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import json
import torch
import torch.nn as nn
def match_name_keywords(n: str, name_keywords: list):
out = False
for b in name_keywords:
if b in n:
out = True
break
return out
def get_param_dict(args, model_without_ddp: nn.Module):
try:
param_dict_type = args.param_dict_type
except:
param_dict_type = 'default'
assert param_dict_type in ['default', 'ddetr_in_mmdet', 'large_wd']
# by default
if param_dict_type == 'default':
param_dicts = [{
'params': [
p for n, p in model_without_ddp.named_parameters()
if 'backbone' not in n and p.requires_grad
]
}, {
'params': [
p for n, p in model_without_ddp.named_parameters()
if 'backbone' in n and p.requires_grad
],
'lr':
args.lr_backbone,
}]
return param_dicts
if param_dict_type == 'ddetr_in_mmdet':
param_dicts = [{
'params': [
p for n, p in model_without_ddp.named_parameters()
if not match_name_keywords(n, args.lr_backbone_names)
and not match_name_keywords(n, args.lr_linear_proj_names)
and p.requires_grad
],
'lr':
args.lr,
}, {
'params': [
p for n, p in model_without_ddp.named_parameters()
if match_name_keywords(n, args.lr_backbone_names)
and p.requires_grad
],
'lr':
args.lr_backbone,
}, {
'params': [
p for n, p in model_without_ddp.named_parameters()
if match_name_keywords(n, args.lr_linear_proj_names)
and p.requires_grad
],
'lr':
args.lr * args.lr_linear_proj_mult,
}]
return param_dicts
if param_dict_type == 'large_wd':
param_dicts = [{
'params': [
p for n, p in model_without_ddp.named_parameters()
if not match_name_keywords(n, ['backbone'])
and not match_name_keywords(n, ['norm', 'bias'])
and p.requires_grad
],
}, {
'params': [
p for n, p in model_without_ddp.named_parameters()
if match_name_keywords(n, ['backbone']) and
match_name_keywords(n, ['norm', 'bias']) and p.requires_grad
],
'lr':
args.lr_backbone,
'weight_decay':
0.0,
}, {
'params': [
p for n, p in model_without_ddp.named_parameters()
if match_name_keywords(n, ['backbone'])
and not match_name_keywords(n, ['norm', 'bias'])
and p.requires_grad
],
'lr':
args.lr_backbone,
'weight_decay':
args.weight_decay,
}, {
'params': [
p for n, p in model_without_ddp.named_parameters()
if not match_name_keywords(n, ['backbone']) and
match_name_keywords(n, ['norm', 'bias']) and p.requires_grad
],
'lr':
args.lr,
'weight_decay':
0.0,
}]
return param_dicts
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