Evaluation metrics. To measure the performance on image retrieval, we consider the standard metrics Recall@1 (R1), Recall@5 (R5), and Recall@10 (R10). PretrainingTrain or nocaps val datafinetune on in-domain near-domain out-of-domain overall coco cap? CIDEr SPICE CIDEr SPICE CIDEr SPICE...
一个heavy_query加载1万个series,查询时间24小时,30秒一个点来算,所需要的内存大小为 439MB,所以同时多个heavy_query会将prometheus内存打爆,prometheus也加了上面一堆参数去限制 当然除了上面说的queryPreparation过程外,查询时还涉及sort和eval等也需要耗时 prometheus原生不支持downsample 还有个原因是prometheus原生不支...
替换后的metrics_name 会变成hke:heavy_expr:xxxx字样,而对应的tag不变。对于大分部panel中已经设置了曲线的Legend,所以展示没有区别 现在每晚23:30增量更新heavy_query策略。对于大部分设定好的dashboard没有影响(因为已经存量heavy_query已经跑7天以上了),对于新增策略会从策略生效后开始展示数据,对于查询高峰的白天来...
pretrained_model.eval() test_correct = 0 test_total = 0 with torch.no_grad(): for inputs, labels in test_loader: inputs = inputs.to(device) labels = labels.to(device) outputs = pretrained_model(inputs) _, predicted = outputs.max(1) test_total += labels.size(0) test_correct +=...
level=infomsg="option bpf-sock-rev-map-max set by dynamic sizing to 145311"subsys=config level=infomsg="--agent-health-port='9876'"subsys=daemon level=infomsg="--agent-labels=''"subsys=daemon level=infomsg="--allow-icmp-frag-needed='true'"subsys=daemon ...
Changes to FPS would need significant code changes. The expert discriminator's eval loss should go down to ~0.25 and the Wav2Lip eval sync loss should go down to ~0.2 to get good results.When raising an issue on this topic, please let us know that you are aware of all these points....
(t) g.metrics.evalDuration.Observe(since.Seconds()) rule.SetEvaluationDuration(since) rule.SetEvaluationTimestamp(t) }(time.Now()) g.metrics.evalTotal.WithLabelValues(groupKey(g.File(), g.Name())).Inc() vector, err := rule.Eval(ctx, ts, g.opts.QueryFunc, g.opts.ExternalURL) if ...
max_eval_steps:验证的最大步数 use_tpu:是否使用tpu 其他TPU相关的参数 准备函数 run_pretraining.py中会用到几个modeling.py文件中的方法,其中有个方法在model的源码阅读中并没有介绍,这边先介绍一下,get_assignment_map_from_checkpoint方法,主要就是根据已有变量与checkpoint中保存的变量获取公共的可初始化的变...
pretrained_model.eval() test_correct = 0 test_total = 0 with torch.no_grad(): for inputs, labels in test_loader: inputs = inputs.to(device) labels = labels.to(device) outputs = pretrained_model(inputs) _, predicted = outputs.max(1) ...
metrics_types.py mm_utils.py models gpt2.py olmo.py qwen2_vl.py server.py test long_prompt.txt simple_eval_common.py simple_eval_humaneval.py simple_eval_mgsm.py rust Cargo.toml readme.md scripts ci_install_rust.sh playground/lora lora_hf_play.py lora_vll...