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T4
Starting
on
T4
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 | |
# import pdb;pdb.set_trace() | |
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_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, | |
} | |
] | |
# print("param_dicts: {}".format(param_dicts)) | |
return param_dicts |