23 lines
1.0 KiB
Python
23 lines
1.0 KiB
Python
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from evaluation import evalrank, evalrank2,evalrank3,evalrank_vse,evalrank_maxpool,evalrank_f_c,evalrank_avgpool,evalrank_f_c2
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import numpy as np
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from transformers import BertTokenizer
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "5"
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# os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
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#f30k数据集和coco数据集上实验
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evalrank_f_c("../SCAN-master/data/f30k_precomp","f30k")
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# evalrank_f_c("../SCAN-master/data/coco_precomp","coco")
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#evalrank_f_c2("../SCAN-master/data/coco_precomp","f30k")
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# evalrank_f_c2("../SCAN-master/data/f30k_precomp","coco")
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#消融实验
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#evalrank_vse("./runs/bert_adam_bcan_gpo_vseinfty_vse_data_3045_lr2_vse_gpo/model_best.pth.tar", "../SCAN-master/data/", "test")
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#evalrank("./runs/bert_adam_bcan_gpo_vseinfty_union_2035/model_best.pth.tar", "../SCAN-master/data/", "test")
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#池化方式对比实验
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#evalrank_avgpool("./runs/bert_adam_bcan_gpo_vseinfty_bcan/model_best.pth.tar", "../SCAN-master/data/", "test")
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#evalrank_maxpool("./runs/bert_adam_bcan_gpo_vseinfty_bcan/model_best.pth.tar", "../SCAN-master/data/", "test")
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