Adds `experiment_config_json` for setting custom configurations with a json.

run_pretrained_openfold.py

@@ -20,6 +20,7 @@ import os
 import pickle
 import random
 import time
+import json
 
 logging.basicConfig()
 logger = logging.getLogger(__file__)
@@ -179,6 +180,11 @@ def main(args):
 
     config = model_config(args.config_preset, long_sequence_inference=args.long_sequence_inference)
 
+    if args.experiment_config_json: 
+        with open(args.experiment_config_json, 'r') as f:
+            custom_config_dict = json.load(f)
+        config.update_from_flattened_dict(custom_config_dict)
+
     if args.trace_model:
         if not config.data.predict.fixed_size:
             raise ValueError(
@@ -452,6 +458,9 @@ if __name__ == "__main__":
         "--cif_output", action="store_true", default=False,
         help="Output predicted models in ModelCIF format instead of PDB format (default)"
     )
+    parser.add_argument(
+        "--experiment_config_json", default="", help="Path to a json file with custom config values to overwrite config setting",
+    )
     add_data_args(parser)
     args = parser.parse_args()
 

train_openfold.py

@@ -2,6 +2,7 @@ import argparse
 import logging
 import os
 import sys
+import json
 
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks.lr_monitor import LearningRateMonitor
@@ -39,7 +40,6 @@ from scripts.zero_to_fp32 import (
     get_fp32_state_dict_from_zero_checkpoint,
     get_global_step_from_zero_checkpoint
 )
-from scripts.zero_to_fp32 import get_optim_files, parse_optim_states, get_model_state_file
 
 from openfold.utils.logger import PerformanceLoggingCallback
 
@@ -59,6 +59,7 @@ class OpenFoldWrapper(pl.LightningModule):
         
         self.cached_weights = None
         self.last_lr_step = -1
+        self.save_hyperparameters
 
     def forward(self, batch):
         return self.model(batch)
@@ -280,6 +281,11 @@ def main(args):
         train=True, 
         low_prec=(str(args.precision) == "16")
     ) 
+    if args.experiment_config_json: 
+        with open(args.experiment_config_json, 'r') as f:
+            custom_config_dict = json.load(f)
+        config.update_from_flattened_dict(custom_config_dict)
+
     model_module = OpenFoldWrapper(config)
 
     if args.resume_from_ckpt:
@@ -611,6 +617,9 @@ if __name__ == "__main__":
         "--distillation_alignment_index_path", type=str, default=None,
         help="Distillation alignment index. See the README for instructions."
     )
+    parser.add_argument(
+        "--experiment_config_json", default="", help="Path to a json file with custom config values to overwrite config setting",
+    )
     parser = pl.Trainer.add_argparse_args(parser)
    
     # Disable the initial validation pass