move uniform_recycling

openfold/config.py

@@ -174,7 +174,6 @@ config = mlc.ConfigDict(
             },
             "supervised": {
                 "clamp_prob": 0.9,
-                "uniform_recycling": True,
                 "supervised_features": [
                     "all_atom_mask",
                     "all_atom_positions",
@@ -194,6 +193,7 @@ config = mlc.ConfigDict(
                 "crop_size": None,
                 "supervised": False,
                 "subsample_recycling": False,
+                "uniform_recycling": False,
             },
             "eval": {
                 "fixed_size": True,
@@ -206,6 +206,7 @@ config = mlc.ConfigDict(
                 "crop_size": None,
                 "supervised": True,
                 "subsample_recycling": False,
+                "uniform_recycling": False,
             },
             "train": {
                 "fixed_size": True,
@@ -221,6 +222,7 @@ config = mlc.ConfigDict(
                 "clamp_prob": 0.9,
                 "subsample_recycling": True,
                 "max_distillation_msa_clusters": 1000,
+                "uniform_recycling": True,
             },
             "data_module": {
                 "use_small_bfd": False,

openfold/data/data_modules.py

@@ -284,18 +284,16 @@ class OpenFoldDataLoader(torch.utils.data.DataLoader):
                 ("use_clamped_fape", [1 - clamp_prob, clamp_prob])
             )
 
-        if(self.stage == "train" and self.config.supervised.uniform_recycling):
+        if(stage_cfg.uniform_recycling):
             recycling_probs = [
                 1. / (max_iters + 1) for _ in range(max_iters + 1)
             ]
-            keyed_probs.append(
-                ("no_recycling_iters", recycling_probs)
-            )
         else:
             recycling_probs = [
                 0. for _ in range(max_iters + 1)
             ]
             recycling_probs[-1] = 1.
+
         keyed_probs.append(
             ("no_recycling_iters", recycling_probs)
         )