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import torch
import torch.nn as nn
from methods.elasticdnn.api.algs.fm_lora import ElasticDNN_FMLoRAAlg
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_lora.base import FMLoRA_Util
from glip import ElasticGLIPUtil, FMLoRA_GLIP_Util, FM_to_MD_GLIP_Util, ElasticDNN_OfflineMMDetFMModel, ElasticDNN_OfflineMMDetMDModel
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.model.vit import ElasticViTUtil
from methods.elasticdnn.api.algs.md_pretraining_index_v2_train_index_and_md import ElasticDNN_MDPretrainingIndexAlg
from utils.dl.common.model import LayerActivation3, get_module, get_parameter
from utils.common.exp import save_models_dict_for_init, get_res_save_dir
from data import build_scenario
import torch.nn.functional as F
from maskrcnn_benchmark.structures.bounding_box import BoxList
import os
from utils.dl.common.loss import CrossEntropyLossSoft
from new_impl.cv.feat_align.main_glip 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
from new_impl.cv.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel
os.environ['TOKENIZERS_PARALLELISM'] = 'true'
torch.cuda.set_device(0)
device = 'cuda'
app_name = 'cls'
scenario = build_scenario(
source_datasets_name=['MM-COCO2017'],
target_datasets_order=['MM-CityscapesDet', 'MM-GTA5Det'] * 10,
da_mode='close_set',
data_dirs={
'MM-COCO2017': '/data/zql/datasets/coco2017',
'MM-CityscapesDet': '/data/zql/datasets/cityscape',
'MM-GTA5Det': '/data/zql/datasets/GTA-ls-copy/GTA5',
},
)
class DetOnlineFeatAlignModel(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 LayerActivation3(get_module(self.models_dict['main'], 'model.rpn'), False, self.device)
def forward_to_get_task_loss(self, x, y):
loss_dict = self.infer(x)
losses = sum(loss for loss in loss_dict.values())
# print(losses)
return losses
def get_mmd_loss(self, f1, f2):
# f1 = f1.view(f1.shape[0], -1)
# f2 = f2.view(f2.shape[0], -1)
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):
# print('DeeplabV3: start test acc')
_d = test_loader.dataset
imgsz = _d.cocods.img_size
cls_num = len(_d.cocods.class_ids)
# num_classes = len(_d.cls_names)
from data import build_dataloader
if _d.__class__.__name__ == 'MergedDataset':
# print('\neval on merged datasets')
datasets = _d.datasets
if self.collate_fn is None:
test_loaders = [build_dataloader(d, test_loader.batch_size, test_loader.num_workers, False, None, collate_fn=None) for d in datasets]
else:
test_loaders = [build_dataloader(d, test_loader.batch_size, test_loader.num_workers, False, None, collate_fn=self.collate_fn) for d in datasets]
accs = [self.get_accuracy(loader) for loader in test_loaders]
# print(accs)
return sum(accs) / len(accs)
# print('dataset len', len(test_loader.dataset))
model = self.models_dict['main']
device = self.device
model.eval()
# print('# classes', model.num_classes)
model = model.to(device)
from evaluator import COCOEvaluator, MMCOCODecoder
from utils.common.others import HiddenPrints
with torch.no_grad():
with HiddenPrints():
evaluator = COCOEvaluator(
dataloader=test_loader,
img_size=imgsz,
confthre=0.01,
nmsthre=0.65,
num_classes=cls_num,
testdev=False
)
res = evaluator.evaluate(model, False, False, decoder=MMCOCODecoder)
map50 = res[1]
# print('eval info', res[-1])
return map50
from glip import glip_model, build_transform, run_ner, collect_mm_fn
cfg_path = 'new_impl/cv/glip/object_detection/pretrained_model/glip_Swin_T_O365_GoldG.yaml'
model_path = 'new_impl/cv/glip/object_detection/pretrained_model/glip_tiny_model_o365_goldg_cc_sbu.pth'
config, _ = glip_model(cfg_path, model_path)
transform = build_transform(config, None)
da_alg = FeatAlignAlg
from utils.dl.common.lr_scheduler import get_linear_schedule_with_warmup
#from new_impl.cv.model import ClsOnlineFeatAlignModel
da_model = DetOnlineFeatAlignModel(
app_name,
'new_impl/cv/glip/object_detection/results/det_md_wo_fbs.py/20231129/999999-153230-results/models/md_best.pt',
device
)
da_alg_hyp = {
'MM-GTA5Det': {
'train_batch_size': 8,
'val_batch_size': 1,
'num_workers': 8,
'optimizer': 'AdamW',
'optimizer_args': {'lr': 2e-6, 'betas': [0.9, 0.999], 'weight_decay': 0.01},
'scheduler': '',
'scheduler_args': {},
'num_iters': 100,
'val_freq': 20,
'feat_align_loss_weight': 0.3,
'transform':transform
},
'MM-CityscapesDet': {
'train_batch_size': 8,
'val_batch_size': 1,
'num_workers': 8,
'optimizer': 'AdamW',
'optimizer_args': {'lr': 2e-6, 'betas': [0.9, 0.999], 'weight_decay': 0.01},
'scheduler': '',
'scheduler_args': {},
'num_iters': 100,
'val_freq': 20,
'feat_align_loss_weight': 0.3,
'transform':transform
},
}
baseline_da(
app_name,
scenario,
da_alg,
da_alg_hyp,
da_model,
device,
__file__,
"results",
collate_fn=collect_mm_fn
) |