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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 new_impl.cv.elasticdnn.pipeline.offline.fm_lora.vit import FMLoRA_ViT_Util
from new_impl.cv.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util
from new_impl.cv.elasticdnn.pipeline.offline.fm_to_md.vit import FM_to_MD_ViT_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_ViT_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)
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:
return ElasticViTUtil()
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.logits, 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_ViT_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 CvtForImageClassification
fm_models_dict_path = save_models_dict_for_init({
'main':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)
},__file__,'cvt_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_ViT_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，注意这个对应的层数不要设置太高
'train_batch_size': 16,
'val_batch_size': 32,
'num_workers': 16,
'optimizer': 'Adam',
'optimizer_args': {'lr': 1e-2, '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': 400
}
)
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