预测模型:时空预测模型PyTorch复现

上传者: QYgujingjing | 上传时间: 2024-07-06 18:25:29 | 文件大小: 56KB | 文件类型: ZIP
预测模型:时空预测模型PyTorch复现 models 文件夹 在 models 目录中,每一个文件夹存储一个结构的完整模型代码,复现参照了论文中的公式、图示以及 GitHub 作者实现的代码(如果有的话) 这些模型均假定输入的 Tensor 的 shape 为 (batch, sequence, channel, height, width) 这里的目的是为了学习,尽可能内聚成一个个小的 Module 再组合的,应该效率很差 util 文件夹 patch 针对大尺寸数据进行 patch 分割的方法,不过这里要根据实际情况修改下,这里是针对五维数据的,如果针对四维,则参照逻辑修改下即可 TrainingTemplate 和 TestingTemplate 我自己写的训练过程的模板类,一般继承重写一些方法即可 content_tree 包含生成目录树的方法

文件下载

资源详情

[{"title":"( 64 个子文件 56KB ) 预测模型:时空预测模型PyTorch复现","children":[{"title":"STudy-main","children":[{"title":"LICENSE <span style='color:#111;'> 1.04KB </span>","children":null,"spread":false},{"title":"study","children":[{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":".idea","children":[{"title":"vcs.xml <span style='color:#111;'> 183B </span>","children":null,"spread":false},{"title":"misc.xml <span style='color:#111;'> 204B </span>","children":null,"spread":false},{"title":"inspectionProfiles","children":[{"title":"Project_Default.xml <span style='color:#111;'> 3.08KB </span>","children":null,"spread":false},{"title":"profiles_settings.xml <span style='color:#111;'> 174B </span>","children":null,"spread":false}],"spread":true},{"title":"study.iml <span style='color:#111;'> 336B </span>","children":null,"spread":false},{"title":"modules.xml <span style='color:#111;'> 262B </span>","children":null,"spread":false},{"title":"deployment.xml <span style='color:#111;'> 420B </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 176B </span>","children":null,"spread":false}],"spread":true},{"title":"models","children":[{"title":"__init__.py <span style='color:#111;'> 435B </span>","children":null,"spread":false},{"title":"MotionRNN","children":[{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"SpatiotemporalLSTM.py <span style='color:#111;'> 3.86KB </span>","children":null,"spread":false},{"title":"MotionRNN.py <span style='color:#111;'> 4.44KB </span>","children":null,"spread":false},{"title":"MotionGRU.py <span style='color:#111;'> 7.67KB </span>","children":null,"spread":false}],"spread":true},{"title":"ConvLSTM","children":[{"title":"__init__.py <span style='color:#111;'> 61B </span>","children":null,"spread":false},{"title":"ConvLSTM.py <span style='color:#111;'> 5.90KB </span>","children":null,"spread":false},{"title":"ConvLSTMCell.py <span style='color:#111;'> 2.86KB </span>","children":null,"spread":false}],"spread":true},{"title":"PredRNNpp","children":[{"title":"__init__.py <span style='color:#111;'> 58B </span>","children":null,"spread":false},{"title":"CausalLSTM.py <span style='color:#111;'> 3.74KB </span>","children":null,"spread":false},{"title":"PredRNNpp.py <span style='color:#111;'> 4.53KB </span>","children":null,"spread":false},{"title":"GradientHighwayUnit.py <span style='color:#111;'> 1.85KB </span>","children":null,"spread":false}],"spread":true},{"title":"CrevNet","children":[{"title":"__init__.py <span style='color:#111;'> 52B </span>","children":null,"spread":false},{"title":"PixelShuffle.py <span style='color:#111;'> 1017B </span>","children":null,"spread":false},{"title":"SpatiotemporalLSTM.py <span style='color:#111;'> 3.47KB </span>","children":null,"spread":false},{"title":"ReversiblePredictiveModule.py <span style='color:#111;'> 1.47KB </span>","children":null,"spread":false},{"title":"CrevNet.py <span style='color:#111;'> 3.32KB </span>","children":null,"spread":false},{"title":"i_RevNet_Block.py <span style='color:#111;'> 2.50KB </span>","children":null,"spread":false}],"spread":true},{"title":"TrajGRU","children":[{"title":"__init__.py <span style='color:#111;'> 52B </span>","children":null,"spread":false},{"title":"TrajGRUCell.py <span style='color:#111;'> 6.77KB </span>","children":null,"spread":false},{"title":"TrajGRU.py <span style='color:#111;'> 4.36KB </span>","children":null,"spread":false}],"spread":true},{"title":"Autoformer","children":[{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"AutoCorrelation.py <span style='color:#111;'> 700B </span>","children":null,"spread":false},{"title":"Projector.py <span style='color:#111;'> 564B </span>","children":null,"spread":false},{"title":"FeedForward.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"SeriesDecomp.py <span style='color:#111;'> 988B </span>","children":null,"spread":false}],"spread":true},{"title":"MIM","children":[{"title":"__init__.py <span style='color:#111;'> 40B </span>","children":null,"spread":false},{"title":"MIM.py <span style='color:#111;'> 3.94KB </span>","children":null,"spread":false},{"title":"SpatiotemporalLSTM.py <span style='color:#111;'> 3.47KB </span>","children":null,"spread":false},{"title":"MIMBlock.py <span style='color:#111;'> 8.33KB </span>","children":null,"spread":false}],"spread":true},{"title":"Eidetic3DLSTM","children":[{"title":"__init__.py <span style='color:#111;'> 70B </span>","children":null,"spread":false},{"title":"Eidetic3DLSTM.py <span style='color:#111;'> 3.64KB </span>","children":null,"spread":false},{"title":"Eidetic3DLSTMCell.py <span style='color:#111;'> 4.66KB </span>","children":null,"spread":false}],"spread":false},{"title":"CubicRNN","children":[{"title":"__init__.py <span style='color:#111;'> 54B </span>","children":null,"spread":false},{"title":"CubicRNN.py <span style='color:#111;'> 4.59KB </span>","children":null,"spread":false},{"title":"CubicLSTM.py <span style='color:#111;'> 3.67KB </span>","children":null,"spread":false}],"spread":false},{"title":"FoldedRNN","children":[{"title":"__init__.py <span style='color:#111;'> 58B </span>","children":null,"spread":false},{"title":"UpSample.py <span style='color:#111;'> 471B </span>","children":null,"spread":false},{"title":"FoldedRNN.py <span style='color:#111;'> 4.81KB </span>","children":null,"spread":false},{"title":"dGRU.py <span style='color:#111;'> 2.78KB </span>","children":null,"spread":false}],"spread":false},{"title":"FURENet","children":[{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"SEBlock.py <span style='color:#111;'> 657B </span>","children":null,"spread":false},{"title":"ResidualConv2d.py <span style='color:#111;'> 1.75KB </span>","children":null,"spread":false},{"title":"FURENet.py <span style='color:#111;'> 4.42KB </span>","children":null,"spread":false}],"spread":false},{"title":"PredRNN","children":[{"title":"__init__.py <span style='color:#111;'> 52B </span>","children":null,"spread":false},{"title":"SpatiotemporalLSTM.py <span style='color:#111;'> 3.85KB </span>","children":null,"spread":false},{"title":"PredRNN.py <span style='color:#111;'> 3.38KB </span>","children":null,"spread":false}],"spread":false}],"spread":false},{"title":"util","children":[{"title":"__init__.py <span style='color:#111;'> 254B </span>","children":null,"spread":false},{"title":"TrainingTemplate.py <span style='color:#111;'> 7.57KB </span>","children":null,"spread":false},{"title":"patch.py <span style='color:#111;'> 1.71KB </span>","children":null,"spread":false},{"title":"content_tree.py <span style='color:#111;'> 694B </span>","children":null,"spread":false},{"title":"TestingTemplate.py <span style='color:#111;'> 2.72KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":".gitignore <span style='color:#111;'> 1.76KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 1007B </span>","children":null,"spread":false}],"spread":true}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明