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训练问题 #4
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应该是兼容的,可以直接训练 |
您好!我把run_train.sh修改成如下: #larger batch id="dense_box_bn"$1 python train.py --caption_model denseatt --input_json data/chinese_talk.json --input_label_h5 data/chinese_talk_label.h5 --input_fc_dir data/chinese_talk_fc --input_att_dir data/chinese_talk_att --seq_per_img 512 --batch_size 50 --beam_size 1 --learning_rate 5e-4 --learning_rate_decay_start 0 --scheduled_sampling_start 0 --checkpoint_path $ckpt_path $start_from --save_checkpoint_every 3000 --language_eval 1 --val_images_use 10000 --max_epoch 37 --rnn_size 1300 --use_bn 1 if [ ! -d xe/$ckpt_path ]; then python train.py --caption_model denseatt --input_json data/chinese_talk.json --input_label_h5 data/chinese_talk_label.h5 --input_fc_dir data/chinese_talk_fc --input_att_dir data/chinese_talk_att --seq_per_img 5 --batch_size 50 --beam_size 1 --learning_rate 5e-5 --learning_rate_decay_start 0 --learning_rate_decay_every 55 --learning_rate_decay_rate 0.1 --scheduled_sampling_start 0 --checkpoint_path $ckpt_path --start_from $ckpt_path --save_checkpoint_every 3000 --language_eval 1 --val_images_use 10000 --self_critical_after 37 --rnn_size 1300 --use_bn 1 运行结果: DataLoader loading json file: data/chinese_talk.json 不知道是训练参数设置问题,还是使用resnet18提取特征导致输出的参数跟后面不能匹配? |
哦,好像不完全兼容。。seq per img不用改。你可以在dataloader的load not时,把14x14x512 reshape成196x512 |
好的,还想再问一个问题:用resnet101提取特征的时候,我的1080显卡(8.1G))每次处理到3000张图片的时候就爆掉了,您当时提取特征的显存多少?以及代码是否设置了多显卡选项? |
1080ti。没有多卡 |
好的,谢谢! |
你好,我也遇到了这个问题,请问是如何解决的,谢谢。 |
您好!我用resnet18提取了数据集的特征,但是不知道怎样训练,好像训练代码是针对于bottom-up-attention的。同时也不是很清楚如何用bottom-up-attention提出AI Challenger训练集的特征
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