from typing import List from data.dataloader import build_dataloader # from methods.elasticdnn.api.online_model import ElasticDNN_OnlineModel from new_impl.cv.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel 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 new_impl.cv.elasticdnn.pipeline.offline.fm_to_md.vit import FM_to_MD_ViT_Util 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 sam import ElasticsamUtil 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 new_impl.cv.feat_align.main import OnlineFeatAlignModel, FeatAlignAlg import tqdm from new_impl.cv.feat_align.mmd import mmd_rbf from new_impl.cv.utils.elasticfm_da import init_online_model, elasticfm_da torch.cuda.set_device(1) device = 'cuda' app_name = 'seg' sd_sparsity = 0. settings = { 'involve_fm': True } 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/' }, ) class ElasticDNN_SegOnlineModel(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): device = self.device self.to_eval_mode() from methods.elasticdnn.api.model import StreamSegMetrics metrics = StreamSegMetrics(self.num_classes) metrics.reset() import tqdm pbar = tqdm.tqdm(enumerate(test_loader), total=len(test_loader), leave=False, dynamic_ncols=True) with torch.no_grad(): for batch_index, (x, y) in pbar: x, y = x.to(device, dtype=x.dtype, non_blocking=True, copy=False), \ y.to(device, dtype=y.dtype, non_blocking=True, copy=False) output = self.infer(x) pred = output.detach().max(dim=1)[1].cpu().numpy() metrics.update((y + 0).cpu().numpy(), pred) res = metrics.get_results() pbar.set_description(f'cur batch mIoU: {res["Mean Acc"]:.4f}') res = metrics.get_results() return res['Mean Acc'] def get_elastic_dnn_util(self) -> ElasticDNNUtil: return ElasticsamUtil() 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 'layernorm' 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]) + '.fc' fm_qkv = get_module(fm, fm_qkv_name) fm_abs_name = '.'.join(ss[0: -1]) + '.ab' 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(fm_abs[1].weight @ fm_abs[0].weight)) 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.lin1' 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.lin2' 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]) + '.fc' 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]) + '.ab' 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 'layernorm' 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.lin1.0.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.lin2' 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 SegOnlineFeatAlignModel(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'], 'head'), False, self.device) def forward_to_get_task_loss(self, x, y): return F.cross_entropy(self.infer(x), y) def get_mmd_loss(self, f1, f2): return mmd_rbf(f1.flatten(1), f2.flatten(1)) def infer(self, x, *args, **kwargs): 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] return qkv_and_norm_params def infer(self, x, *args, **kwargs): return self.models_dict['main'](x) def get_accuracy(self, test_loader, *args, **kwargs): device = self.device self.to_eval_mode() from methods.elasticdnn.api.model import StreamSegMetrics metrics = StreamSegMetrics(self.num_classes) metrics.reset() import tqdm pbar = tqdm.tqdm(enumerate(test_loader), total=len(test_loader), leave=False, dynamic_ncols=True) with torch.no_grad(): for batch_index, (x, y) in pbar: x, y = x.to(device, dtype=x.dtype, non_blocking=True, copy=False), \ y.to(device, dtype=y.dtype, non_blocking=True, copy=False) output = self.infer(x) pred = output.detach().max(dim=1)[1].cpu().numpy() metrics.update((y + 0).cpu().numpy(), pred) res = metrics.get_results() pbar.set_description(f'cur batch mIoU: {res["Mean Acc"]:.4f}') res = metrics.get_results() return res['Mean Acc'] #from new_impl.cv.model import ElasticDNN_ClsOnlineModel elasticfm_model = ElasticDNN_SegOnlineModel('cls', init_online_model( # 'experiments/elasticdnn/vit_b_16/offline/fm_to_md/results/cls_md_index.py/20230529/star_999997-154037-only_prune_mlp/models/fm_best.pt', # 'experiments/elasticdnn/vit_b_16/offline/fm_to_md/results/cls_md_index.py/20230529/star_999997-154037-only_prune_mlp/models/md_best.pt', #'experiments/elasticdnn/vit_b_16/offline/fm_to_md/cls/results/cls_md_index.py/20230617/999992-101343-lr1e-5_index_bug_fixed/models/fm_best.pt', #'experiments/elasticdnn/vit_b_16/offline/fm_to_md/cls/results/cls_md_index.py/20230617/999992-101343-lr1e-5_index_bug_fixed/models/md_best.pt', 'new_impl/cv/sam/results/seg_wo_index.py/20231125/999999-175801-/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/cv/sam/seg_wo_index.py/models/fm_best.pt', 'new_impl/cv/sam/results/seg_wo_index.py/20231125/999999-175801-/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/cv/sam/seg_wo_index.py/models/md_best.pt', 'seg', __file__ ), device, { 'md_to_fm_alpha': 0.1, 'fm_to_md_alpha': 0.1 },scenario.num_classes) da_alg = FeatAlignAlg from utils.dl.common.lr_scheduler import get_linear_schedule_with_warmup #from new_impl.cv.model import ClsOnlineFeatAlignModel da_model = SegOnlineFeatAlignModel da_alg_hyp = {'Cityscapes': { 'train_batch_size': 16, 'val_batch_size': 128, 'num_workers': 16, 'optimizer': 'AdamW', 'optimizer_args': {'lr': 3e-5, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, 'scheduler': '', 'scheduler_args': {}, 'num_iters': 100, 'val_freq': 20, 'sd_sparsity': 0.5, 'feat_align_loss_weight': 0.3 }, 'BaiduPerson': { 'train_batch_size': 16, 'val_batch_size': 128, 'num_workers': 16, 'optimizer': 'AdamW', 'optimizer_args': {'lr': 1e-7,'betas': [0.9, 0.999], 'weight_decay': 0.01}, 'scheduler': '', 'scheduler_args': {}, 'num_iters': 100, 'val_freq': 20, 'sd_sparsity': 0.5, 'feat_align_loss_weight': 0.3 }} elasticfm_da( [app_name], [scenario], [elasticfm_model], [da_alg], [da_alg_hyp], [da_model], device, settings, __file__, sys.argv[0] )