Add flexible load for lr scheduler.

gluefactory/train.py

@@ -48,11 +48,12 @@
     "optimizer_options": {},  # optional arguments passed to the optimizer
     "lr": 0.001,  # learning rate
     "lr_schedule": {
-        "type": None,
+        "type": None,  # can be any of [factor, exp] or torch.optim.lr_scheduler
         "start": 0,
         "exp_div_10": 0,
         "on_epoch": False,
         "factor": 1.0,
+        "options": None,  # add lr_scheduler arguments here
     },
     "lr_scaling": [(100, ["dampingnet.const"])],
     "eval_every_iter": 1000,  # interval for evaluation on the validation set
@@ -141,6 +142,26 @@ def filter_fn(x):
     return params
 
 
+def get_lr_scheduler(optimizer, conf):
+    """Get lr scheduler specified by conf.train.lr_schedule."""
+    if conf.type not in ["factor", "exp", None]:
+        return getattr(torch.optim.lr_scheduler, conf.type)(optimizer, **conf.options)
+
+    # backward compatibility
+    def lr_fn(it):  # noqa: E306
+        if conf.type is None:
+            return 1
+        if conf.type == "factor":
+            return 1.0 if it < conf.start else conf.factor
+        if conf.type == "exp":
+            gam = 10 ** (-1 / conf.exp_div_10)
+            return 1.0 if it < conf.start else gam
+        else:
+            raise ValueError(conf.type)
+
+    return torch.optim.lr_scheduler.MultiplicativeLR(optimizer, lr_fn)
+
+
 def pack_lr_parameters(params, base_lr, lr_scaling):
     """Pack each group of parameters with the respective scaled learning rate."""
     filters, scales = tuple(zip(*[(n, s) for s, names in lr_scaling for n in names]))
@@ -310,22 +331,7 @@ def sigint_handler(signal, frame):
 
     results = None  # fix bug with it saving
 
-    def lr_fn(it):  # noqa: E306
-        if conf.train.lr_schedule.type is None:
-            return 1
-        if conf.train.lr_schedule.type == "factor":
-            return (
-                1.0
-                if it < conf.train.lr_schedule.start
-                else conf.train.lr_schedule.factor
-            )
-        if conf.train.lr_schedule.type == "exp":
-            gam = 10 ** (-1 / conf.train.lr_schedule.exp_div_10)
-            return 1.0 if it < conf.train.lr_schedule.start else gam
-        else:
-            raise ValueError(conf.train.lr_schedule.type)
-
-    lr_scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, lr_fn)
+    lr_scheduler = get_lr_scheduler(optimizer=optimizer, conf=conf.train.lr_scheduler)
     if args.restore:
         optimizer.load_state_dict(init_cp["optimizer"])
         if "lr_scheduler" in init_cp: