Rename runner, update command in README

README.md

@@ -49,9 +49,26 @@ To run inference on a sequence using a set of DeepMind's pretrained parameters,
 run e.g.
 
 ```bash
-python3 run_pretrained_alphafold.py --device cuda:1 --model model_1_ptm
+python3 run_pretrained_openfold.py \
+        test.fasta \
+        data/uniref90/uniref90.fasta \
+        data/mgnify/mgy_clusters_2018_12.fa \
+        data/pdb70/pdb70 \
+        data/pdb_mmcif/mmcif_files/ \
+        --output_dir ./ \
+        --bfd_database_path data/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
+        --uniclust30_database_path data/uniclust30/uniclust30_2018_08/uniclust30_2018_08 \
+        --device cuda:1
 ```
 
+where `data` is a directory populated by `scripts/download_all_data.sh`. Run
+
+```bash
+python3 run_pretrained_openfold.py --help
+```
+
+for a full list of options.
+
 ## Copyright notice
 
 While AlphaFold's and, by extension, OpenFold's source code is licensed under

run_pretrained_alphafold.py → run_pretrained_openfold.py

@@ -95,20 +95,23 @@ def main(args):
     if not os.path.exists(alignment_dir):
         os.makedirs(alignment_dir)
 
-    print("Collecting data...")
-    alignment_runner.run_from_fasta(
+    print("Generating features...")
+    alignment_runner.run(
         args.fasta_path, alignment_dir
     )     
 
     feature_dict = data_processor.process_fasta(
-        input_fasta_path=args.fasta_path, alignment_dir=alignment_dir
+        fasta_path=args.fasta_path, alignment_dir=alignment_dir
     )
 
-    print("Generating features...")
     processed_feature_dict = feature_processor.process_features(
         feature_dict, random_seed
     )
 
+    for k, v in processed_feature_dict.items():
+        print(k)
+        print(v.shape)
+
     print("Executing model...")
     batch = processed_feature_dict
     with torch.no_grad():