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import sys
from utils.dl.common.env import set_random_seed
set_random_seed(1)
from typing import List
from data.dataloader import build_dataloader
from data import Scenario
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
import shutil
from utils.common.log import logger
from utils.common.data_record import write_json
# from methods.shot.shot import OnlineShotModel
from methods.feat_align.main import OnlineFeatAlignModel, FeatAlignAlg
import tqdm
from methods.feat_align.mmd import mmd_rbf
from methods.base.alg import BaseAlg
from methods.base.model import BaseModel
def baseline_da(app_name: str,
scenario: Scenario,
da_alg: BaseAlg,
da_alg_hyp: dict,
da_model: BaseModel,
device,
__entry_file__,
tag=None):
# involve_fm = settings['involve_fm']
task_name = app_name
# online_model = elasticfm_model
log_dir = get_res_save_dir(__entry_file__, tag=tag)
tb_writer = create_tbwriter(os.path.join(log_dir, 'tb_log'), False)
res = []
global_avg_after_acc = 0.
global_iter = 0
for domain_index, _ in enumerate(scenario.target_domains_order):
cur_target_domain_name = scenario.target_domains_order[scenario.cur_domain_index]
if cur_target_domain_name in da_alg_hyp:
da_alg_hyp = da_alg_hyp[cur_target_domain_name]
logger.info(f'use dataset-specific hyps')
# tmp_sd_path = os.path.join(log_dir, 'tmp_sd_model.pt')
# torch.save({'main': sd}, tmp_sd_path)
# if task_name != 'cls':
# da_model_args = [f'{task_name}/{domain_index}',
# tmp_sd_path,
# device,
# scenario.num_classes]
# else:
# da_model_args = [f'{task_name}/{domain_index}',
# tmp_sd_path,
# device]
# cur_da_model = da_model(*da_model_args)
da_metrics, after_da_model = da_alg(
{'main': da_model},
os.path.join(log_dir, f'{task_name}/{domain_index}')
).run(scenario, da_alg_hyp)
# os.remove(tmp_sd_path)
if domain_index > 0:
shutil.rmtree(os.path.join(log_dir, f'{task_name}/{domain_index}/backup_codes'))
accs = da_metrics['accs']
before_acc = accs[0]['acc']
after_acc = accs[-1]['acc']
tb_writer.add_scalars(f'accs/{task_name}', dict(before=before_acc, after=after_acc), domain_index)
tb_writer.add_scalar(f'times/{task_name}', da_metrics['time'], domain_index)
for _acc in accs:
tb_writer.add_scalar('total_acc', _acc['acc'], _acc['iter'] + global_iter)
global_iter += _acc['iter'] + 1
scenario.next_domain()
logger.info(f"task: {task_name}, domain {domain_index}, acc: {before_acc:.4f} -> "
f"{after_acc:.4f} ({da_metrics['time']:.2f}s)")
global_avg_after_acc += after_acc
cur_res = da_metrics
res += [cur_res]
write_json(os.path.join(log_dir, 'res.json'), res, backup=False)
global_avg_after_acc /= (domain_index + 1)
logger.info(f'-----> final metric: {global_avg_after_acc:.4f}')
write_json(os.path.join(log_dir, f'res_{global_avg_after_acc:.4f}.json'), res, backup=False)
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