Fix links to documentation (#1354)

Author: fepegar

src/torchio/data/image.py

@@ -121,8 +121,8 @@ class Image(dict):
         >>> image = tio.ScalarImage('t1.npy', reader=numpy_reader)
 
     .. _lazy loaders: https://en.wikipedia.org/wiki/Lazy_loading
-    .. _preprocessing: https://torchio-project.github.io/torchio/transforms/preprocessing.html#intensity
-    .. _augmentation: https://torchio-project.github.io/torchio/transforms/augmentation.html#intensity
+    .. _preprocessing: https://docs.torchio.org/transforms/preprocessing.html#intensity
+    .. _augmentation: https://docs.torchio.org/transforms/augmentation.html#intensity
     .. _NiBabel docs: https://nipy.org/nibabel/image_orientation.html
     .. _NiBabel docs on coordinates: https://nipy.org/nibabel/coordinate_systems.html#the-affine-matrix-as-a-transformation-between-spaces
     .. _3D Slicer wiki: https://www.slicer.org/wiki/Coordinate_systems

src/torchio/transforms/data_parser.py

@@ -75,7 +75,7 @@ def get_subject(self):
                     'If the input is a dictionary, a value for "include" must'
                     ' be specified when instantiating the transform. See the'
                     ' docs for Transform:'
-                    ' https://torchio-project.github.io/torchio/transforms/transforms.html#torchio.transforms.Transform'
+                    ' https://docs.torchio.org/transforms/transforms.html#torchio.transforms.Transform'
                 )
                 raise RuntimeError(message)
             subject = self._get_subject_from_dict(

src/torchio/transforms/preprocessing/spatial/crop_or_pad.py

@@ -59,7 +59,7 @@ class CropOrPad(SpatialTransform):
 
     .. warning:: If :attr:`target_shape` is ``None``, subjects in the dataset
         will probably have different shapes. This is probably fine if you are
-        using `patch-based training <https://torchio-project.github.io/torchio/patches/index.html>`_.
+        using `patch-based training <https://docs.torchio.org/patches/index.html>`_.
         If you are using full volumes for training and a batch size larger than
         one, an error will be raised by the :class:`~torch.utils.data.DataLoader`
         while trying to collate the batches.