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| from typing import List | |
| from data.dataloader import build_dataloader | |
| # from methods.elasticdnn.api.online_model import ElasticDNN_OnlineModel | |
| from methods.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel | |
| import torch | |
| import sys | |
| from torch import nn | |
| from methods.elasticdnn.api.model import ElasticDNN_OfflineSegFMModel, ElasticDNN_OfflineSegMDModel | |
| 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_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.pipeline.offline.fm_lora.base import FMLoRA_Util | |
| from methods.elasticdnn.pipeline.offline.fm_lora.vit import FMLoRA_ViT_Util | |
| from methods.elasticdnn.model.vit import ElasticViTUtil | |
| from utils.common.file import ensure_dir | |
| 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 | |
| from utils.dl.common.env import create_tbwriter | |
| import os | |
| from utils.common.log import logger | |
| from utils.common.data_record import write_json | |
| # from methods.shot.shot import OnlineShotModel | |
| from methods.feat_align.main import OnlineFeatAlignModel, FeatAlignAlg | |
| import tqdm | |
| from methods.feat_align.mmd import mmd_rbf | |
| class ElasticDNN_DetOnlineModel(ElasticDNN_OnlineModel): | |
| def __init__(self, name: str, models_dict_path: str, device: str, ab_options: dict, num_classes: int): | |
| super().__init__(name, models_dict_path, device, ab_options) | |
| self.num_classes = num_classes | |
| def get_accuracy(self, test_loader, *args, **kwargs): | |
| # print('DeeplabV3: start test acc') | |
| _d = test_loader.dataset | |
| from data import build_dataloader | |
| if _d.__class__.__name__ == 'MergedDataset': | |
| # print('\neval on merged datasets') | |
| datasets = _d.datasets | |
| test_loaders = [build_dataloader(d, test_loader.batch_size, test_loader.num_workers, False, None) 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 dnns.yolov3.coco_evaluator import COCOEvaluator | |
| from utils.common.others import HiddenPrints | |
| with torch.no_grad(): | |
| with HiddenPrints(): | |
| evaluator = COCOEvaluator( | |
| dataloader=test_loader, | |
| img_size=(224, 224), | |
| confthre=0.01, | |
| nmsthre=0.65, | |
| num_classes=self.num_classes, | |
| testdev=False | |
| ) | |
| res = evaluator.evaluate(model, False, False) | |
| map50 = res[1] | |
| # print('eval info', res[-1]) | |
| return map50 | |
| def get_elastic_dnn_util(self) -> ElasticDNNUtil: | |
| return ElasticViTUtil() | |
| def get_fm_matched_param_of_md_param(self, md_param_name): | |
| # only between qkv.weight, norm.weight/bias | |
| self_param_name = md_param_name | |
| fm = self.models_dict['fm'] | |
| if any([k in self_param_name for k in ['fbs', 'cls_token', 'pos_embed']]): | |
| return None | |
| p = get_parameter(self.models_dict['md'], self_param_name) | |
| if p.dim() == 0: | |
| return None | |
| elif p.dim() == 1 and 'norm' in self_param_name and 'weight' in self_param_name: | |
| if self_param_name.startswith('norm'): | |
| return None | |
| return get_parameter(fm, self_param_name) | |
| # 1. xx.qkv.to_qkv.yy to xx.qkv.qkv.aa and xx.qkv.abs.zz | |
| if 'qkv.weight' in self_param_name: | |
| ss = self_param_name.split('.') | |
| fm_qkv_name = '.'.join(ss[0: -1]) + '.qkv' | |
| fm_qkv = get_module(fm, fm_qkv_name) | |
| fm_abs_name = '.'.join(ss[0: -1]) + '.abs' | |
| fm_abs = get_module(fm, fm_abs_name) | |
| # NOTE: unrecoverable operation! multiply LoRA parameters to allow it being updated in update_fm_param() | |
| # TODO: if fm will be used for inference, _mul_lora_weight will not be applied! | |
| if not hasattr(fm_abs, '_mul_lora_weight'): | |
| logger.debug(f'set _mul_lora_weight in {fm_abs_name}') | |
| setattr(fm_abs, '_mul_lora_weight', | |
| nn.Parameter(torch.cat([(_abs[0].weight.T @ _abs[1].weight.T).T for _abs in fm_abs], dim=0))) | |
| return torch.cat([ | |
| fm_qkv.weight.data, # task-agnositc params | |
| fm_abs._mul_lora_weight.data # 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 and 'weight' in self_param_name: | |
| fm_param_name = self_param_name.replace('.linear', '') | |
| return get_parameter(fm, fm_param_name) | |
| elif 'mlp.fc2' in self_param_name and 'weight' in self_param_name: | |
| fm_param_name = self_param_name | |
| return get_parameter(fm, fm_param_name) | |
| else: | |
| # return get_parameter(fm, self_param_name) | |
| return None | |
| def update_fm_param(self, md_param_name, cal_new_fm_param_by_md_param): | |
| if not 'qkv.weight' in md_param_name: | |
| matched_fm_param_ref = self.get_fm_matched_param_of_md_param(md_param_name) | |
| matched_fm_param_ref.copy_(cal_new_fm_param_by_md_param) | |
| else: | |
| new_fm_attn_weight, new_fm_lora_weight = torch.chunk(cal_new_fm_param_by_md_param, 2, 0) | |
| ss = md_param_name.split('.') | |
| fm = self.models_dict['fm'] | |
| # update task-agnostic parameters | |
| fm_qkv_name = '.'.join(ss[0: -1]) + '.qkv' | |
| fm_qkv = get_module(fm, fm_qkv_name) | |
| fm_qkv.weight.data.copy_(new_fm_attn_weight) | |
| # update task-specific parameters | |
| fm_abs_name = '.'.join(ss[0: -1]) + '.abs' | |
| fm_abs = get_module(fm, fm_abs_name) | |
| fm_abs._mul_lora_weight.data.copy_(new_fm_lora_weight) # TODO: this will not be applied in inference! | |
| def get_md_matched_param_of_fm_param(self, fm_param_name): | |
| return super().get_md_matched_param_of_fm_param(fm_param_name) | |
| def get_md_matched_param_of_sd_param(self, sd_param_name): | |
| # only between qkv.weight, norm.weight/bias | |
| self_param_name = sd_param_name | |
| md = self.models_dict['md'] | |
| if any([k in self_param_name for k in ['fbs', 'cls_token', 'pos_embed']]): | |
| return None | |
| p = get_parameter(self.models_dict['sd'], self_param_name) | |
| if p.dim() == 0: | |
| return None | |
| elif p.dim() == 1 and 'norm' in self_param_name and 'weight' in self_param_name: | |
| return get_parameter(md, self_param_name) | |
| # 1. xx.qkv.to_qkv.yy to xx.qkv.qkv.aa and xx.qkv.abs.zz | |
| if 'qkv.weight' in self_param_name: | |
| return get_parameter(md, self_param_name) | |
| # 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.fc1.0.weight' in self_param_name: | |
| fm_param_name = '.'.join(self_param_name.split('.')[0: -2]) + '.linear.weight' | |
| return get_parameter(md, fm_param_name) | |
| elif 'mlp.fc2' in self_param_name and 'weight' in self_param_name: | |
| fm_param_name = self_param_name | |
| return get_parameter(md, fm_param_name) | |
| else: | |
| # return get_parameter(fm, self_param_name) | |
| return None | |
| def get_task_head_params(self): | |
| head = get_module(self.models_dict['sd'], 'head') | |
| return list(head.parameters()) | |
| class DetOnlineFeatAlignModel(OnlineFeatAlignModel): | |
| def __init__(self, name: str, models_dict_path: str, device: str, num_classes): | |
| super().__init__(name, models_dict_path, device) | |
| self.num_classes = num_classes | |
| def get_feature_hook(self): | |
| return LayerActivation(get_module(self.models_dict['main'], 'blocks.11.drop_path2'), False, self.device) | |
| def forward_to_get_task_loss(self, x, y): | |
| self.to_train_mode() | |
| return self.models_dict['main'](x, y)['total_loss'] | |
| def get_mmd_loss(self, f1, f2): | |
| return mmd_rbf(f1.flatten(1), f2.flatten(1)) | |
| def infer(self, x, *args, **kwargs): | |
| if len(args) > 0: | |
| return self.models_dict['main'](x, *args) # forward(x, label) | |
| return self.models_dict['main'](x) | |
| def get_trained_params(self): | |
| qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters() if 'qkv.weight' in n or 'norm' in n or 'mlp' in n or 'head' in n] | |
| return qkv_and_norm_params | |
| def get_accuracy(self, test_loader, *args, **kwargs): | |
| # print('DeeplabV3: start test acc') | |
| _d = test_loader.dataset | |
| from data import build_dataloader | |
| if _d.__class__.__name__ == 'MergedDataset': | |
| print('\neval on merged datasets') | |
| datasets = _d.datasets | |
| test_loaders = [build_dataloader(d, test_loader.batch_size, test_loader.num_workers, False, None) 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 dnns.yolov3.coco_evaluator import COCOEvaluator | |
| from utils.common.others import HiddenPrints | |
| with torch.no_grad(): | |
| with HiddenPrints(): | |
| evaluator = COCOEvaluator( | |
| dataloader=test_loader, | |
| img_size=(224, 224), | |
| confthre=0.01, | |
| nmsthre=0.65, | |
| num_classes=self.num_classes, | |
| testdev=False | |
| ) | |
| res = evaluator.evaluate(model, False, False) | |
| map50 = res[1] | |
| # print('eval info', res[-1]) | |
| return map50 |