Finish setup.py and READMEs.

README.md

@@ -41,7 +41,7 @@ combining the FC-Loss, see Section 3.2 in the paper, and a conventional MSE-loss
 
 ## Installation
 
-We use [fast-transformers]() as underlying transformer implementation. In our super-resolution experiments we use their
+We use [fast-transformers](https://github.com/idiap/fast-transformers) as underlying transformer implementation. In our super-resolution experiments we use their
 `causal-linear` implementation, which uses custom CUDA code (prediction works without this custom code). This code is
 compiled during the installation of fast-transformers and it is necessary that CUDA and NVIDIA driver versions match.
 For our experiments we used CUDA 10.2 and NVIDIA driver 440.118.02.
@@ -55,14 +55,18 @@ Next activate the new environment.:
 
 `conda activate fit`
 
-Now we have to install the `astra-toolbox`:
-
-`conda install -c astra-toolbox/label/dev astra-toolbox`
-
 Then we install PyTorch for CUDA 10.2:
 
 `conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch`
 
+Followed by installing fast-transformers:
+
+`pip install --user pytorch-fast-transformers`
+
+Now we have to install the `astra-toolbox`:
+
+`conda install -c astra-toolbox/label/dev astra-toolbox`
+
 And finally we install Fourier Image Transformer:
 
 `pip install fourier-image-transformer`

setup.py

@@ -33,8 +33,6 @@
           'Topic :: Scientific/Engineering',
           'License :: OSI Approved :: BSD License',
 
-          'Programming Language :: Python :: 3.5',
-          'Programming Language :: Python :: 3.6',
           'Programming Language :: Python :: 3.7',
       ],
 

singularity/README.md

@@ -7,7 +7,7 @@ Build Python package:
 `python setup.py bdist_wheel`
 
 Build singularity recipe:
-`neurodocker generate singularity -b nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04 -p apt --copy ./dist/fourier_image_transformer-0.2.0-py3-none-any.whl /fourier_image_transformer-0.2.0-py3-none-any.whl --miniconda create_env=fit conda_install='python=3.7 astra-toolbox pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch -c astra-toolbox/label/dev' pip_install='/fourier_image_transformer-0.2.0-py3-none-any.whl' activate=true --entrypoint "/neurodocker/startup.sh python" > singularity/v0.2.0.Singularity`
+`neurodocker generate singularity -b nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04 -p apt --copy ./dist/fourier_image_transformer-0.2.0-py3-none-any.whl /fourier_image_transformer-0.2.0-py3-none-any.whl --miniconda create_env=fit conda_install='python=3.6 astra-toolbox pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch -c astra-toolbox/label/dev' pip_install='/fourier_image_transformer-0.2.0-py3-none-any.whl' activate=true --entrypoint "/neurodocker/startup.sh python" > singularity/v0.2.0.Singularity`
 
 Build singularity container:
 `sudo singularity build singularity/fit_v0.2.0.simg singularity/v0.2.0.Singularity`