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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.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 | |
class ElasticDNN_ViT_OfflineSegFMModel(ElasticDNN_OfflineSegFMModel): | |
def generate_md_by_reducing_width(self, reducing_width_ratio, samples: torch.Tensor): | |
return FM_to_MD_sam_Util().init_md_from_fm_by_reducing_width_with_perf_test(self.models_dict['main'], | |
reducing_width_ratio, samples).to(self.device) | |
def get_feature_hook(self) -> LayerActivation: | |
return LayerActivation(get_module(self.models_dict['main'], 'head'), True, self.device) | |
def get_elastic_dnn_util(self) -> ElasticDNNUtil: | |
return ElasticsamUtil() | |
def forward_to_get_task_loss(self, x, y, *args, **kwargs): | |
return F.cross_entropy(self.infer(x), y) | |
def get_lora_util(self) -> FMLoRA_Util: | |
return FMLoRA_sam_Util() | |
def get_task_head_params(self): | |
head = get_module(self.models_dict['main'], 'head') | |
return list(head.parameters()) | |
class ElasticDNN_ViT_OfflineSegMDModel(ElasticDNN_OfflineSegMDModel): | |
def get_feature_hook(self) -> LayerActivation: | |
return LayerActivation(get_module(self.models_dict['main'], 'head'), True, self.device) | |
def forward_to_get_task_loss(self, x, y, *args, **kwargs): | |
return F.cross_entropy(self.infer(x), y) | |
def get_distill_loss(self, student_output, teacher_output): | |
return F.mse_loss(student_output, teacher_output) | |
def get_matched_param_of_fm(self, self_param_name, fm: nn.Module): | |
if any([k in self_param_name for k in ['fbs', 'cls_token', 'pos_embed']]): | |
return None | |
# 1. xx.qkv.to_qkv.yy to xx.qkv.qkv.aa and xx.qkv.abs.zz | |
if 'to_qkv.weight' in self_param_name: | |
ss = self_param_name.split('.') | |
fm_qkv_name = '.'.join(ss[0: -2]) + '.fc' | |
fm_qkv = get_module(fm, fm_qkv_name) | |
fm_abs_name = '.'.join(ss[0: -2]) + '.ab' | |
fm_abs = get_module(fm, fm_abs_name) | |
return torch.cat([ | |
fm_qkv.weight.data, # task-agnositc params | |
torch.cat([(_abs[0].weight.T @ _abs[1].weight.T).T for _abs in fm_abs], dim=0) # task-specific params (LoRA) | |
], dim=0) | |
elif 'to_qkv.bias' in self_param_name: | |
ss = self_param_name.split('.') | |
fm_qkv_name = '.'.join(ss[0: -2]) + '.qkv.bias' | |
return get_parameter(fm, fm_qkv_name) | |
elif 'mlp.fc1' in self_param_name: | |
fm_param_name = self_param_name.replace('.linear', '') | |
return get_parameter(fm, fm_param_name) | |
else: | |
return get_parameter(fm, self_param_name) | |
if __name__ == '__main__': | |
from utils.dl.common.env import set_random_seed | |
set_random_seed(1) | |
# 3. init scenario | |
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/' | |
}, | |
) | |
# 1. init model | |
# from dnns.deeplabv3.head import modify_forward_head | |
# modify_forward_head() # TODO: bring a bug | |
from dnns.vit import vit_b_16 | |
fm_models_dict_path = 'new_impl/cv/sam/results/seg.py/20231123/999983-212616/models/fm_best.pt' | |
fm_models = torch.load(fm_models_dict_path) | |
# for n,m in fm_models['main'].named_modules(): | |
# print(n) | |
# from utils.dl.common.model import set_module | |
# set_module( | |
# fm_models['main'], | |
# 'norm', | |
# nn.Sequential( | |
# get_module(fm_models['main'], 'norm'), | |
# get_module(fm_models['main'], 'head') | |
# ) | |
# ) | |
# set_module(fm_models['main'], 'head', nn.Identity()) | |
# fm_models['main'].forward = fm_models['main'].forward_features | |
fm_models_dict_path = save_models_dict_for_init(fm_models, __file__, 'fm_sam_seg_lora') | |
md_models_dict_path = save_models_dict_for_init({ | |
'main': -1 | |
}, __file__, 'md_sam_none') | |
device = 'cuda' | |
fm_model = ElasticDNN_ViT_OfflineSegFMModel('fm', fm_models_dict_path, device, scenario.num_classes) | |
md_model = ElasticDNN_ViT_OfflineSegMDModel('md', md_models_dict_path, device, scenario.num_classes) | |
# 2. init alg | |
models = { | |
'fm': fm_model, | |
'md': md_model | |
} | |
fm_to_md_alg = ElasticDNN_MDPretrainingWoFBSAlg(models, get_res_save_dir(__file__, None)) | |
from utils.dl.common.lr_scheduler import get_linear_schedule_with_warmup | |
fm_to_md_alg.run(scenario, hyps={ | |
'launch_tbboard': False, | |
'samples_size': (1, 3, 224, 224), | |
'generate_md_width_ratio': 8, | |
'train_batch_size': 16, | |
'val_batch_size': 128, | |
'num_workers': 16, | |
'optimizer': 'AdamW', | |
'optimizer_args': {'lr': 5e-4, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, | |
'scheduler': 'LambdaLR', | |
'scheduler_args': {'lr_lambda': get_linear_schedule_with_warmup(10000, 70000)}, | |
'num_iters': 80000, | |
'val_freq': 1000, | |
'distill_loss_weight': 1.0 | |
}) | |