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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
})
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