1.0.0 official release (#18)

* fix bug in fix_BN

* gaitgl OUMVLP support.

* update ./doc/3.advance_usage.md Cross-Dataset Evalution & Data Agumentation

* update config

* update docs.3

* update docs.3

* add loss doc and gather input decorator

* refine the create model doc

* support rearrange directory of unzipped OUMVLP

* fix some bugs in loss_aggregator.py

* refine docs and little fix

* add oumvlp pretreatment description

* pretreatment dataset fix oumvlp description

* add gaitgl oumvlp result

* assert gaitgl input size

* add pipeline

* update the readme.

* update pipeline and readme

* Corrigendum.

* add logo and remove path

* update new logo

* Update README.md

* modify logo size

Co-authored-by: 12131100 <12131100@mail.sustech.edu.cn>
Co-authored-by: noahshen98 <77523610+noahshen98@users.noreply.github.com>
Co-authored-by: Noah <595311942@qq.com>

lib/modeling/loss_aggregator.py

@@ -1,3 +1,5 @@
+"""The loss aggregator."""
+
 import torch
 from . import losses
 from utils import is_dict, get_attr_from, get_valid_args, is_tensor, get_ddp_module
@@ -6,18 +8,48 @@ from utils import get_msg_mgr
 
 
 class LossAggregator():
+    """The loss aggregator.
+
+    This class is used to aggregate the losses.
+    For example, if you have two losses, one is triplet loss, the other is cross entropy loss,
+    you can aggregate them as follows:
+    loss_num = tripley_loss + cross_entropy_loss 
+
+    Attributes:
+        losses: A dict of losses.
+    """
+
     def __init__(self, loss_cfg) -> None:
+        """
+        Initialize the loss aggregator.
+
+        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}
 
     def _build_loss_(self, loss_cfg):
+        """Build the losses from loss_cfg.
+
+        Args:
+            loss_cfg: Config of loss.
+        """
         Loss = get_attr_from([losses], loss_cfg['type'])
         valid_loss_arg = get_valid_args(
-            Loss, loss_cfg, ['type', 'pair_based_loss'])
-        loss = get_ddp_module(Loss(**valid_loss_arg))
+            Loss, loss_cfg, ['type', 'gather_and_scale'])
+        loss = get_ddp_module(Loss(**valid_loss_arg).cuda())
         return loss
 
     def __call__(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 
+        built losses and the value is torch.Tensor, then it is the computed loss to be added loss_sum.
+
+        Args:
+            training_feats: A dict of features. The same as the output["training_feat"] of the model.
+        """
         loss_sum = .0
         loss_info = Odict()
 
@@ -28,14 +60,12 @@ class LossAggregator():
                 for name, value in info.items():
                     loss_info['scalar/%s/%s' % (k, name)] = value
                 loss = loss.mean() * loss_func.loss_term_weights
-                if loss_func.pair_based_loss:
-                    loss = loss * torch.distributed.get_world_size()
                 loss_sum += loss
 
             else:
                 if isinstance(v, dict):
                     raise ValueError(
-                        "The key %s in -Trainng-Feat- should be stated as the log_prefix of a certain loss defined in your loss_cfg."
+                        "The key %s in -Trainng-Feat- should be stated as the log_prefix of a certain loss defined in your loss_cfg."%v
                     )
                 elif is_tensor(v):
                     _ = v.mean()