import torch import torch.nn as nn from methods.elasticdnn.api.algs.fm_lora import ElasticDNN_FMLoRAAlg 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_lora.base import FMLoRA_Util from glip import ElasticGLIPUtil, FMLoRA_GLIP_Util, FM_to_MD_GLIP_Util, ElasticDNN_OfflineMMDetFMModel, ElasticDNN_OfflineMMDetMDModel 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.model.vit import ElasticViTUtil from methods.elasticdnn.api.algs.md_pretraining_index_v2_train_index_and_md import ElasticDNN_MDPretrainingIndexAlg from utils.dl.common.model import LayerActivation3, get_module, get_parameter from utils.common.exp import save_models_dict_for_init, get_res_save_dir from data import build_scenario import torch.nn.functional as F from maskrcnn_benchmark.structures.bounding_box import BoxList import os from utils.dl.common.loss import CrossEntropyLossSoft from new_impl.cv.feat_align.main_glip import OnlineFeatAlignModel, FeatAlignAlg import tqdm from new_impl.cv.feat_align.mmd import mmd_rbf from new_impl.cv.utils.baseline_da import baseline_da from new_impl.cv.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel os.environ['TOKENIZERS_PARALLELISM'] = 'true' torch.cuda.set_device(0) device = 'cuda' app_name = 'cls' scenario = build_scenario( source_datasets_name=['MM-COCO2017'], target_datasets_order=['MM-CityscapesDet', 'MM-GTA5Det'] * 10, da_mode='close_set', data_dirs={ 'MM-COCO2017': '/data/zql/datasets/coco2017', 'MM-CityscapesDet': '/data/zql/datasets/cityscape', 'MM-GTA5Det': '/data/zql/datasets/GTA-ls-copy/GTA5', }, ) class DetOnlineFeatAlignModel(OnlineFeatAlignModel): def get_trained_params(self): # TODO: elastic fm only train a part of params #qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters() if 'attention.attention.projection_query' in n or 'attention.attention.projection_key' in n or 'attention.attention.projection_value' in n or 'intermediate.dense' in n or 'output.dense' in n] qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters()] return qkv_and_norm_params def get_feature_hook(self): return LayerActivation3(get_module(self.models_dict['main'], 'model.rpn'), False, self.device) def forward_to_get_task_loss(self, x, y): loss_dict = self.infer(x) losses = sum(loss for loss in loss_dict.values()) # print(losses) return losses def get_mmd_loss(self, f1, f2): # f1 = f1.view(f1.shape[0], -1) # f2 = f2.view(f2.shape[0], -1) return mmd_rbf(f1, f2) def infer(self, x, *args, **kwargs): return self.models_dict['main'](**x) def get_accuracy(self, test_loader, *args, **kwargs): # print('DeeplabV3: start test acc') _d = test_loader.dataset imgsz = _d.cocods.img_size cls_num = len(_d.cocods.class_ids) # num_classes = len(_d.cls_names) from data import build_dataloader if _d.__class__.__name__ == 'MergedDataset': # print('\neval on merged datasets') datasets = _d.datasets if self.collate_fn is None: test_loaders = [build_dataloader(d, test_loader.batch_size, test_loader.num_workers, False, None, collate_fn=None) for d in datasets] else: test_loaders = [build_dataloader(d, test_loader.batch_size, test_loader.num_workers, False, None, collate_fn=self.collate_fn) 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 evaluator import COCOEvaluator, MMCOCODecoder from utils.common.others import HiddenPrints with torch.no_grad(): with HiddenPrints(): evaluator = COCOEvaluator( dataloader=test_loader, img_size=imgsz, confthre=0.01, nmsthre=0.65, num_classes=cls_num, testdev=False ) res = evaluator.evaluate(model, False, False, decoder=MMCOCODecoder) map50 = res[1] # print('eval info', res[-1]) return map50 from glip import glip_model, build_transform, run_ner, collect_mm_fn cfg_path = 'new_impl/cv/glip/object_detection/pretrained_model/glip_Swin_T_O365_GoldG.yaml' model_path = 'new_impl/cv/glip/object_detection/pretrained_model/glip_tiny_model_o365_goldg_cc_sbu.pth' config, _ = glip_model(cfg_path, model_path) transform = build_transform(config, None) da_alg = FeatAlignAlg from utils.dl.common.lr_scheduler import get_linear_schedule_with_warmup #from new_impl.cv.model import ClsOnlineFeatAlignModel da_model = DetOnlineFeatAlignModel( app_name, 'new_impl/cv/glip/object_detection/results/det_md_wo_fbs.py/20231129/999999-153230-results/models/md_best.pt', device ) da_alg_hyp = { 'MM-GTA5Det': { 'train_batch_size': 8, 'val_batch_size': 1, 'num_workers': 8, 'optimizer': 'AdamW', 'optimizer_args': {'lr': 2e-6, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, 'scheduler': '', 'scheduler_args': {}, 'num_iters': 100, 'val_freq': 20, 'feat_align_loss_weight': 0.3, 'transform':transform }, 'MM-CityscapesDet': { 'train_batch_size': 8, 'val_batch_size': 1, 'num_workers': 8, 'optimizer': 'AdamW', 'optimizer_args': {'lr': 2e-6, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, 'scheduler': '', 'scheduler_args': {}, 'num_iters': 100, 'val_freq': 20, 'feat_align_loss_weight': 0.3, 'transform':transform }, } baseline_da( app_name, scenario, da_alg, da_alg_hyp, da_model, device, __file__, "results", collate_fn=collect_mm_fn )