reverse back to multimer branch version

openfold/config.py

@@ -432,7 +432,7 @@ config = mlc.ConfigDict(
                 "use_small_bfd": False,
                 "data_loaders": {
                     "batch_size": 1,
-                    "num_workers": 1,
+                    "num_workers": 16,
                     "pin_memory": True,
                 },
             },
@@ -764,7 +764,7 @@ multimer_config_update = mlc.ConfigDict({
                 ],
                 "true_msa": [NUM_MSA_SEQ, NUM_RES]
             },
-            "max_recycling_iters": 1,  # For training, value is 3
+            "max_recycling_iters": 20,  # For training, value is 3
             "unsupervised_features": [
                     "aatype",
                     "residue_index",
@@ -799,7 +799,7 @@ multimer_config_update = mlc.ConfigDict({
         "train": {
             "max_msa_clusters": 508,
             "max_extra_msa": 2048,
-            "crop_size": 32,
+            "crop_size": 640,
             "spatial_crop_prob": 0.5,
             "interface_threshold": 10.,
             "clamp_prob": 1.,

openfold/data/data_modules.py

@@ -195,6 +195,7 @@ class OpenFoldSingleDataset(torch.utils.data.Dataset):
             alignment_index=alignment_index,
             seqemb_mode=self.config.seqemb_mode.enabled
         )
+
         return data
 
     def chain_id_to_idx(self, chain_id):
@@ -422,21 +423,24 @@ class OpenFoldSingleMultimerDataset(torch.utils.data.Dataset):
     def _parse_mmcif(self, path, file_id, alignment_dir, alignment_index):
         with open(path, 'r') as f:
             mmcif_string = f.read()
-        import time
+
         mmcif_object = mmcif_parsing.parse(
             file_id=file_id, mmcif_string=mmcif_string
         )
+
         # Crash if an error is encountered. Any parsing errors should have
         # been dealt with at the alignment stage.
         if mmcif_object.mmcif_object is None:
             raise list(mmcif_object.errors.values())[0]
 
         mmcif_object = mmcif_object.mmcif_object
+
         data = self.data_pipeline.process_mmcif(
             mmcif=mmcif_object,
             alignment_dir=alignment_dir,
             alignment_index=alignment_index
         )
+
         return data
 
     def mmcif_id_to_idx(self, mmcif_id):
@@ -450,8 +454,6 @@ class OpenFoldSingleMultimerDataset(torch.utils.data.Dataset):
         alignment_index = None
 
         if self.mode == 'train' or self.mode == 'eval':
-            import time 
-            start = time.time()
             path = os.path.join(self.data_dir, f"{mmcif_id}")
             ext = None
             for e in self.supported_exts:
@@ -476,6 +478,7 @@ class OpenFoldSingleMultimerDataset(torch.utils.data.Dataset):
                 fasta_path=path,
                 alignment_dir=self.alignment_dir
             )
+
         if self._output_raw:
             return data
 
@@ -738,7 +741,9 @@ class OpenFoldMultimerDataset(OpenFoldDataset):
                 generator=self.generator,
             )
             samples = samples.squeeze()
+
             cache = [i for i, s in zip(idx, samples) if s]
+
             for datapoint_idx in cache:
                 yield datapoint_idx
 

openfold/data/parsers.py

@@ -20,7 +20,7 @@ import itertools
 import re
 import string
 from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Set
-import asyncio
+
 
 DeletionMatrix = Sequence[Sequence[int]]
 
@@ -120,9 +120,9 @@ def parse_stockholm(stockholm_string: str) -> Msa:
         line = line.strip()
         if not line or line.startswith(("#", "//")):
             continue
-        name, sequence = line.split(maxsplit=1)
+        name, sequence = line.split()
         if name not in name_to_sequence:
-            name_to_sequence.setdefault(name,"")
+            name_to_sequence[name] = ""
         name_to_sequence[name] += sequence
 
     msa = []

train_openfold.py

@@ -42,9 +42,6 @@ from scripts.zero_to_fp32 import (
 
 from openfold.utils.logger import PerformanceLoggingCallback
 
-def calculate_elapse(start, end):
-    elapse = end - start
-    print(f"this function runs {round(elapse,3)} seconds i.e. {round(elapse/60, 3)} minutes")
 
 class OpenFoldWrapper(pl.LightningModule):
     def __init__(self, config):