[Refactory] Merge BEiT and ConvNext 's LR decay optimizer constructors

mmseg/core/__init__.py

@@ -2,12 +2,10 @@
 from .builder import (OPTIMIZER_BUILDERS, build_optimizer,
                       build_optimizer_constructor)
 from .evaluation import *  # noqa: F401, F403
-from .layer_decay_optimizer_constructor import \
-    LayerDecayOptimizerConstructor  # noqa: F401
+from .optimizers import *  # noqa: F401, F403
 from .seg import *  # noqa: F401, F403
 from .utils import *  # noqa: F401, F403
 
 __all__ = [
-    'LayerDecayOptimizerConstructor', 'OPTIMIZER_BUILDERS', 'build_optimizer',
-    'build_optimizer_constructor'
+    'OPTIMIZER_BUILDERS', 'build_optimizer', 'build_optimizer_constructor'
 ]

mmseg/core/optimizers/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .layer_decay_optimizer_constructor import (
+    LayerDecayOptimizerConstructor, LearningRateDecayOptimizerConstructor)
+
+__all__ = [
+    'LearningRateDecayOptimizerConstructor', 'LayerDecayOptimizerConstructor'
+]

mmseg/core/utils/layer_decay_optimizer_constructor.py → mmseg/core/optimizers/layer_decay_optimizer_constructor.py

@@ -1,19 +1,20 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import json
+import warnings
 
 from mmcv.runner import DefaultOptimizerConstructor, get_dist_info
 
 from mmseg.utils import get_root_logger
 from ..builder import OPTIMIZER_BUILDERS
 
 
-def get_num_layer_layer_wise(var_name, num_max_layer=12):
+def get_layer_id_for_convnext(var_name, max_layer_id):
     """Get the layer id to set the different learning rates in ``layer_wise``
     decay_type.
 
     Args:
         var_name (str): The key of the model.
-        num_max_layer (int): Maximum number of backbone layers.
+        max_layer_id (int): Maximum number of backbone layers.
 
     Returns:
         int: The id number corresponding to different　learning rate in
@@ -32,7 +33,7 @@ def get_num_layer_layer_wise(var_name, num_max_layer=12):
         elif stage_id == 2:
             layer_id = 3
         elif stage_id == 3:
-            layer_id = num_max_layer
+            layer_id = max_layer_id
         return layer_id
     elif var_name.startswith('backbone.stages'):
         stage_id = int(var_name.split('.')[2])
@@ -44,19 +45,19 @@ def get_num_layer_layer_wise(var_name, num_max_layer=12):
         elif stage_id == 2:
             layer_id = 3 + block_id // 3
         elif stage_id == 3:
-            layer_id = num_max_layer
+            layer_id = max_layer_id
         return layer_id
     else:
-        return num_max_layer + 1
+        return max_layer_id + 1
 
 
-def get_num_layer_stage_wise(var_name, num_max_layer):
-    """Get the layer id to set the different learning rates in ``stage_wise``
+def get_stage_id_for_convnext(var_name, max_stage_id):
+    """Get the stage id to set the different learning rates in ``stage_wise``
     decay_type.
 
     Args:
         var_name (str): The key of the model.
-        num_max_layer (int): Maximum number of backbone layers.
+        max_stage_id (int): Maximum number of backbone layers.
     Returns:
         int: The id number corresponding to different　learning rate in
         ``LearningRateDecayOptimizerConstructor``.
@@ -71,14 +72,38 @@ def get_num_layer_stage_wise(var_name, num_max_layer):
         stage_id = int(var_name.split('.')[2])
         return stage_id + 1
     else:
-        return num_max_layer - 1
+        return max_stage_id - 1
+
+
+def get_layer_id_for_vit(var_name, max_layer_id):
+    """Get the layer id to set the different learning rates.
+
+    Args:
+        var_name (str): The key of the model.
+        num_max_layer (int): Maximum number of backbone layers.
+    Returns:
+        int: Returns the layer id of the key.
+    """
+
+    if var_name in ('backbone.cls_token', 'backbone.mask_token',
+                    'backbone.pos_embed'):
+        return 0
+    elif var_name.startswith('backbone.patch_embed'):
+        return 0
+    elif var_name.startswith('backbone.layers'):
+        layer_id = int(var_name.split('.')[2])
+        return layer_id + 1
+    else:
+        return max_layer_id - 1
 
 
 @OPTIMIZER_BUILDERS.register_module()
 class LearningRateDecayOptimizerConstructor(DefaultOptimizerConstructor):
-    """Different learning rates are set for different layers of backbone."""
+    # Different learning rates are set for different layers of backbone.
+    # Note: Currently, this optimizer constructor is built for ConvNeXt
+    # and BEiT.
 
-    def add_params(self, params, module):
+    def add_params(self, params, module, **kwargs):
         """Add all parameters of module to the params list.
 
         The parameters of the given module will be added to the list of param
@@ -99,7 +124,6 @@ def add_params(self, params, module):
         logger.info('Build LearningRateDecayOptimizerConstructor  '
                     f'{decay_type} {decay_rate} - {num_layers}')
         weight_decay = self.base_wd
-
         for name, param in module.named_parameters():
             if not param.requires_grad:
                 continue  # frozen weights
@@ -110,14 +134,22 @@ def add_params(self, params, module):
             else:
                 group_name = 'decay'
                 this_weight_decay = weight_decay
-
-            if decay_type == 'layer_wise':
-                layer_id = get_num_layer_layer_wise(
-                    name, self.paramwise_cfg.get('num_layers'))
-                logger.info(f'set param {name} as id {layer_id}')
+            if 'layer_wise' in decay_type:
+                if 'ConvNeXt' in module.backbone.__class__.__name__:
+                    layer_id = get_layer_id_for_convnext(
+                        name, self.paramwise_cfg.get('num_layers'))
+                    logger.info(f'set param {name} as id {layer_id}')
+                elif 'BEiT' in module.backbone.__class__.__name__:
+                    layer_id = get_layer_id_for_vit(name, num_layers)
+                    logger.info(f'set param {name} as id {layer_id}')
+                else:
+                    raise NotImplementedError()
             elif decay_type == 'stage_wise':
-                layer_id = get_num_layer_stage_wise(name, num_layers)
-                logger.info(f'set param {name} as id {layer_id}')
+                if 'ConvNeXt' in module.backbone.__class__.__name__:
+                    layer_id = get_stage_id_for_convnext(name, num_layers)
+                    logger.info(f'set param {name} as id {layer_id}')
+                else:
+                    raise NotImplementedError()
             group_name = f'layer_{layer_id}_{group_name}'
 
             if group_name not in parameter_groups:
@@ -146,3 +178,24 @@ def add_params(self, params, module):
                 }
             logger.info(f'Param groups = {json.dumps(to_display, indent=2)}')
         params.extend(parameter_groups.values())
+
+
+@OPTIMIZER_BUILDERS.register_module()
+class LayerDecayOptimizerConstructor(LearningRateDecayOptimizerConstructor):
+    # Different learning rates are set for different layers of backbone.
+    # Note: Currently, this optimizer constructor is built for BEiT,
+    # and it will be deprecated.
+    # Please use ``LearningRateDecayOptimizerConstructor`` instead.
+
+    def __init__(self, optimizer_cfg, paramwise_cfg):
+        warnings.warn('DeprecationWarning: Original '
+                      'LayerDecayOptimizerConstructor of BEiT '
+                      'will be deprecated. Please use '
+                      'LearningRateDecayOptimizerConstructor instead, '
+                      'and set decay_type = layer_wise_vit in paramwise_cfg.')
+        paramwise_cfg.update({'decay_type': 'layer_wise_vit'})
+        warnings.warn('DeprecationWarning: Layer_decay_rate will '
+                      'be deleted, please use decay_rate instead.')
+        paramwise_cfg['decay_rate'] = paramwise_cfg.pop('layer_decay_rate')
+        super(LayerDecayOptimizerConstructor,
+              self).__init__(optimizer_cfg, paramwise_cfg)

mmseg/core/utils/__init__.py

@@ -1,10 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .dist_util import check_dist_init, sync_random_seed
-from .layer_decay_optimizer_constructor import \
-    LearningRateDecayOptimizerConstructor
 from .misc import add_prefix
 
-__all__ = [
-    'add_prefix', 'LearningRateDecayOptimizerConstructor', 'check_dist_init',
-    'sync_random_seed'
-]
+__all__ = ['add_prefix', 'check_dist_init', 'sync_random_seed']