update docstring (#20)

configs/nas/darts/darts_supernet_unroll_1xb64_cifar10.py

@@ -1,5 +1,5 @@
 _base_ = [
-    '../../_base_/datasets/mmcls/cifar10_bs16.py', 
+    '../../_base_/datasets/mmcls/cifar10_bs16.py',
     '../../_base_/mmcls_runtime.py'
 ]
 

docs/en/model_zoo.md

@@ -4,24 +4,24 @@
 
 ### CWD
 
-Please refer to [CWD](/configs/distill/cwd/README.md) for details.
+Please refer to [CWD](https://github.com/open-mmlab/mmrazor/blob/master/configs/distill/cwd/README.md) for details.
 
 ### WSLD
 
-Please refer to [WSLD](/configs/distill/wsld/README.md) for details.
+Please refer to [WSLD](https://github.com/open-mmlab/mmrazor/blob/master/configs/distill/wsld/README.md) for details.
 
 ### DARTS
 
-Please refer to [DARTS](/configs/nas/darts/README.md) for details.
+Please refer to [DARTS](https://github.com/open-mmlab/mmrazor/blob/master/configs/nas/darts/README.md) for details.
 
 ### DETNAS
 
-Please refer to [DETNAS](/configs/nas/detnas/README.md) for details.
+Please refer to [DETNAS](https://github.com/open-mmlab/mmrazor/blob/master/configs/nas/detnas/README.md) for details.
 
 ### SPOS
 
-Please refer to [SPOS](/configs/nas/spos/README.md) for details.
+Please refer to [SPOS](https://github.com/open-mmlab/mmrazor/blob/master/configs/nas/spos/README.md) for details.
 
 ### AUTOSLIM
 
-Please refer to [AUTOSLIM](/configs/pruning/autoslim/README.md) for details.
+Please refer to [AUTOSLIM](https://github.com/open-mmlab/mmrazor/blob/master/configs/pruning/autoslim/README.md) for details.

docs/en/test.md

@@ -8,7 +8,7 @@ To test nas method, you can use following command
 python tools/${task}/test_${task}.py ${CONFIG_FILE} ${CHECKPOINT_PATH} --cfg-options algorithm.mutable_cfg=${MUTABLE_CFG_PATH} [optional arguments]
 ```
 
-- `MUTABLE_CFG_PATH`: Path of `mutable_cfg`. `mutable_cfg` represents **config for mutable of the subnet searched out**, used to specify different subnets for testing. An example for `mutable_cfg` can be found [here](/configs/nas/spos/SPOS_SHUFFLENET_300M.yaml).
+- `MUTABLE_CFG_PATH`: Path of `mutable_cfg`. `mutable_cfg` represents **config for mutable of the subnet searched out**, used to specify different subnets for testing. An example for `mutable_cfg` can be found [here](https://github.com/open-mmlab/mmrazor/blob/master/configs/nas/spos/SPOS_SHUFFLENETV2_330M_IN1k_PAPER.yaml).
 
 The usage of optional arguments are the same as corresponding tasks like mmclassification, mmdetection and mmsegmentation.
 
@@ -35,7 +35,7 @@ To test pruning method, you can use following command
 python tools/${task}/test_${task}.py ${CONFIG_FILE} ${CHECKPOINT_PATH} --cfg-options algorithm.channel_cfg=${CHANNEL_CFG_PATH} [optional arguments]
 ```
 
-- `CHANNEL_CFG_PATH`: Path of `channel_cfg`. `channel_cfg` represents **config for channel of the subnet searched out**, used to specify different subnets for testing. An example for `channel_cfg` can be found [here](/configs/pruning/autoslim/AUTOSLIM_MBV2_220M_OFFICIAL.yaml), and the usage can be found [here](/configs/pruning/autoslim/README.md#test-a-subnet).
+- `CHANNEL_CFG_PATH`: Path of `channel_cfg`. `channel_cfg` represents **config for channel of the subnet searched out**, used to specify different subnets for testing. An example for `channel_cfg` can be found [here](https://github.com/open-mmlab/mmrazor/blob/master/configs/pruning/autoslim/AUTOSLIM_MBV2_220M_OFFICIAL.yaml), and the usage can be found [here](https://github.com/open-mmlab/mmrazor/blob/master/configs/pruning/autoslim/README.md#test-a-subnet).
 
 ## Distillation
 

docs/en/train.md

@@ -31,7 +31,7 @@ python tools/${task}/search_${task}.py ${CONFIG_FILE} ${CHECKPOINT_PATH} [option
 python tools/${task}/train_${task}.py ${CONFIG_FILE} --cfg-options algorithm.mutable_cfg=${MUTABLE_CFG_PATH} [optional arguments]
 ```
 
-- `MUTABLE_CFG_PATH`: Path of `mutable_cfg`. `mutable_cfg` represents **config for mutable of the subnet searched out**, used to specify different subnets for retraining. An example for `mutable_cfg` can be found [here](/configs/nas/spos/SPOS_SHUFFLENET_300M.yaml), and the usage can be found [here](/configs/nas/spos/README.md#subnet-retraining-on-imagenet).
+- `MUTABLE_CFG_PATH`: Path of `mutable_cfg`. `mutable_cfg` represents **config for mutable of the subnet searched out**, used to specify different subnets for retraining. An example for `mutable_cfg` can be found [here](https://github.com/open-mmlab/mmrazor/blob/master/configs/nas/spos/SPOS_SHUFFLENETV2_330M_IN1k_PAPER.yaml), and the usage can be found [here](https://github.com/open-mmlab/mmrazor/blob/master/configs/nas/spos/README.md#subnet-retraining-on-imagenet).
 
 ## Pruning
 
@@ -45,7 +45,7 @@ python tools/${task}/train_${task}.py ${CONFIG_FILE} --cfg-options algorithm.cha
 
 Different from NAS, the argument that needs to be specified here is `channel_cfg` instead of `mutable_cfg`.
 
-- `CHANNEL_CFG_PATH`: Path of `channel_cfg`. `channel_cfg` represents **config for channel of the subnet searched out**, used to specify different subnets for testing. An example for `channel_cfg` can be found [here](/configs/pruning/autoslim/AUTOSLIM_MBV2_220M_OFFICIAL.yaml), and the usage can be found [here](/configs/pruning/autoslim/README.md#subnet-retraining-on-imagenet).
+- `CHANNEL_CFG_PATH`: Path of `channel_cfg`. `channel_cfg` represents **config for channel of the subnet searched out**, used to specify different subnets for testing. An example for `channel_cfg` can be found [here](https://github.com/open-mmlab/mmrazor/blob/master/configs/pruning/autoslim/AUTOSLIM_MBV2_220M_OFFICIAL.yaml), and the usage can be found [here](https://github.com/open-mmlab/mmrazor/blob/master/configs/pruning/autoslim/README.md#subnet-retraining-on-imagenet).
 
 ## Distillation
 

mmrazor/models/algorithms/align_method_kd.py

@@ -10,6 +10,7 @@ class AlignMethodDistill(GeneralDistill):
         super(AlignMethodDistill, self).__init__(**kwargs)
 
     def train_step(self, data, optimizer):
+
         with self.distiller.context_manager:
             outputs = super().train_step(data, optimizer)
         return outputs

mmrazor/models/algorithms/general_distill.py

@@ -25,7 +25,7 @@ class GeneralDistill(BaseAlgorithm):
         self.with_teacher_loss = with_teacher_loss
 
     def train_step(self, data, optimizer):
-
+        """"""
         losses = dict()
         if self.with_teacher_loss:
             teacher_losses = self.distiller.exec_teacher_forward(data)

mmrazor/models/losses/cwd.py

@@ -14,7 +14,7 @@ class ChannelWiseDivergence(nn.Module):
 
     Args:
         tau (float): Temperature coefficient. Defaults to 1.0.
-        weight (float): Weight of loss. Defaults to 1.0.
+        loss_weight (float): Weight of loss. Defaults to 1.0.
     """
 
     def __init__(

mmrazor/models/losses/weighted_soft_label_distillation.py

@@ -8,17 +8,17 @@ from ..builder import LOSSES
 
 @LOSSES.register_module()
 class WSLD(nn.Module):
+    """PyTorch version of `Rethinking Soft Labels for Knowledge
+    Distillation: A Bias-Variance Tradeoff Perspective
+    <https://arxiv.org/abs/2102.00650>`_.
+
+    Args:
+        tau (float): Temperature coefficient. Defaults to 1.0.
+        weight (float): Weight of loss. Defaults to 1.0.
+        num_classes (int): Defaults to 1000.
+    """
 
     def __init__(self, tau=1.0, loss_weight=1.0, num_classes=1000):
-        """PyTorch version of `Rethinking Soft Labels for Knowledge
-        Distillation: A Bias-Variance Tradeoff Perspective
-        <https://arxiv.org/abs/2102.00650>`_.
-
-        Args:
-            tau (float): Temperature coefficient. Defaults to 1.0.
-            weight (float): Weight of loss. Defaults to 1.0.
-            num_classes (int): Defaults to 1000.
-        """
         super(WSLD, self).__init__()
 
         self.tau = tau

mmrazor/models/pruners/structure_pruning.py

@@ -69,12 +69,11 @@ def register_parser(parser_dict, name=None, force=False):
 
 @PRUNERS.register_module()
 class StructurePruner(BaseModule, metaclass=ABCMeta):
-    """Base class for structure pruning.
-
-    This class defines the basic functions of a structure pruner. Any pruner
-    that inherits this class should at least define its own `sample_subnet` and
-    `set_min_channel` functions. This part is being continuously optimized, and
-     there may be major changes in the future.
+    """Base class for structure pruning. This class defines the basic functions
+    of a structure pruner. Any pruner that inherits this class should at least
+    define its own `sample_subnet` and `set_min_channel` functions. This part
+    is being continuously optimized, and there may be major changes in the
+    future.
 
     Args:
         except_start_keys (List[str]): the module whose name start with a