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from typing import List |
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from data.dataloader import build_dataloader |
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from methods.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel |
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import torch |
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import sys |
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from torch import nn |
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from methods.elasticdnn.api.model import ElasticDNN_OfflineSegFMModel, ElasticDNN_OfflineSegMDModel |
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from methods.elasticdnn.api.algs.md_pretraining_wo_fbs import ElasticDNN_MDPretrainingWoFBSAlg |
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from methods.elasticdnn.model.base import ElasticDNNUtil |
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from methods.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util |
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from methods.elasticdnn.pipeline.offline.fm_to_md.vit import FM_to_MD_ViT_Util |
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from methods.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util |
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from methods.elasticdnn.pipeline.offline.fm_lora.vit import FMLoRA_ViT_Util |
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from methods.elasticdnn.model.vit import ElasticViTUtil |
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from utils.common.file import ensure_dir |
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from utils.dl.common.model import LayerActivation, get_module, get_parameter |
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from utils.common.exp import save_models_dict_for_init, get_res_save_dir |
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from data import build_scenario |
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from utils.dl.common.loss import CrossEntropyLossSoft |
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import torch.nn.functional as F |
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from utils.dl.common.env import create_tbwriter |
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import os |
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from utils.common.log import logger |
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from utils.common.data_record import write_json |
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from methods.feat_align.main import OnlineFeatAlignModel, FeatAlignAlg |
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import tqdm |
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from methods.feat_align.mmd import mmd_rbf |
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from experiments.utils.elasticfm_da import init_online_model, elasticfm_da |
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device = 'cuda' |
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app_name = 'seg' |
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sd_sparsity = 0.8 |
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settings = { |
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'involve_fm': True |
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} |
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scenario = build_scenario( |
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source_datasets_name=['GTA5', 'SuperviselyPerson'], |
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target_datasets_order=['Cityscapes', 'BaiduPerson'] * 15, |
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da_mode='close_set', |
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data_dirs={ |
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'GTA5': '/data/zql/datasets/GTA-ls-copy/GTA5', |
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'SuperviselyPerson': '/data/zql/datasets/supervisely_person/Supervisely Person Dataset', |
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'Cityscapes': '/data/zql/datasets/cityscape/', |
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'BaiduPerson': '/data/zql/datasets/baidu_person/clean_images/' |
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}, |
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) |
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from experiments.elasticdnn.vit_b_16.online_new.seg.model import ElasticDNN_SegOnlineModel |
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elasticfm_model = ElasticDNN_SegOnlineModel('cls', init_online_model( |
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'experiments/elasticdnn/vit_b_16/offline/fm_to_md/seg/results/seg_md_index.py/20230613/999995-093643-new_md_structure/models/fm_best.pt', |
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'experiments/elasticdnn/vit_b_16/offline/fm_to_md/seg/results/seg_md_index.py/20230613/999995-093643-new_md_structure/models/md_best.pt', |
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'seg', __file__ |
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), device, { |
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'md_to_fm_alpha': 0.1, |
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'fm_to_md_alpha': 0.1 |
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}, scenario.num_classes) |
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da_alg = FeatAlignAlg |
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from experiments.elasticdnn.vit_b_16.online_new.seg.model import SegOnlineFeatAlignModel |
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da_model = SegOnlineFeatAlignModel |
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da_alg_hyp = {'Cityscapes': { |
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'train_batch_size': 16, |
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'val_batch_size': 128, |
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'num_workers': 16, |
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'optimizer': 'AdamW', |
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'optimizer_args': {'lr': 3e-6, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, |
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'scheduler': '', |
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'scheduler_args': {}, |
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'num_iters': 100, |
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'val_freq': 20, |
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'sd_sparsity': 0.8, |
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'feat_align_loss_weight': 0.3 |
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}, 'BaiduPerson': { |
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'train_batch_size': 16, |
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'val_batch_size': 128, |
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'num_workers': 16, |
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'optimizer': 'AdamW', |
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'optimizer_args': {'lr': 1e-7, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, |
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'scheduler': '', |
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'scheduler_args': {}, |
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'num_iters': 100, |
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'val_freq': 20, |
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'sd_sparsity': 0.8, |
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'feat_align_loss_weight': 0.3 |
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}} |
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elasticfm_da( |
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[app_name], |
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[scenario], |
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[elasticfm_model], |
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[da_alg], |
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[da_alg_hyp], |
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[da_model], |
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device, |
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settings, |
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__file__, |
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sys.argv[1] |
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) |
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