Reconstruct LossAggregator and fix some typos in config files (ShiqiY…

…u#100)

* fix Gait3D configs typo

* Use ModuleDict to reconstruct LossAggregator

* fix typo

configs/baseline/baseline_Gait3D.yaml

@@ -83,7 +83,7 @@ trainer_cfg:
   enable_float16: true # half_percesion float for memory reduction and speedup
   fix_BN: false
   log_iter: 100
-  with_test: 10000
+  with_test: true
   restore_ckpt_strict: true
   restore_hint: 0
   save_iter: 10000

configs/smplgait/smplgait.yaml

@@ -85,7 +85,7 @@ trainer_cfg:
   enable_float16: true # half_percesion float for memory reduction and speedup
   fix_BN: false
   log_iter: 100
-  with_test: 10000
+  with_test: true
   restore_ckpt_strict: true
   restore_hint: 0
   save_iter: 10000

opengait/modeling/loss_aggregator.py

@@ -1,13 +1,14 @@
 """The loss aggregator."""
 
 import torch
+import torch.nn as nn
 from . import losses
 from utils import is_dict, get_attr_from, get_valid_args, is_tensor, get_ddp_module
 from utils import Odict
 from utils import get_msg_mgr
 
 
-class LossAggregator():
+class LossAggregator(nn.Module):
     """The loss aggregator.
 
     This class is used to aggregate the losses.
@@ -18,16 +19,21 @@ class LossAggregator():
     Attributes:
         losses: A dict of losses.
     """
-
     def __init__(self, loss_cfg) -> None:
         """
         Initialize the loss aggregator.
 
+        LossAggregator can be indexed like a regular Python dictionary, 
+        but modules it contains are properly registered, and will be visible by all Module methods.
+        All parameters registered in losses can be accessed by the method 'self.parameters()',
+        thus they can be trained properly.
+        
         Args:
             loss_cfg: Config of losses. List for multiple losses.
         """
-        self.losses = {loss_cfg['log_prefix']: self._build_loss_(loss_cfg)} if is_dict(loss_cfg) \
-            else {cfg['log_prefix']: self._build_loss_(cfg) for cfg in loss_cfg}
+        super().__init__()
+        self.losses = nn.ModuleDict({loss_cfg['log_prefix']: self._build_loss_(loss_cfg)} if is_dict(loss_cfg) \
+            else {cfg['log_prefix']: self._build_loss_(cfg) for cfg in loss_cfg})
 
     def _build_loss_(self, loss_cfg):
         """Build the losses from loss_cfg.
@@ -41,7 +47,7 @@ def _build_loss_(self, loss_cfg):
         loss = get_ddp_module(Loss(**valid_loss_arg).cuda())
         return loss
 
-    def __call__(self, training_feats):
+    def forward(self, training_feats):
         """Compute the sum of all losses.
 
         The input is a dict of features. The key is the name of loss and the value is the feature and label. If the key not in