change way of layer freezing

Remove `find_unused_parameters` in DDP and add a new step function in optimizer for freezing backbone. It will accelerate training speed in this way.

fastreid/engine/defaults.py

@@ -213,7 +213,6 @@ class DefaultTrainer(TrainerBase):
             # for part of the parameters is not updated.
             model = DistributedDataParallel(
                 model, device_ids=[comm.get_local_rank()], broadcast_buffers=False,
-                find_unused_parameters=True
             )
 
         self._trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(
@@ -305,9 +304,9 @@ class DefaultTrainer(TrainerBase):
 
         ret.append(hooks.LayerFreeze(
             self.model,
+            self.optimizer,
             cfg.MODEL.FREEZE_LAYERS,
             cfg.SOLVER.FREEZE_ITERS,
-            cfg.SOLVER.FREEZE_FC_ITERS,
         ))
 
         # Do PreciseBN before checkpointer, because it updates the model and need to

fastreid/engine/hooks.py

@@ -449,19 +449,18 @@ class PreciseBN(HookBase):
 
 
 class LayerFreeze(HookBase):
-    def __init__(self, model, freeze_layers, freeze_iters, fc_freeze_iters):
+    def __init__(self, model, optimizer, freeze_layers, freeze_iters):
         self._logger = logging.getLogger(__name__)
 
         if isinstance(model, DistributedDataParallel):
             model = model.module
         self.model = model
+        self.optimizer = optimizer
 
         self.freeze_layers = freeze_layers
         self.freeze_iters = freeze_iters
-        self.fc_freeze_iters = fc_freeze_iters
 
         self.is_frozen = False
-        self.fc_frozen = False
 
     def before_step(self):
         # Freeze specific layers
@@ -472,18 +471,6 @@ class LayerFreeze(HookBase):
         if self.trainer.iter >= self.freeze_iters and self.is_frozen:
             self.open_all_layer()
 
-        if self.trainer.max_iter - self.trainer.iter <= self.fc_freeze_iters \
-                and not self.fc_frozen:
-            self.freeze_classifier()
-
-    def freeze_classifier(self):
-        for p in self.model.heads.classifier.parameters():
-            p.requires_grad_(False)
-
-        self.fc_frozen = True
-        self._logger.info("Freeze classifier training for "
-                          "last {} iterations".format(self.fc_freeze_iters))
-
     def freeze_specific_layer(self):
         for layer in self.freeze_layers:
             if not hasattr(self.model, layer):
@@ -493,8 +480,24 @@ class LayerFreeze(HookBase):
             if name in self.freeze_layers:
                 # Change BN in freeze layers to eval mode
                 module.eval()
-                for p in module.parameters():
+
-                    p.requires_grad_(False)
+        def zero_freeze_grad():
+            for group in self.optimizer.param_groups:
+                if group["name"].split('.')[0] in self.freeze_layers:
+                    for p in group["params"]:
+                        if p.grad is not None:
+                            p.grad = None
+
+        origin_step = self.optimizer.step
+        self.origin_step = origin_step
+
+        @torch.no_grad()
+        def step(closure=None):
+            zero_freeze_grad()
+            loss = origin_step(closure)
+            return loss
+
+        self.optimizer.step = step
 
         self.is_frozen = True
         freeze_layers = ", ".join(self.freeze_layers)
@@ -504,8 +507,8 @@ class LayerFreeze(HookBase):
         for name, module in self.model.named_children():
             if name in self.freeze_layers:
                 module.train()
-                for p in module.parameters():
+
-                    p.requires_grad_(True)
+        self.optimizer.step = self.origin_step
 
         self.is_frozen = False