I am trying to create a custom transformation to part of the CIFAR10 data set which superimposing of an image over the dataset. I was able to download the data and divide it into subsets. Using the following code:

```
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
traindata = datasets.CIFAR10('./data', train=True, download=True,
transform= transform_train)
partitions = 5
traindata_split = torch.utils.data.random_split(traindata, [int(traindata.data.shape[0] / partitions) for _ in range(partitions)])
```

then I wanted to modify part of the splits so I created the following class and functions to use as as follows:

```
class MyDataset(Dataset): # https://discuss.pytorch.org/t/torch-utils-data-dataset-random-split/32209/3
def __init__(self, subset, transform=None):
self.subset = subset
self.transform = transform
def __getitem__(self, index):
x, y = self.subset[index]
if self.transform:
x = self.transform(x)
return x, y
def __len__(self):
return len(self.subset)
```

and

```
class ImageSuperImpose(object):
""" Image input as PIL and output as PIL
To be used as part of torchvision.transforms
Args: p, a threshold value to control image thinning
"""
def __init__(self, p=0):
self.p = p
def __call__(self, image):
img = cv2.imread('img.jpg')
img = img('float32')/255
imgSm = cv2.resize(img,(32,32))
np_arr = image.cpu().detach().numpy().T
sample = cv2.addWeighted(np_arr, 1, imgSm, 1, 0)
sample = sample.T
t = torch.from_numpy(sample)
return sample
transform_train2 = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
ImagePoisoning(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
datasetA = MyDataset(
traindata_split[0], transform= transform_train2
)
test_loader = torch.utils.data.DataLoader(datasetA, batch_size=128, shuffle=True)
```

But when I tried to train the model on the subset I got the following error:

`RuntimeError: The size of tensor a (32) must match the size of tensor b (3) at non-singleton dimension 0`