added forward compatibility with cutorch. updated readme

README.md

@@ -13,14 +13,14 @@ Install Torch requirements:
 ```bash
 luarocks install torchx
 ```
-* [npy4th](https://github.com/htwaijry/npy4th)
+* [npy4th](https://github.com/htwaijry/npy4th) (You may need to checkout commit from 5-10-16)
 ```bash
 git clone https://github.com/htwaijry/npy4th.git
 cd npy4th
 luarocks make
 ```
 
-Install Python requirements:
+Install Python requirements if needed:
 * [numpy](http://www.numpy.org/)
 * [scipy](https://www.scipy.org/install.html)
 * [matplotlib](http://matplotlib.org/users/installing.html)
@@ -29,40 +29,40 @@ Install Python requirements:
 Install the Spatial Tranformer module provided:
 ```bash
 cd stnbhwd-master
-CODE FOR INSTALLING
+luarocks make
 ```
 It is a modification of the code from [Spatial Transformer Network (Jaderberg et al.)](https://github.com/qassemoquab/stnbhwd) and includes a Thin Plate Spline grid generator layer.
 
 ##Dataset
-Download the [Horse Dataset](https://www.dropbox.com/s/9t770jhcjqo3mmg/release_data.zip?dl=0) (580 MB)
+Download the [Horse Dataset](https://www.dropbox.com/s/9t770jhcjqo3mmg/release_data.zip) (580 MB)
 Run the following commands
 ```bash
 cd data
 unzip <path to data zip file>
 ```
 
 ##Models
-To download all the pretrained and untrained models go [here](amend) (8.6 GB)
+To download all the pretrained and untrained models go [here](https://www.dropbox.com/s/4r2ghpwoncq656t/release_models.zip) (8.6 GB)
 Run the following commands
 ```bash
 cd models
 unzip <path to models zip file>
 ```
 Otherwise add the individual models to *models/*
-[Full model for horses with tps warping](LINK)(4.4 GB)
-[Full model for horses with affine warping](LINK)(4.4 GB)
-[TPS Warping model for horses](LINK)(738 MB)
-[Affine Warping model for horses](LINK)(764 MB)
-[Keypoint network trained on human faces](LINK)(3.4 GB)
-[Untrained TPS Warping model](LINK)(111 MB)
-[Untrained Affine Warping model](LINK)(121 MB)
+* [Full model for horses with tps warping](https://www.dropbox.com/s/3tc6qxqv9qw3suq/horse_full_model.dat)(4.4 GB)
+* [Full model for horses with affine warping](https://www.dropbox.com/s/crg4ss6bbn0pt0n/horse_full_model_affine.dat)(4.4 GB)
+* [TPS Warping model for horses](https://www.dropbox.com/s/penbxkrpu9on0js/horse_tps_model.dat)(738 MB)
+* [Affine Warping model for horses](https://www.dropbox.com/s/1c6ujbh49ye8j61/horse_affine_model.dat)(764 MB)
+* [Keypoint network trained on human faces](https://www.dropbox.com/s/u3l2qrmke66t62n/human_face_model.dat)(3.4 GB)
+* [Untrained TPS Warping model](https://www.dropbox.com/s/19twyz9865zrz2a/tps_localization_net_untrained.dat)(111 MB)
+* [Untrained Affine Warping model](https://www.dropbox.com/s/byxphqqmzrkstpa/affine_localization_net_untrained.dat)(121 MB)
 
 ##Testing
 To test pretrained model run the following commands
 ```bash
 cd torch
-th test.th out_dir_images <path to results directory>
-python ../python visualize_results.py --test_dir <path to results directory> 
+th test.th -out_dir_images <path to results directory>
+python ../python/visualize_results.py --test_dir <path to results directory> 
 ```
 after replacing <path to results directory> with the path to the folder where you would like the output images to be saved.