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import json
from mmcv.runner import OPTIMIZER_BUILDERS, DefaultOptimizerConstructor
from mmcv.runner import get_dist_info
def get_num_layer_for_vit(var_name, num_max_layer):
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.blocks"):
layer_id = int(var_name.split('.')[2])
return layer_id + 1
else:
return num_max_layer - 1
@OPTIMIZER_BUILDERS.register_module()
class LayerDecayOptimizerConstructor(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 = {}
print(self.paramwise_cfg)
num_layers = self.paramwise_cfg.get('num_layers') + 2
layer_decay_rate = self.paramwise_cfg.get('layer_decay_rate')
print("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 'pos_embed' in name:
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"],
}
print("Param groups = %s" % json.dumps(to_display, indent=2))
params.extend(parameter_groups.values())
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