ALBEF图文检索

2022-04-11 09:00:10 阅读：268 来源： 互联网

标签：检索 00 mem max 8621 eta time ALBEF 图文

代码地址https://github.com/salesforce/ALBEF.git

目的使用少量的图片，训练模型。

准备文件（1）

/Users/xuehuiping/git/ALBEF/configs/Retrieval_flickr.yaml

train_file:  ['/Users/xuehuiping/dataset/flickr_sample/flickr30k_train.json']
val_file: '/Users/xuehuiping/dataset/flickr_sample/flickr30k_val.json'
test_file: '/Users/xuehuiping/dataset/flickr_sample/flickr30k_test.json'
image_root: '/Users/xuehuiping/dataset/flickr_sample/'

bert_config: 'configs/config_bert.json'

image_res: 384
batch_size_train: 4 #32
batch_size_test: 8 #64

queue_size: 65536
momentum: 0.995
vision_width: 768
embed_dim: 256
temp: 0.07
k_test: 128

alpha: 0.4
distill: True
warm_up: True

optimizer: {opt: adamW, lr: 1e-5, weight_decay: 0.02} 
schedular: {sched: cosine, lr: 1e-5, epochs: 10, min_lr: 1e-6, decay_rate: 1, warmup_lr: 1e-5, warmup_epochs: 1, cooldown_epochs: 0}

数据目录截图

图片路径写到flickr30k-images所在目录即可，不需要包括flickr30k-images。

test.json的格式是：一张图片对应5个caption
val.json也是这个格式。

{
        "image": "flickr30k-images/183647966.jpg",
        "caption": [
            "A man waring a polo shirt fixes a ticket machine while persons line up at an adjoining machine.",
            "A woman dressed in black with shopping bags is waiting on the sidewalk.",
            "A male worker with his tool box is kneeling next to two women.",
            "A man working on a ticket machine as two women stand near.",
            "A man working on a ticket box."
        ]
    },

train.json的格式是：一张图片对应1个caption

    {
        "image": "flickr30k-images/1000092795.jpg",
        "caption": "Two young guys with shaggy hair look at their hands while hanging out in the yard.",
        "image_id": 0
    },

运行脚本

python Retrieval.py \
--config ./configs/Retrieval_flickr.yaml \
--output_dir output/Retrieval_flickr_sample \

Retrieval.py主要包含

（1）创建数据集create_dataset
（2）创建数据加载器create_loader
（3）创建模型model = ALBEF()
（4）创建优化器create_optimizer
（5）开始迭代：train、evaluation

部分日志

……
Train Epoch: [9]  [  0/362]  eta: 0:03:35  lr: 0.000001  loss_itm: 0.4073  loss_ita: 7.9941  time: 0.5952  data: 0.2224  max mem: 8621
Train Epoch: [9]  [ 50/362]  eta: 0:01:44  lr: 0.000001  loss_itm: 0.4031  loss_ita: 8.4971  time: 0.3288  data: 0.0001  max mem: 8621
Train Epoch: [9]  [100/362]  eta: 0:01:26  lr: 0.000001  loss_itm: 0.6508  loss_ita: 8.5376  time: 0.3291  data: 0.0001  max mem: 8621
Train Epoch: [9]  [150/362]  eta: 0:01:10  lr: 0.000001  loss_itm: 0.3102  loss_ita: 7.9713  time: 0.3301  data: 0.0001  max mem: 8621
Train Epoch: [9]  [200/362]  eta: 0:00:53  lr: 0.000001  loss_itm: 0.5846  loss_ita: 8.0293  time: 0.3289  data: 0.0001  max mem: 8621
Train Epoch: [9]  [250/362]  eta: 0:00:37  lr: 0.000001  loss_itm: 0.2595  loss_ita: 7.7533  time: 0.3299  data: 0.0001  max mem: 8621
Train Epoch: [9]  [300/362]  eta: 0:00:20  lr: 0.000001  loss_itm: 0.4346  loss_ita: 8.0876  time: 0.3306  data: 0.0002  max mem: 8621
Train Epoch: [9]  [350/362]  eta: 0:00:03  lr: 0.000001  loss_itm: 0.5066  loss_ita: 8.3133  time: 0.3303  data: 0.0001  max mem: 8621
Train Epoch: [9]  [361/362]  eta: 0:00:00  lr: 0.000001  loss_itm: 0.3811  loss_ita: 8.0253  time: 0.3280  data: 0.0001  max mem: 8621
Train Epoch: [9] Total time: 0:01:59 (0.3305 s / it)
Averaged stats: lr: 0.0000  loss_itm: 0.4907  loss_ita: 8.1612
Computing features for evaluation...
Evaluation:  [  0/100]  eta: 0:00:01    time: 0.0116  data: 0.0003  max mem: 8621
Evaluation:  [ 50/100]  eta: 0:00:00    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [ 99/100]  eta: 0:00:00    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation: Total time: 0:00:01 (0.0117 s / it)
Evaluation:  [  0/500]  eta: 0:00:05    time: 0.0102  data: 0.0008  max mem: 8621
Evaluation:  [ 50/500]  eta: 0:00:05    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [100/500]  eta: 0:00:04    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [150/500]  eta: 0:00:04    time: 0.0117  data: 0.0000  max mem: 8621
Evaluation:  [200/500]  eta: 0:00:03    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [250/500]  eta: 0:00:02    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [300/500]  eta: 0:00:02    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [350/500]  eta: 0:00:01    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [400/500]  eta: 0:00:01    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [450/500]  eta: 0:00:00    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [499/500]  eta: 0:00:00    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation: Total time: 0:00:05 (0.0116 s / it)
Evaluation time 0:00:08
Computing features for evaluation...
Evaluation:  [  0/100]  eta: 0:00:01    time: 0.0119  data: 0.0004  max mem: 8621
Evaluation:  [ 50/100]  eta: 0:00:00    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [ 99/100]  eta: 0:00:00    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation: Total time: 0:00:01 (0.0117 s / it)
Evaluation:  [  0/500]  eta: 0:00:05    time: 0.0106  data: 0.0009  max mem: 8621
Evaluation:  [ 50/500]  eta: 0:00:05    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [100/500]  eta: 0:00:04    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [150/500]  eta: 0:00:04    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [200/500]  eta: 0:00:03    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [250/500]  eta: 0:00:02    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [300/500]  eta: 0:00:02    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [350/500]  eta: 0:00:01    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [400/500]  eta: 0:00:01    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation:  [450/500]  eta: 0:00:00    time: 0.0117  data: 0.0000  max mem: 8621
Evaluation:  [499/500]  eta: 0:00:00    time: 0.0116  data: 0.0000  max mem: 8621
Evaluation: Total time: 0:00:05 (0.0116 s / it)
Evaluation time 0:00:08
{'txt_r1': 5.0, 'txt_r5': 19.0, 'txt_r10': 21.0, 'txt_r_mean': 15.0, 'img_r1': 4.2, 'img_r5': 15.6, 'img_r10': 23.6, 'img_r_mean': 14.466666666666669, 'r_mean': 14.733333333333334}
{'txt_r1': 4.0, 'txt_r5': 19.0, 'txt_r10': 24.0, 'txt_r_mean': 15.666666666666666, 'img_r1': 2.6, 'img_r5': 16.6, 'img_r10': 26.2, 'img_r_mean': 15.133333333333335, 'r_mean': 15.4}
Training time 0:24:54

Evaluation运行2次，是因为eval运行一次，test运行一次。

以上是用ALBEF代码对Image Retrieval任务的简单运行，数据量只有很少，最后的指标只是示意。

标签：检索,00,mem,max,8621,eta,time,ALBEF,图文
来源： https://www.cnblogs.com/xuehuiping/p/16128518.html

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ALBEF图文检索

准备文件（1）

运行脚本

部分日志