Hello
I load MNIST dataset using torchvision, then I’ve tried to access some special indexes like below
train_dataset = MNIST(’…/data/MNIST’, train=True, download=True,transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((mean,), (std,))]))
mini_train_dataset = torch.utils.data.Subset(train_dataset, indices=range(50000))
triplet_train_dataset = TripletMNIST(mini_train_dataset) # Returns triplets of images
where TripletMNIST is:
class TripletMNIST(Dataset):
def __init__(self, mnist_dataset):
self.mnist_dataset = mnist_dataset
*self.train = self.mnist_dataset.train* #here I got this error ('Subset' object has no attribute 'train')
self.transform = self.mnist_dataset.transform
if self.train:
self.train_labels = self.mnist_dataset.train_labels
self.train_data = self.mnist_dataset.train_data
self.labels_set = set(self.train_labels.numpy())
self.label_to_indices = {label: np.where(self.train_labels.numpy() == label)[0]
for label in self.labels_set}
else:
self.test_labels = self.mnist_dataset.test_labels
self.test_data = self.mnist_dataset.test_data
# generate fixed triplets for testing
self.labels_set = set(self.test_labels.numpy())
self.label_to_indices = {label: np.where(self.test_labels.numpy() == label)[0]
for label in self.labels_set}
random_state = np.random.RandomState(29)
triplets = [[i,
random_state.choice(self.label_to_indices[self.test_labels[i].item()]),
random_state.choice(self.label_to_indices[np.random.choice(list(self.labels_set - set([self.test_labels[i].item()]))
)
])
]
for i in range(len(self.test_data))]
self.test_triplets = triplets
def __getitem__(self, index):
if self.train:
img1, label1 = self.train_data[index], self.train_labels[index].item()
positive_index = index
while positive_index == index:
positive_index = np.random.choice(self.label_to_indices[label1])
negative_label = np.random.choice(list(self.labels_set - set([label1])))
negative_index = np.random.choice(self.label_to_indices[negative_label])
img2 = self.train_data[positive_index]
img3 = self.train_data[negative_index]
else:
img1 = self.test_data[self.test_triplets[index][0]]
img2 = self.test_data[self.test_triplets[index][1]]
img3 = self.test_data[self.test_triplets[index][2]]
img1 = Image.fromarray(img1.numpy(), mode='L')
img2 = Image.fromarray(img2.numpy(), mode='L')
img3 = Image.fromarray(img3.numpy(), mode='L')
if self.transform is not None:
img1 = self.transform(img1)
img2 = self.transform(img2)
img3 = self.transform(img3)
return (img1, img2, img3), []
def __len__(self):
return len(self.mnist_dataset)
how can i handel this error?
self.train = self.mnist_dataset.train
this line gives an error
