Hello,
l want to transform my data set into torchvision format.
l have a numpy array which represents training examples as follow :
l have 15,000 training examples each of224*224*3
so the dimension of my data is dimension=(15.000, 224*224*3
)
and another numpy array which contains lables .Its dimension is (15.000, )
Hence
training_data= (15.000,224*224*3)
training_labels= (15.000,)
My question is as follow :
how can l transform my own data to pytorch format from
training_data= (15.000,224*224*3)
training_labels= (15.000,)
to
traindir = os.path.join(args.data, 'train')
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomSizedCrop(size[0]), #224 , 299
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
labels = len(train_dataset.classes)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, sampler=train_sampler)
so that to make the training in pytorch format
for i, (input, target) in enumerate(train_loader):
if cuda:
input, target = input.cuda(async=True), target.cuda(async=True)
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
Thank you