.workers_num + 1
this_rank = args.this_rank
test_transform = train_transform
train_dataset = datasets.ImageFolder(args.data_dir,
train=True,
download=False,
transform=train_transform)
test_dataset = datasets.ImageFolder(args.data_dir,
train=False,
download=False,
transform=test_transform)
elif args.model == 'Abalone':
model = ResNetOnCifar10.abalone_model()
train_dataset = mldatasets.abalone(args.data_dir, True)
test_dataset = mldatasets.abalone(args.data_dir, False)
elif args.model == "Bodyfat":
model = ResNetOnCifar10.bodyfat_model()
train_dataset = mldatasets.bodyfat(args.data_dir, True)
test_dataset = mldatasets.bodyfat(args.data_dir, False)
elif args.model == 'Housing':
model = ResNetOnCifar10.housing_model()
train_dataset = mldatasets.housing(args.data_dir, True)
test_dataset = mldatasets.housing(args.data_dir, False)
else:
print('Model must be {} or {}!'.format('MnistCNN', 'AlexNet'))
sys.exit(-1)
train_bsz = args.train_bsz
test_bsz = 400
train_bsz /= len(workers)
train_bsz = int(train_bsz)