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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]
)
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