chouse_train/datasetmaker.py

from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, datasets
import os
import sys

class TrainDataClass(Dataset):
    def __init__(self, image_path, transform=None):
        super(TrainDataClass, self).__init__()
        self.data = datasets.ImageFolder(os.path.join(image_path,'train'), transform)
    
    def __getitem__(self,idx):
        x,y = self.data[idx]
        return x,y
    def __len__(self):
        return len(self.data)

class TestDataClass(Dataset):
    def __init__(self, image_path, transform=None):
        super(TestDataClass, self).__init__()
        self.data = datasets.ImageFolder(os.path.join(image_path,'test'), transform)
    
    def __getitem__(self,idx):
        x,y = self.data[idx]
        return x,y
    def __len__(self):
        return len(self.data)




#if __name__ == "__main__":
    #NOTE: we don't have to transform because we already shrink raw images as same as MoblieNetV2's input size
    #TODO: PLEASE ERASE WHEN YOU REFRACORING!!
    #dataset_root = 'C:\\Users\\atari\\workspace\\chouse_train\\datasets\\201118\\middle'
    #classes = tuple(os.listdir(os.path.join(dataset_root,'train')))
#
    #tsfm=transforms.Compose([
    #    transforms.ToTensor()
    #])
#
    #train = TrainDataClass(dataset_root, tsfm)
    #trainloader = DataLoader(train, batch_size=512, shuffle=True)
#
    #test = TestDataClass(dataset_root, tsfm)
    #testloader = DataLoader(test, batch_size=512, shuffle=True)
#
#
    #dataiter = iter(trainloader)
    #images,labels = dataiter.next()
    #print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(512)))