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from typing import List | |
from data.dataloader import build_dataloader | |
# from methods.elasticdnn.api.online_model import ElasticDNN_OnlineModel | |
from new_impl.cv.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel | |
import torch | |
import sys | |
from torch import nn | |
from new_impl.cv.elasticdnn.api.model import ElasticDNN_OfflineSegFMModel, ElasticDNN_OfflineSegMDModel | |
from new_impl.cv.elasticdnn.api.algs.md_pretraining_wo_fbs import ElasticDNN_MDPretrainingWoFBSAlg | |
from new_impl.cv.elasticdnn.model.base import ElasticDNNUtil | |
from new_impl.cv.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util | |
from clip import FM_to_MD_clip_Util | |
from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util | |
from clip import FMLoRA_clip_Util | |
from clip import ElasticclipUtil | |
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.shot.shot import OnlineShotModel | |
from new_impl.cv.feat_align.main import OnlineFeatAlignModel, FeatAlignAlg | |
import tqdm | |
from new_impl.cv.feat_align.mmd import mmd_rbf | |
from new_impl.cv.utils.baseline_da import baseline_da | |
device = 'cuda' | |
app_name = 'cls' | |
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' | |
}, | |
) | |
class ClsOnlineFeatAlignModel(OnlineFeatAlignModel): | |
def get_trained_params(self): # TODO: elastic fm only train a part of params | |
#qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters() if 'attention.attention.projection_query' in n or 'attention.attention.projection_key' in n or 'attention.attention.projection_value' in n or 'intermediate.dense' in n or 'output.dense' in n] | |
qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters()] | |
return qkv_and_norm_params | |
def get_feature_hook(self): | |
return LayerActivation(get_module(self.models_dict['main'], 'classifier'), False, self.device) | |
def forward_to_get_task_loss(self, x, y): | |
return F.cross_entropy(self.infer(x), y) | |
def get_mmd_loss(self, f1, f2): | |
return mmd_rbf(f1, f2) | |
def infer(self, x, *args, **kwargs): | |
return self.models_dict['main'](x) | |
def get_accuracy(self, test_loader, *args, **kwargs): | |
acc = 0 | |
sample_num = 0 | |
self.to_eval_mode() | |
with torch.no_grad(): | |
pbar = tqdm.tqdm(enumerate(test_loader), total=len(test_loader), dynamic_ncols=True, leave=False) | |
for batch_index, (x, y) in pbar: | |
x, y = x.to(self.device), y.to(self.device) | |
output = self.infer(x) | |
pred = F.softmax(output, dim=1).argmax(dim=1) | |
correct = torch.eq(pred, y).sum().item() | |
acc += correct | |
sample_num += len(y) | |
pbar.set_description(f'cur_batch_total: {len(y)}, cur_batch_correct: {correct}, ' | |
f'cur_batch_acc: {(correct / len(y)):.4f}') | |
acc /= sample_num | |
return acc | |
da_alg = FeatAlignAlg | |
#from experiments.cua.vit_b_16.online.cls.model import ClsOnlineFeatAlignModel | |
da_model = ClsOnlineFeatAlignModel( | |
app_name, | |
'new_impl/cv/clip/results/cls_md_wo_fbs.py/20231115/999998-195939-/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/cv/clip/cls_md_wo_fbs.py/models/md_best.pt', | |
device | |
) | |
da_alg_hyp = { | |
'CityscapesCls': { | |
'train_batch_size': 64, | |
'val_batch_size': 512, | |
'num_workers': 8, | |
'optimizer': 'AdamW', | |
'optimizer_args': {'lr': 4e-8/2, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, | |
'scheduler': '', | |
'scheduler_args': {}, | |
'num_iters': 100, | |
'val_freq': 20, | |
'feat_align_loss_weight': 3.0 | |
}, | |
'BaiduPersonCls': { | |
'train_batch_size': 64, | |
'val_batch_size': 512, | |
'num_workers': 8, | |
'optimizer': 'SGD', | |
'optimizer_args': {'lr': 1e-10, 'momentum': 0.9}, | |
'scheduler': '', | |
'scheduler_args': {}, | |
'num_iters': 100, | |
'val_freq': 20, | |
'feat_align_loss_weight': 0.2 | |
} | |
} | |
baseline_da( | |
app_name, | |
scenario, | |
da_alg, | |
da_alg_hyp, | |
da_model, | |
device, | |
__file__, | |
sys.argv[0] | |
) | |