Hello,
I am using custom Dataset class, but the problem is that when I get the data from the Dataloader I am left with array that has different tensor shape than I want.
shape that I get: torch.Size([1, 56, 128, 128])
shape that I want: torch.Size([1, 56, 1, 128, 128])
my approach was to:
-
to apply
numpy.expand_dimson thearrayand gettorch.Size([1, 1, 56, 128, 128]) -
then to
np.transposeon thearrayto get the shape I wanttorch.Size([1, 56, 1, 128, 128])
after first step I am getting the error:
raise ValueError('pic should be 2/3 dimensional. Got {} dimensions.'.format(pic.ndim))
ValueError: pic should be 2/3 dimensional. Got 4 dimensions.
if I do the transpose first, none of the combination of np.transpose(array, axes=(1,2,0)) yields the shape torch.Size([56, 1, 128, 128])
if I convert array to Tensor first and then do torch.unsqueeze I get the error:
raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))
TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
here is my code:
class patientdataset(Dataset):
def __init__(self, csv_file, root_dir, transform=None):
self.annotations = pd.read_csv(csv_file)
self.root_dir = root_dir
self.transform = transform
def __len__(self):
return len(self.annotations)
def __getitem__(self, index):
img_path = os.path.join(self.root_dir, self.annotations.iloc[index,0])
# np_load_old = np.load
# np.load = lambda *a, **k: np_load_old(*a, allow_pickle=True, **k)
image= np.asarray(np.load(img_path))
image= np.transpose(image, axes=(1,2,0))
image = torch.Tensor (image)
image = torch.unsqueeze(image, dim=1)
y_label = torch.tensor(np.asarray(self.annotations.iloc[index,1]))
if self.transform:
image = self.transform(image)
return (image, y_label)