|
from typing import List |
|
from data.dataloader import build_dataloader |
|
|
|
from methods.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel |
|
|
|
import torch |
|
import sys |
|
from torch import nn |
|
from methods.elasticdnn.api.model import ElasticDNN_OfflineSegFMModel, ElasticDNN_OfflineSegMDModel |
|
from methods.elasticdnn.api.algs.md_pretraining_wo_fbs import ElasticDNN_MDPretrainingWoFBSAlg |
|
from methods.elasticdnn.model.base import ElasticDNNUtil |
|
from methods.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util |
|
from methods.elasticdnn.pipeline.offline.fm_to_md.vit import FM_to_MD_ViT_Util |
|
from methods.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util |
|
from methods.elasticdnn.pipeline.offline.fm_lora.vit import FMLoRA_ViT_Util |
|
from methods.elasticdnn.model.vit import ElasticViTUtil |
|
from utils.common.file import ensure_dir |
|
from utils.dl.common.model import LayerActivation, get_module, get_parameter |
|
from utils.common.exp import save_models_dict_for_init, get_res_save_dir |
|
from data import build_scenario |
|
from utils.dl.common.loss import CrossEntropyLossSoft |
|
import torch.nn.functional as F |
|
from utils.dl.common.env import create_tbwriter |
|
import os |
|
from utils.common.log import logger |
|
from utils.common.data_record import write_json |
|
|
|
from methods.feat_align.main import OnlineFeatAlignModel, FeatAlignAlg |
|
import tqdm |
|
from methods.feat_align.mmd import mmd_rbf |
|
from experiments.utils.elasticfm_da import init_online_model, elasticfm_da |
|
|
|
device = 'cuda' |
|
app_name = 'cls' |
|
sd_sparsity = 0.8 |
|
|
|
settings = { |
|
'involve_fm': True |
|
} |
|
|
|
scenario = build_scenario( |
|
source_datasets_name=['GTA5Cls', 'SuperviselyPersonCls'], |
|
target_datasets_order=['CityscapesCls', 'BaiduPersonCls'] * 15, |
|
da_mode='close_set', |
|
data_dirs={ |
|
'GTA5Cls': '/data/zql/datasets/gta5_for_cls_task', |
|
'SuperviselyPersonCls': '/data/zql/datasets/supervisely_person_for_cls_task', |
|
'CityscapesCls': '/data/zql/datasets/cityscapes_for_cls_task', |
|
'BaiduPersonCls': '/data/zql/datasets/baiduperson_for_cls_task' |
|
}, |
|
) |
|
|
|
from experiments.elasticdnn.vit_b_16.online_new.cls.model import ElasticDNN_ClsOnlineModel |
|
elasticfm_model = ElasticDNN_ClsOnlineModel('cls', init_online_model( |
|
|
|
|
|
'experiments/elasticdnn/vit_b_16/offline/fm_to_md/cls/results/cls_md_index.py/20230617/999992-101343-lr1e-5_index_bug_fixed/models/fm_best.pt', |
|
'experiments/elasticdnn/vit_b_16/offline/fm_to_md/cls/results/cls_md_index.py/20230617/999992-101343-lr1e-5_index_bug_fixed/models/md_best.pt', |
|
'cls', __file__ |
|
), device, { |
|
'md_to_fm_alpha': 1, |
|
'fm_to_md_alpha': 1 |
|
}) |
|
|
|
da_alg = FeatAlignAlg |
|
from experiments.elasticdnn.vit_b_16.online_new.cls.model import ClsOnlineFeatAlignModel |
|
da_model = ClsOnlineFeatAlignModel |
|
da_alg_hyp = { |
|
'CityscapesCls': { |
|
'train_batch_size': 64, |
|
'val_batch_size': 512, |
|
'num_workers': 8, |
|
'optimizer': 'AdamW', |
|
'optimizer_args': {'lr': 4e-6/2, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, |
|
'scheduler': '', |
|
'scheduler_args': {}, |
|
'num_iters': 100, |
|
'val_freq': 20, |
|
'sd_sparsity': sd_sparsity, |
|
'feat_align_loss_weight': 3.0 |
|
}, |
|
'BaiduPersonCls': { |
|
'train_batch_size': 64, |
|
'val_batch_size': 512, |
|
'num_workers': 8, |
|
'optimizer': 'SGD', |
|
'optimizer_args': {'lr': 1e-4/2, 'momentum': 0.9}, |
|
'scheduler': '', |
|
'scheduler_args': {}, |
|
'num_iters': 100, |
|
'val_freq': 20, |
|
'sd_sparsity': sd_sparsity, |
|
'feat_align_loss_weight': 0.3 |
|
} |
|
} |
|
|
|
|
|
elasticfm_da( |
|
[app_name], |
|
[scenario], |
|
[elasticfm_model], |
|
[da_alg], |
|
[da_alg_hyp], |
|
[da_model], |
|
device, |
|
settings, |
|
__file__, |
|
sys.argv[0] |
|
) |
|
|
