File size: 5,120 Bytes
b84549f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
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]
)
|