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# Copyright (c) ByteDance, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Mostly copy-paste from BEiT library:
https://github.com/microsoft/unilm/blob/master/beit/semantic_segmentation/mmcv_custom/layer_decay_optimizer_constructor.py
"""
import json
from mmcv.runner import (OPTIMIZER_BUILDERS, DefaultOptimizerConstructor,
get_dist_info)
from mmseg.utils import get_root_logger
def get_num_layer_for_vit(var_name, num_max_layer):
var_name = var_name.replace('cb_modules.0.', '').replace('cb_modules.1.', '')
var_name = var_name.replace('levels.', 'layers.')
if var_name in ('backbone.cls_token', 'backbone.mask_token',
'backbone.pos_embed', 'backbone.visual_embed'):
return 0
elif var_name.startswith('backbone.visual_embed'):
return 0
elif var_name.startswith('backbone.patch_embed'):
return 0
elif var_name.startswith('backbone.blocks') or var_name.startswith('backbone.layers'):
layer_id = int(var_name.split('.')[2])
return layer_id + 1
else:
return num_max_layer - 1
@OPTIMIZER_BUILDERS.register_module()
class CustomLayerDecayOptimizerConstructor(DefaultOptimizerConstructor):
def add_params(self, params, module, prefix='', is_dcn_module=None):
"""Add all parameters of module to the params list.
The parameters of the given module will be added to the list of param
groups, with specific rules defined by paramwise_cfg.
Args:
params (list[dict]): A list of param groups, it will be modified
in place.
module (nn.Module): The module to be added.
prefix (str): The prefix of the module
is_dcn_module (int|float|None): If the current module is a
submodule of DCN, `is_dcn_module` will be passed to
control conv_offset layer's learning rate. Defaults to None.
"""
parameter_groups = {}
logger = get_root_logger()
logger.info(self.paramwise_cfg)
num_layers = self.paramwise_cfg.get('num_layers') + 2
layer_decay_rate = self.paramwise_cfg.get('layer_decay_rate')
logger.info('Build LayerDecayOptimizerConstructor %f - %d' % (layer_decay_rate, num_layers))
weight_decay = self.base_wd
for name, param in module.named_parameters():
if not param.requires_grad:
continue # frozen weights
if len(param.shape) == 1 or name.endswith('.bias') or name in ('pos_embed', 'cls_token'):
group_name = 'no_decay'
this_weight_decay = 0.
else:
group_name = 'decay'
this_weight_decay = weight_decay
layer_id = get_num_layer_for_vit(name, num_layers)
group_name = 'layer_%d_%s' % (layer_id, group_name)
if group_name not in parameter_groups:
scale = layer_decay_rate ** (num_layers - layer_id - 1)
parameter_groups[group_name] = {
'weight_decay': this_weight_decay,
'params': [],
'param_names': [],
'lr_scale': scale,
'group_name': group_name,
'lr': scale * self.base_lr,
}
parameter_groups[group_name]['params'].append(param)
parameter_groups[group_name]['param_names'].append(name)
rank, _ = get_dist_info()
if rank == 0:
to_display = {}
for key in parameter_groups:
to_display[key] = {
'param_names': parameter_groups[key]['param_names'],
'lr_scale': parameter_groups[key]['lr_scale'],
'lr': parameter_groups[key]['lr'],
'weight_decay': parameter_groups[key]['weight_decay'],
}
logger.info('Param groups = %s' % json.dumps(to_display, indent=2))
params.extend(parameter_groups.values())
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