Add type hints for mmcv/runner/optimizer (#2001)

mmcv/runner/optimizer/builder.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import copy
 import inspect
+from typing import Dict, List
 
 import torch
 
@@ -10,7 +11,7 @@ OPTIMIZERS = Registry('optimizer')
 OPTIMIZER_BUILDERS = Registry('optimizer builder')
 
 
-def register_torch_optimizers():
+def register_torch_optimizers() -> List:
     torch_optimizers = []
     for module_name in dir(torch.optim):
         if module_name.startswith('__'):
@@ -26,11 +27,11 @@ def register_torch_optimizers():
 TORCH_OPTIMIZERS = register_torch_optimizers()
 
 
-def build_optimizer_constructor(cfg):
+def build_optimizer_constructor(cfg: Dict):
     return build_from_cfg(cfg, OPTIMIZER_BUILDERS)
 
 
-def build_optimizer(model, cfg):
+def build_optimizer(model, cfg: Dict):
     optimizer_cfg = copy.deepcopy(cfg)
     constructor_type = optimizer_cfg.pop('constructor',
                                          'DefaultOptimizerConstructor')

mmcv/runner/optimizer/default_constructor.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import warnings
+from typing import Dict, List, Optional, Union
 
 import torch
+import torch.nn as nn
 from torch.nn import GroupNorm, LayerNorm
 
 from mmcv.utils import _BatchNorm, _InstanceNorm, build_from_cfg, is_list_of
@@ -93,7 +95,9 @@ class DefaultOptimizerConstructor:
         >>> # model.cls_head is (0.01, 0.95).
     """
 
-    def __init__(self, optimizer_cfg, paramwise_cfg=None):
+    def __init__(self,
+                 optimizer_cfg: Dict,
+                 paramwise_cfg: Optional[Dict] = None):
         if not isinstance(optimizer_cfg, dict):
             raise TypeError('optimizer_cfg should be a dict',
                             f'but got {type(optimizer_cfg)}')
@@ -103,7 +107,7 @@ class DefaultOptimizerConstructor:
         self.base_wd = optimizer_cfg.get('weight_decay', None)
         self._validate_cfg()
 
-    def _validate_cfg(self):
+    def _validate_cfg(self) -> None:
         if not isinstance(self.paramwise_cfg, dict):
             raise TypeError('paramwise_cfg should be None or a dict, '
                             f'but got {type(self.paramwise_cfg)}')
@@ -126,7 +130,7 @@ class DefaultOptimizerConstructor:
             if self.base_wd is None:
                 raise ValueError('base_wd should not be None')
 
-    def _is_in(self, param_group, param_group_list):
+    def _is_in(self, param_group: Dict, param_group_list: List) -> bool:
         assert is_list_of(param_group_list, dict)
         param = set(param_group['params'])
         param_set = set()
@@ -135,7 +139,11 @@ class DefaultOptimizerConstructor:
 
         return not param.isdisjoint(param_set)
 
-    def add_params(self, params, module, prefix='', is_dcn_module=None):
+    def add_params(self,
+                   params: List[Dict],
+                   module: nn.Module,
+                   prefix: str = '',
+                   is_dcn_module: Optional[Union[int, float]] = None) -> None:
         """Add all parameters of module to the params list.
 
         The parameters of the given module will be added to the list of param
@@ -232,7 +240,7 @@ class DefaultOptimizerConstructor:
                 prefix=child_prefix,
                 is_dcn_module=is_dcn_module)
 
-    def __call__(self, model):
+    def __call__(self, model: nn.Module):
         if hasattr(model, 'module'):
             model = model.module
 
@@ -243,7 +251,7 @@ class DefaultOptimizerConstructor:
             return build_from_cfg(optimizer_cfg, OPTIMIZERS)
 
         # set param-wise lr and weight decay recursively
-        params = []
+        params: List[Dict] = []
         self.add_params(params, model)
         optimizer_cfg['params'] = params