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import torch | |
from new_impl.cv.elasticdnn.api.model import ElasticDNN_OfflineClsFMModel | |
#from methods.elasticdnn.api.algs.fm_lora import ElasticDNN_FMLoRAAlg | |
from new_impl.cv.elasticdnn.api.algs.fm_lora import ElasticDNN_FMLoRAAlg | |
from methods.elasticdnn.model.base import ElasticDNNUtil | |
from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util | |
from clip import FMLoRA_clip_Util | |
from new_impl.cv.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util | |
from new_impl.cv.elasticdnn.model.vit import ElasticViTUtil | |
from data import build_scenario | |
import torch.nn.functional as F | |
from utils.dl.common.model import LayerActivation, get_module | |
from utils.common.exp import save_models_dict_for_init, get_res_save_dir | |
# from transformers import CvtForImageClassification | |
# model = CvtForImageClassification.from_pretrained("/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/cv/cvt_model",num_labels=20,ignore_mismatched_sizes=True).to('cuda') | |
class ElasticDNN_beit_OfflineClsFMModel(ElasticDNN_OfflineClsFMModel): | |
def generate_md_by_reducing_width(self, reducing_width_ratio, samples: torch.Tensor): | |
# return FM_to_MD_ViT_Util().init_md_from_fm_by_reducing_width_with_perf_test(self.models_dict['main'], | |
# reducing_width_ratio, samples) | |
raise NotImplementedError | |
def get_feature_hook(self) -> LayerActivation: | |
return LayerActivation(get_module(self.models_dict['main'], 'classifier'), True, self.device) | |
def get_elastic_dnn_util(self) -> ElasticDNNUtil: | |
raise NotImplementedError | |
def forward_to_get_task_loss(self, x, y, *args, **kwargs): | |
#x1 = torch.rand(1,3,224,224).to('cuda:1') | |
o1 = self.infer(x) | |
# o2 = self.infer(x1) | |
# print(o1.logits) | |
# print(o2.logits) | |
#print(self.models_dict['main']) | |
#print(o1.logits.shape) | |
#print(F.cross_entropy(self.infer(x).logits, y) ) | |
#formatted_values = [[round(value, 4) for value in row] for row in o1.logits.tolist()] | |
#return F.cross_entropy(torch.tensor(formatted_values).to('cuda'), y) | |
return F.cross_entropy(o1, y) #这个是适用于hugging face模型的计算形式,因为它输出的是一个实例化的类,结果封装在类的属性里,你得去给它调出来。 | |
def get_lora_util(self) -> FMLoRA_Util: | |
return FMLoRA_clip_Util() | |
def get_task_head_params(self): | |
head = get_module(self.models_dict['main'], 'classifier') | |
return list(head.parameters()) | |
if __name__ == '__main__': | |
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/baidu_person_for_cls_task' | |
}, | |
) | |
#from transformers import BeitForImageClassification | |
from clip import clip | |
fm_models_dict_path = save_models_dict_for_init({ | |
'main':clip(scenario.num_classes) | |
},__file__,'clip_pretrained') | |
torch.cuda.set_device(1) | |
device = 'cuda' | |
#print(CvtForImageClassification.from_pretrained("/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/cv/cvt_model",num_labels=scenario.num_classes,ignore_mismatched_sizes=True)) | |
fm_model = ElasticDNN_beit_OfflineClsFMModel('fm', fm_models_dict_path, device) | |
#fm_model = CvtForImageClassification.from_pretrained("/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/cv/cvt_model",num_labels=scenario.num_classes,ignore_mismatched_sizes=True).to(device) | |
models = { | |
'fm':fm_model | |
} | |
import sys | |
fm_lora_alg = ElasticDNN_FMLoRAAlg(models, get_res_save_dir(__file__, tag=sys.argv[0])) | |
from utils.dl.common.lr_scheduler import get_linear_schedule_with_warmup | |
fm_lora_alg.run(scenario, hyps={ | |
'launch_tbboard': False, | |
'samples_size': (1, 3, 224, 224), | |
'ab_r': 3,#hugging face中的模型封装得特别严实,自注意力层里面,qkv是分开的,注意这个对应的倍数不要设置太高 | |
'train_batch_size': 256, | |
'val_batch_size': 512, | |
'num_workers': 16, | |
'optimizer': 'Adam', | |
'optimizer_args': {'lr': 1e-4, 'betas': [0.9, 0.999]},#不同的模型,注意调调学习率啊 | |
'scheduler': 'LambdaLR', | |
'scheduler_args': {'lr_lambda': get_linear_schedule_with_warmup(10000, 70000)}, | |
'num_iters': 8000, | |
'val_freq': 200 | |
} | |
) | |