3020 Enhance what's new for transfomer networks

CHANGELOG.md

@@ -32,7 +32,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 * Deprecated input argument `dimensions` and `ndims`, in favor of `spatial_dims`
 * Updated the Sphinx-based documentation theme for better readability
 * `NdarrayTensor` type is replaced by `NdarrayOrTensor` for simpler annotations
-* Attention-based network blocks now support both 2D and 3D inputs
+* Self-attention-based network blocks now support both 2D and 3D inputs
 
 ### Removed
 * The deprecated `TransformInverter`, in favor of `monai.transforms.InvertD`

docs/source/highlights.md

@@ -58,7 +58,7 @@ transformations. These currently include, for example:
 
 
 ### 3. Transforms support both NumPy array and PyTorch Tensor (CPU or GPU accelerated)
-From MONAI v0.7 we introduced PyTorch `Tensor` based computation in transforms, many transforms already support both `numpy array` and `Tensor` data.
+From MONAI v0.7 we introduced PyTorch `Tensor` based computation in transforms, many transforms already support both `NumPy array` and `Tensor` as input types and computational backends. To get the supported backends of every transform, please execute: `python monai/transforms/utils.py`.
 
 To accelerate the transforms, a common approach is to leverage GPU parallel-computation. Users can first convert input data into GPU Tensor by `ToTensor` or `EnsureType` transform, then the following transforms can execute on GPU based on PyTorch `Tensor` APIs.
 GPU transform tutorial is available at [Spleen fast training tutorial](https://github.com/Project-MONAI/tutorials/blob/master/acceleration/fast_training_tutorial.ipynb).

docs/source/whatsnew_0_7.md

@@ -29,7 +29,7 @@ more](https://github.com/Project-MONAI/tutorials/blob/master/acceleration/fast_t
 
  MONAI starts to roll out major usability enhancements for the
  `monai.transforms` module. Many transforms are now supporting both NumPy and
- PyTorch, as input types and computational backends.
+ PyTorch, as input types and computational backends. To get the supported backends of every transform, please execute: `python monai/transforms/utils.py`.
 
 One benefit of these enhancements is that the users can now better leverage the
 GPUs for preprocessing. By transferring the input data onto GPU using