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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.gem.gem_el_vilt import GEMAlg |
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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_cl import init_online_model, elasticfm_cl |
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from data import build_cl_scenario, build_scenario |
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device = 'cuda' |
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app_name = 'vqa' |
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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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target_datasets = ['VQAv2_split1_c_gaussian_noise', 'VQAv2_split1_c_shot_noise', 'VQAv2_split1_c_impulse_noise', 'VQAv2_split1_c_defocus_blur', 'VQAv2_split1_c_glass_blur', 'VQAv2_split1_c_motion_blur', 'VQAv2_split1_c_zoom_blur', 'VQAv2_split1_c_snow', 'VQAv2_split1_c_frost', 'VQAv2_split1_c_fog', 'VQAv2_split1_c_brightness', 'VQAv2_split1_c_contrast', 'VQAv2_split1_c_elastic_transform', 'VQAv2_split1_c_pixelate', 'VQAv2_split1_c_jpeg_compression', 'VQAv2_split1_c_speckle_noise', 'VQAv2_split1_c_gaussian_blur', 'VQAv2_split1_c_spatter', 'VQAv2_split1_c_saturate'] * 2 |
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target_datasets = target_datasets[0: 30] |
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assert len(target_datasets) == 30 |
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scenario = build_scenario( |
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source_datasets_name=['VQAv2_split1'], |
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target_datasets_order=target_datasets, |
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da_mode='close_set', |
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data_dirs={ |
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k: '/data/zql/datasets/vqav2vv' for k in ['VQAv2_split1', 'VQAv2_split1_c_gaussian_noise', 'VQAv2_split1_c_shot_noise', 'VQAv2_split1_c_impulse_noise', 'VQAv2_split1_c_defocus_blur', 'VQAv2_split1_c_glass_blur', 'VQAv2_split1_c_motion_blur', 'VQAv2_split1_c_zoom_blur', 'VQAv2_split1_c_snow', 'VQAv2_split1_c_frost', 'VQAv2_split1_c_fog', 'VQAv2_split1_c_brightness', 'VQAv2_split1_c_contrast', 'VQAv2_split1_c_elastic_transform', 'VQAv2_split1_c_pixelate', 'VQAv2_split1_c_jpeg_compression', 'VQAv2_split1_c_speckle_noise', 'VQAv2_split1_c_gaussian_blur', 'VQAv2_split1_c_spatter', 'VQAv2_split1_c_saturate'] |
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}, |
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) |
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scenario = build_cl_scenario( |
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da_scenario=scenario, |
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target_datasets_name=['VQAv2_split2'], |
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num_classes_per_task=20, |
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max_num_tasks=30, |
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data_dirs={ |
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'VQAv2_split2': '/data/zql/datasets/vqav2vv' |
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}, |
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sanity_check=True |
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) |
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from experiments.elasticdnn.vilt.online.vqa_cl.model import ElasticDNN_VQAOnlineModel |
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elasticfm_model = ElasticDNN_VQAOnlineModel('cls', init_online_model( |
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'experiments/elasticdnn/vilt/offline/fm_to_md/vqa/results/vqa_w_fbs_index.py/20230731/999999-095720-trial/models/fm_best.pt', |
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'experiments/elasticdnn/vilt/offline/fm_to_md/vqa/results/vqa_w_fbs_index.py/20230731/999999-095720-trial/models/md_best.pt', |
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'cls', __file__ |
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), device, { |
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'md_to_fm_alpha': 0.1, |
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'fm_to_md_alpha': 0.001 |
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}) |
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da_alg = GEMAlg |
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from experiments.elasticdnn.vilt.online.vqa_cl.model import VQAOnlineGEMModel |
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da_model = VQAOnlineGEMModel |
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da_alg_hyp = { |
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'train_batch_size': 64, |
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'val_batch_size': 256, |
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'num_workers': 0, |
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'optimizer': 'AdamW', |
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'optimizer_args': {'lr': 3e-3, 'betas': [0.9, 0.999], 'weight_decay': 0.0}, |
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'scheduler': '', |
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'scheduler_args': {}, |
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'num_iters': 100 * 8, |
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'val_freq': 20 * 8, |
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'n_memories': 64, |
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'n_inputs': 3 * 224 * 224, |
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'margin': 0.5, |
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'num_my_iters': 1, |
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'sd_sparsity': sd_sparsity, |
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} |
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elasticfm_cl( |
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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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