add all query masks

README.md

@@ -39,18 +39,30 @@ trainval_balanced_programs.json
 > GQA dictionaries: `gqa_all_attributes.json` and `gqa_all_vocab_classes` are also adapted from [https://github.com/wenhuchen/Meta-Module-Network](https://github.com/wenhuchen/Meta-Module-Network)
 
 ## Generate Query Masks 
-- generates full_relations_df.pkl if not already present
-- generates query masks for all relations with more than 1000 samples
+> All 37 generated query masks are available in [relations.zip](relations.zip) as numpy files 
+
+```python
+# loading a query mask with numpy
+import numpy as np 
+rel = 'to_the_right_of'
+path = f'relations/{rel}.npy'
+mask = np.load(path)
+mask = mask > 0.05 	# binary mask
+```
+If you want to run the generation process, run: 
 ```shell 
 python generate_query_masks.py 
 ``` 
+- generates full_relations_df.pkl if not already present
+- generates query masks for all relations with more than 1000 samples
+
 
 ## Pipeline 
 Execute Pipeline for all samples in GQA: train_balanced (with `TEST=False`) or validation_balanced (with `TEST=True`)
 ```shell 
 python run_programs.py 
 ``` 
-For visualizing samples with full all pipeline steps see [VSA4VQA_examples.ipynb](VSA4VQA_examples.ipynb) <br>
+For visualizing samples with all pipeline steps see [VSA4VQA_examples.ipynb](VSA4VQA_examples.ipynb) <br>
 
 ## Citation 
 Please consider citing this paper if you use VSA4VQA or parts of this publication in your research: