SafeDataset cann't wrapper different dataset

trainset_ = my_data((140,224),transform=image_transform)
testset_ = my_data((140,224),image_set='val',transform=image_transform)

trainset = nc.SafeDataset(trainset_)
testset = nc.SafeDataset(testset_)

Before executing trainset=nc.SafeDataset(trainset_), I try to plot the first image in test by (plt.imshow(testset_[0][0].permute(1,2,0)), so I got the first image in valset which is right. After execute trainset=nc.SafeDataset(trainset_) , the (plt.imshow(testset_[0][0].permute(1,2,0)) shows me the first image in trainset which is wrong. I tried to check the image path in testset object, it’ still the the path of first image in valset. Could you give me some suggestion to solve this problem?

from PIL import Image as image
import torch
import numpy as np
import torchvision.transforms.functional as TF
import torchvision.transforms as transforms
import os
import torchvision.datasets as dset
voc_colormap = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],
                [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],
                [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],
                [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],
                [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],
                [0, 64, 128]]
class my_data(torch.utils.data.Dataset):
    #if target_transform=mask_transform is
    def __init__(self,data_size,root='data',image_set='train',transform=None,target_transform=None):
        self.shape=data_size
        self.root=os.path.expanduser(root)
        self.transform=transform
        self.target_transform=target_transform
        self.image_set=image_set
        voc_dir=os.path.join(self.root,'VOCdevkit/VOC2012')
        image_dir=os.path.join(voc_dir,'JPEGImages')
        mask_dir=os.path.join(voc_dir,'SegmentationClass')
        splits_dir=os.path.join(voc_dir,'ImageSets/Segmentation')
        splits_f=os.path.join(splits_dir, self.image_set + '.txt')
        with open(os.path.join(splits_f),'r') as f:
            file_name=[x.strip() for x in f.readlines()]
        self.image=[os.path.join(image_dir,x+'.jpg') for x in file_name]
        self.mask=[os.path.join(mask_dir,x+'.png') for x in file_name]
        assert (len(self.image)==len(self.mask))

        self.class_index=np.zeros(256**3)
        for i,j in enumerate(voc_colormap):
            tmp=(j[0]*256+j[1])*256+j[2]
            self.class_index[tmp]=i
    def __getitem__(self, index):
        img=image.open(self.image[index]).convert('RGB')
        target=image.open(self.mask[index]).convert('RGB')
        i,j,h,w=transforms.RandomCrop.get_params(img,self.shape)
        # if i<0 or j<0 or h <0 or w<0:
        #     return None,None
        img=TF.crop(img,i,j,h,w)
        target=TF.crop(target,i,j,h,w)
        if  self.target_transform is not None:
            return self.transform(img),self.target_transform(target)
        target=np.array(target).transpose(2,0,1).astype(np.int32)
        target=(target[0]*256+target[1])*256+target[2]
        target=self.class_index[target]
        return self.transform(img),target

    def __len__(self):
        return len(self.image)

[eejackliu]

Can you verify if the latest commit fixes the problem? 37eee52

Yes! It works! Thanks for your repo!

[msamogh]

Can you verify if the latest commit fixes the problem? 37eee520c25595ce7fe71432a34cdaea27712b1d