EdgeTA / new_impl /cv /sam /seg_baseline.py
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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 sam import FM_to_MD_sam_Util
from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util
from sam import FMLoRA_sam_Util
from sam import ElasticsamUtil
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=['GTA5', 'SuperviselyPerson'],
target_datasets_order=['Cityscapes', 'BaiduPerson'] * 10,
da_mode='close_set',
data_dirs={
'GTA5': '/data/zql/datasets/GTA-ls-copy/GTA5',
'SuperviselyPerson': '/data/zql/datasets/supervisely_person/Supervisely Person Dataset',
'Cityscapes': '/data/zql/datasets/cityscape/',
'BaiduPerson': '/data/zql/datasets/baidu_person/clean_images/'
},
)
class SegOnlineFeatAlignModel(OnlineFeatAlignModel):
def __init__(self, name: str, models_dict_path: str, device: str, num_classes):
super().__init__(name, models_dict_path, device)
self.num_classes = num_classes
def get_feature_hook(self):
return LayerActivation(get_module(self.models_dict['main'], 'head'), 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.flatten(1), f2.flatten(1))
def infer(self, x, *args, **kwargs):
return self.models_dict['main'](x)
def get_trained_params(self):
qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters() if 'qkv.weight' in n or 'norm' in n or 'mlp' in n]
return qkv_and_norm_params
def infer(self, x, *args, **kwargs):
return self.models_dict['main'](x)
def get_accuracy(self, test_loader, *args, **kwargs):
device = self.device
self.to_eval_mode()
from methods.elasticdnn.api.model import StreamSegMetrics
metrics = StreamSegMetrics(self.num_classes)
metrics.reset()
import tqdm
pbar = tqdm.tqdm(enumerate(test_loader), total=len(test_loader), leave=False, dynamic_ncols=True)
with torch.no_grad():
for batch_index, (x, y) in pbar:
x, y = x.to(device, dtype=x.dtype, non_blocking=True, copy=False), \
y.to(device, dtype=y.dtype, non_blocking=True, copy=False)
output = self.infer(x)
pred = output.detach().max(dim=1)[1].cpu().numpy()
metrics.update((y + 0).cpu().numpy(), pred)
res = metrics.get_results()
pbar.set_description(f'cur batch mIoU: {res["Mean Acc"]:.4f}')
res = metrics.get_results()
return res['Mean Acc']
da_alg = FeatAlignAlg
#from experiments.cua.vit_b_16.online.cls.model import ClsOnlineFeatAlignModel
da_model = SegOnlineFeatAlignModel(
app_name,
'new_impl/cv/sam/results/seg_wo_fbs.py/20231130/999999-144157/models/md_best.pt',
device,
scenario.num_classes
)
da_alg_hyp = {'Cityscapes': {
'train_batch_size': 16,
'val_batch_size': 128,
'num_workers': 16,
'optimizer': 'AdamW',
'optimizer_args': {'lr': 1e-9, 'betas': [0.9, 0.999], 'weight_decay': 0.01},
'scheduler': '',
'scheduler_args': {},
'num_iters': 10,
'val_freq': 20,
# 'sd_sparsity': 0.8,
'feat_align_loss_weight': 3.0
}, 'BaiduPerson': {
'train_batch_size': 16,
'val_batch_size': 128,
'num_workers': 16,
'optimizer': 'AdamW',
'optimizer_args': {'lr': 1e-2, 'betas': [0.9, 0.999], 'weight_decay': 0.01},
'scheduler': '',
'scheduler_args': {},
'num_iters': 10,
'val_freq': 20,
# 'sd_sparsity': 0.8,
'feat_align_loss_weight': 0.3
}}
baseline_da(
app_name,
scenario,
da_alg,
da_alg_hyp,
da_model,
device,
__file__,
sys.argv[0]
)