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Apache-2.0

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欢迎来到MindSpore ModelZoo

为了让开发者更好地体验MindSpore框架优势,我们将陆续增加更多的典型网络和相关预训练模型。如果您对ModelZoo有任何需求,请通过GiteeMindSpore与我们联系,我们将及时处理。

  • 使用最新MindSpore API的SOTA模型

  • MindSpore优势

  • 官方维护和支持

目录

标准网络

领域 子领域 网络 Ascend GPU CPU
音频(Audio) 音频合成(Speech Synthesis) LPCNet
音频(Audio) 音频合成(Speech Synthesis) MelGAN
音频(Audio) 音频合成(Speech Synthesis) Tacotron2
计算机视觉(CV) 点云模型(Point Cloud Model) OctSqueeze
计算机视觉(CV) 光流估计(Optical Flow Estimation) PWCNet
计算机视觉(CV) 目标跟踪(Object Tracking) Deepsort
计算机视觉(CV) 目标跟踪(Object Tracking) ADNet
计算机视觉(CV) 图像分类(Image Classification) AlexNet
计算机视觉(CV) 图像分类(Image Classification) CNN
计算机视觉(CV) 图像分类(Image Classification) DenseNet100
计算机视觉(CV) 图像分类(Image Classification) DenseNet121
计算机视觉(CV) 图像分类(Image Classification) DPN
计算机视觉(CV) 图像分类(Image Classification) EfficientNet-B0
计算机视觉(CV) 图像分类(Image Classification) GoogLeNet
计算机视觉(CV) 图像分类(Image Classification) InceptionV3
计算机视觉(CV) 图像分类(Image Classification) InceptionV4
计算机视觉(CV) 图像分类(Image Classification) LeNet
计算机视觉(CV) 图像分类(Image Classification) MobileNetV1
计算机视觉(CV) 图像分类(Image Classification) MobileNetV2
计算机视觉(CV) 图像分类(Image Classification) MobileNetV3
计算机视觉(CV) 图像分类(Image Classification) NASNet
计算机视觉(CV) 图像分类(Image Classification) ResNet-18
计算机视觉(CV) 图像分类(Image Classification) ResNet-34
计算机视觉(CV) 图像分类(Image Classification) ResNet-50
计算机视觉(CV) 图像分类(Image Classification) ResNet-101
计算机视觉(CV) 图像分类(Image Classification) ResNet-152
计算机视觉(CV) 图像分类(Image Classification) ResNeXt50
计算机视觉(CV) 图像分类(Image Classification) ResNeXt101
计算机视觉(CV) 图像分类(Image Classification) SE-ResNet50
计算机视觉(CV) 图像分类(Image Classification) SE-ResNext50
计算机视觉(CV) 图像分类(Image Classification) ShuffleNetV1
计算机视觉(CV) 图像分类(Image Classification) ShuffleNetV2
计算机视觉(CV) 图像分类(Image Classification) SqueezeNet
计算机视觉(CV) 图像分类(Image Classification) Tiny-DarkNet
计算机视觉(CV) 图像分类(Image Classification) VGG16
计算机视觉(CV) 图像分类(Image Classification) Xception
计算机视觉(CV) 图像分类(Image Classification) CspDarkNet53
计算机视觉(CV) 图像分类(Image Classification) ErfNet
计算机视觉(CV) 图像分类(Image Classification) SimCLR
计算机视觉(CV) 图像分类(Image Classification) Vit
计算机视觉(CV) 目标检测(Object Detection) CenterFace
计算机视觉(CV) 目标检测(Object Detection) CTPN
计算机视觉(CV) 目标检测(Object Detection) Faster R-CNN
计算机视觉(CV) 目标检测(Object Detection) Mask R-CNN
计算机视觉(CV) 目标检测(Object Detection) Mask R-CNN (MobileNetV1)
计算机视觉(CV) 目标检测(Object Detection) SSD
计算机视觉(CV) 目标检测(Object Detection) SSD-MobileNetV1-FPN
计算机视觉(CV) 目标检测(Object Detection) SSD-Resnet50-FPN
计算机视觉(CV) 目标检测(Object Detection) SSD-VGG16
计算机视觉(CV) 目标检测(Object Detection) WarpCTC
计算机视觉(CV) 目标检测(Object Detection) YOLOv3-ResNet18
计算机视觉(CV) 目标检测(Object Detection) YOLOv3-DarkNet53
计算机视觉(CV) 目标检测(Object Detection) YOLOv4
计算机视觉(CV) 目标检测(Object Detection) YOLOv5
计算机视觉(CV) 目标检测(Object Detection) RetinaNet
计算机视觉(CV) 文本检测(Text Detection) DeepText
计算机视觉(CV) 文本检测(Text Detection) PSENet
计算机视觉(CV) 文本识别(Text Recognition) CNN+CTC
计算机视觉(CV) 语义分割(Semantic Segmentation) DeepLabV3
计算机视觉(CV) 语义分割(Semantic Segmentation) DeepLabV3+
计算机视觉(CV) 语义分割(Semantic Segmentation) U-Net2D (Medical)
计算机视觉(CV) 语义分割(Semantic Segmentation) U-Net3D (Medical)
计算机视觉(CV) 语义分割(Semantic Segmentation) U-Net++
计算机视觉(CV) 语义分割(Semantic Segmentation) Fast-SCNN
计算机视觉(CV) 语义分割(Semantic Segmentation) FCN8s
计算机视觉(CV) 姿态检测(6DoF Pose Estimation) PVNet
计算机视觉(CV) 关键点检测(Keypoint Detection) OpenPose
计算机视觉(CV) 关键点检测(Keypoint Detection) SimplePoseNet
计算机视觉(CV) 文本检测(Scene Text Detection) East
计算机视觉(CV) 文本检测(Scene Text Detection) PSENet
计算机视觉(CV) 文本识别(Scene Text Recognition) CRNN
计算机视觉(CV) 文本识别(Scene Text Recognition) CNN+CTC
计算机视觉(CV) 文本识别(Scene Text Recognition) CRNN-Seq2Seq-OCR
计算机视觉(CV) 文本识别(Scene Text Recognition) WarpCTC
计算机视觉(CV) 缺陷检测(Defect Detection) PatchCore
计算机视觉(CV) 缺陷检测(Defect Detection) ssim-ae
计算机视觉(CV) 人脸检测(Face Detection) RetinaFace-ResNet50
计算机视觉(CV) 人脸检测(Face Detection) CenterFace
计算机视觉(CV) 人脸检测(Face Detection) SphereFace
计算机视觉(CV) 人群计数(Crowd Counting) MCNN
计算机视觉(CV) 深度估计(Depth Estimation) DepthNet
计算机视觉(CV) 相机重定位(Camera Relocalization) PoseNet
计算机视觉(CV) 图像抠图(Image Matting) Semantic Human Matting
计算机视觉(CV) 视频分类(Video Classification) C3D
计算机视觉(CV) 图像超分(Image Super-Resolution) RDN
计算机视觉(CV) 图像超分(Image Super-Resolution) SRCNN
计算机视觉(CV) 图像去噪(Image Denoising) BRDNet
计算机视觉(CV) 图像去噪(Image Denoising) DnCNN
计算机视觉(CV) 图像去噪(Image Denoising) Learning-to-See-in-the-Dark
计算机视觉(CV) 图像质量评估(Image Quality Assessment) NIMA
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) BERT
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) FastText
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) GNMT v2
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) GRU
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) MASS
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) SentimentNet
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) Transformer
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) TinyBERT
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) TextCNN
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) CPM
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) ERNIE
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) GPT-3
自然语言处理(NLP) 情感分析(Emotion Classification) EmoTect
自然语言处理(NLP) 情感分析(Emotion Classification) LSTM
自然语言处理(NLP) 对话系统(Dialogue Generation) DGU
自然语言处理(NLP) 对话系统(Dialogue Generation) DuConv
推荐(Recommender) 推荐系统、点击率预估(Recommender System, CTR prediction) DeepFM
推荐(Recommender) 推荐系统、搜索、排序(Recommender System, Search, Ranking) Wide&Deep
推荐(Recommender) 推荐系统(Recommender System) NAML
推荐(Recommender) 推荐系统(Recommender System) NCF
图神经网络(GNN) 文本分类(Text Classification) GCN
图神经网络(GNN) 文本分类(Text Classification) GAT
图神经网络(GNN) 推荐系统(Recommender System) BGCF

研究网络

领域 子领域 网络 Ascend GPU CPU
计算机视觉(CV) 图像分类(Image Classification) 3D Densenet
计算机视觉(CV) 图像分类(Image Classification) Auto Augment
计算机视觉(CV) 图像分类(Image Classification) AVA
计算机视觉(CV) 图像分类(Image Classification) CCT
计算机视觉(CV) 图像分类(Image Classification) dnet-nas
计算机视觉(CV) 图像分类(Image Classification) Efficientnet-b0
计算机视觉(CV) 图像分类(Image Classification) Efficientnet-b1
计算机视觉(CV) 图像分类(Image Classification) Efficientnet-b2
计算机视觉(CV) 图像分类(Image Classification) Efficientnet-b3
计算机视觉(CV) 图像分类(Image Classification) FDA-BNN
计算机视觉(CV) 图像分类(Image Classification) fishnet99
计算机视觉(CV) 图像分类(Image Classification) GENET
计算机视觉(CV) 图像分类(Image Classification) GhostNet
计算机视觉(CV) 图像分类(Image Classification) Glore_res200
计算机视觉(CV) 图像分类(Image Classification) Glore_res50
计算机视觉(CV) 图像分类(Image Classification) HarDNet
计算机视觉(CV) 图像分类(Image Classification) HourNAS
计算机视觉(CV) 图像分类(Image Classification) HRNetW48-cls
计算机视觉(CV) 图像分类(Image Classification) ibn-net
计算机视觉(CV) 图像分类(Image Classification) Inception ResNet V2
计算机视觉(CV) 图像分类(Image Classification) Resnetv2_50_frn
计算机视觉(CV) 图像分类(Image Classification) META-Baseline
计算机视觉(CV) 图像分类(Image Classification) MNasNet
计算机视觉(CV) 图像分类(Image Classification) MobilenetV3-Large
计算机视觉(CV) 图像分类(Image Classification) MobilenetV3-Small
计算机视觉(CV) 图像分类(Image Classification) NFNet-F0
计算机视觉(CV) 图像分类(Image Classification) ntsnet
计算机视觉(CV) 图像分类(Image Classification) Pdarts
计算机视觉(CV) 图像分类(Image Classification) PNASNet-5
计算机视觉(CV) 图像分类(Image Classification) ProtoNet
计算机视觉(CV) 图像分类(Image Classification) Proxylessnas
计算机视觉(CV) 图像分类(Image Classification) RelationNet
计算机视觉(CV) 图像分类(Image Classification) renas
计算机视觉(CV) 图像分类(Image Classification) Res2net
计算机视觉(CV) 图像分类(Image Classification) ResNeSt-50
计算机视觉(CV) 图像分类(Image Classification) ResNet50-BAM
计算机视觉(CV) 图像分类(Image Classification) ResNet50-quadruplet
计算机视觉(CV) 图像分类(Image Classification) ResNet50-triplet
计算机视觉(CV) 图像分类(Image Classification) ResNetV2
计算机视觉(CV) 图像分类(Image Classification) ResNeXt152_vd_64x4d
计算机视觉(CV) 图像分类(Image Classification) SE-Net
计算机视觉(CV) 图像分类(Image Classification) SERes2Net50
计算机视觉(CV) 图像分类(Image Classification) SinglePathNas
计算机视觉(CV) 图像分类(Image Classification) SKNet-50
计算机视觉(CV) 图像分类(Image Classification) SPPNet
计算机视觉(CV) 图像分类(Image Classification) SqueezeNet
计算机视觉(CV) 图像分类(Image Classification) SqueezeNet1_1
计算机视觉(CV) 图像分类(Image Classification) Swin Transformer
计算机视觉(CV) 图像分类(Image Classification) TNT
计算机视觉(CV) 图像分类(Image Classification) VGG19
计算机视觉(CV) 图像分类(Image Classification) Vit-Base
计算机视觉(CV) 图像分类(Image Classification) Wide ResNet
计算机视觉(CV) 图像分类(Image Classification) FaceAttributes
计算机视觉(CV) 图像分类(Image Classification) FaceQualityAssessment
计算机视觉(CV) 重识别(Re-Identification) Aligned-ReID
计算机视觉(CV) 重识别(Re-Identification) DDAG
计算机视觉(CV) 重识别(Re-Identification) MVD
计算机视觉(CV) 重识别(Re-Identification) OSNet
计算机视觉(CV) 重识别(Re-Identification) PAMTRI
计算机视觉(CV) 重识别(Re-Identification) VehicleNet
计算机视觉(CV) 人脸检测(Face Detection) FaceDetection
计算机视觉(CV) 人脸检测(Face Detection) FaceBoxes
计算机视觉(CV) 人脸检测(Face Detection) RetinaFace
计算机视觉(CV) 人脸识别(Face Recognition) Arcface
计算机视觉(CV) 人脸识别(Face Recognition) DeepID
计算机视觉(CV) 人脸识别(Face Recognition) FaceRecognition
计算机视觉(CV) 人脸识别(Face Recognition) FaceRecognitionForTracking
计算机视觉(CV) 人脸识别(Face Recognition) LightCNN
计算机视觉(CV) 目标检测(Object Detection) Spnas
计算机视觉(CV) 目标检测(Object Detection) SSD-GhostNet
计算机视觉(CV) 目标检测(Object Detection) EGNet
计算机视觉(CV) 目标检测(Object Detection) FasterRCNN-FPN-DCN
计算机视觉(CV) 目标检测(Object Detection) NAS-FPN
计算机视觉(CV) 目标检测(Object Detection) RAS
计算机视觉(CV) 目标检测(Object Detection) r-cnn
计算机视觉(CV) 目标检测(Object Detection) RefineDet
计算机视觉(CV) 目标检测(Object Detection) Res2net_fasterrcnn
计算机视觉(CV) 目标检测(Object Detection) Res2net_yolov3
计算机视觉(CV) 目标检测(Object Detection) Retinanet_resnet101
计算机视觉(CV) 目标检测(Object Detection) SSD_MobilenetV2_fpnlite
计算机视觉(CV) 目标检测(Object Detection) ssd_mobilenet_v2
计算机视觉(CV) 目标检测(Object Detection) ssd_resnet50
计算机视觉(CV) 目标检测(Object Detection) ssd_inceptionv2
计算机视觉(CV) 目标检测(Object Detection) ssd_resnet34
计算机视觉(CV) 目标检测(Object Detection) U-2-Net
计算机视觉(CV) 目标检测(Object Detection) YOLOV3-tiny
计算机视觉(CV) 目标跟踪(Object Tracking) SiamFC
计算机视觉(CV) 目标跟踪(Object Tracking) SiamRPN
计算机视觉(CV) 目标跟踪(Object Tracking) FairMOT
计算机视觉(CV) 关键点检测(Key Point Detection) CenterNet
计算机视觉(CV) 关键点检测(Key Point Detection) CenterNet-hourglass
计算机视觉(CV) 关键点检测(Key Point Detection) CenterNet-resnet101
计算机视觉(CV) 关键点检测(Key Point Detection) CenterNet-resnet50
计算机视觉(CV) 点云模型(Point Cloud Model) PointNet
计算机视觉(CV) 点云模型(Point Cloud Model) PointNet++
计算机视觉(CV) 点云模型(Point Cloud Model) PointNet++
计算机视觉(CV) 深度估计(Depth Estimation) midas
计算机视觉(CV) 序列图片分类(Sequential Image Classification) TCN
计算机视觉(CV) 时空定位(Temporal Localization) TALL
计算机视觉(CV) 图像抠图(Image Matting) FCA-net
计算机视觉(CV) 视频分类(Video Classification) Attention Cluster
计算机视觉(CV) 视频分类(Video Classification) ECO-lite
计算机视觉(CV) 视频分类(Video Classification) R(2+1)D
计算机视觉(CV) 视频分类(Video Classification) Resnet-3D
计算机视觉(CV) 视频分类(Video Classification) StNet
计算机视觉(CV) 视频分类(Video Classification) TSM
计算机视觉(CV) 视频分类(Video Classification) TSN
计算机视觉(CV) Zero-Shot Learnning DEM
计算机视觉(CV) 风格迁移(Style Transfer) AECRNET
计算机视觉(CV) 风格迁移(Style Transfer) APDrawingGAN
计算机视觉(CV) 风格迁移(Style Transfer) Arbitrary-image-stylization
计算机视觉(CV) 风格迁移(Style Transfer) AttGAN
计算机视觉(CV) 风格迁移(Style Transfer) CycleGAN
计算机视觉(CV) 图像超分(Image Super-Resolution) CSD
计算机视觉(CV) 图像超分(Image Super-Resolution) DBPN
计算机视觉(CV) 图像超分(Image Super-Resolution) EDSR
计算机视觉(CV) 图像超分(Image Super-Resolution) esr-ea
计算机视觉(CV) 图像超分(Image Super-Resolution) ESRGAN
计算机视觉(CV) 图像超分(Image Super-Resolution) IRN
计算机视觉(CV) 图像超分(Image Super-Resolution) RCAN
计算机视觉(CV) 图像超分(Image Super-Resolution) sr-ea
计算机视觉(CV) 图像超分(Image Super-Resolution) SRGAN
计算机视觉(CV) 图像超分(Image Super-Resolution) wdsr
计算机视觉(CV) 图像去噪(Image Denoising) Neighbor2Neighbor
计算机视觉(CV) 图像生成(Image Generation) CGAN
计算机视觉(CV) 图像生成(Image Generation) DCGAN
计算机视觉(CV) 图像生成(Image Generation) GAN
计算机视觉(CV) 图像生成(Image Generation) IPT
计算机视觉(CV) 图像生成(Image Generation) pgan
计算机视觉(CV) 图像生成(Image Generation) Photo2Cartoon
计算机视觉(CV) 图像生成(Image Generation) Pix2Pix
计算机视觉(CV) 图像生成(Image Generation) SinGAN
计算机视觉(CV) 图像生成(Image Generation) StarGAN
计算机视觉(CV) 图像生成(Image Generation) STGAN
计算机视觉(CV) 图像生成(Image Generation) WGAN
计算机视觉(CV) 文本检测(Scene Text Detection) AdvancedEast
计算机视觉(CV) 文本检测(Scene Text Detection) TextFuseNet
计算机视觉(CV) 文本识别(Scene Text Recognition) ManiDP
计算机视觉(CV) 语义分割(Semantic Segmentation) 3d-cnn
计算机视觉(CV) 语义分割(Semantic Segmentation) adelaide_ea
计算机视觉(CV) 语义分割(Semantic Segmentation) DDRNet
计算机视觉(CV) 语义分割(Semantic Segmentation) E-Net
计算机视觉(CV) 语义分割(Semantic Segmentation) Hrnet
计算机视觉(CV) 语义分割(Semantic Segmentation) ICNet
计算机视觉(CV) 语义分割(Semantic Segmentation) PSPnet
计算机视觉(CV) 语义分割(Semantic Segmentation) RefineNet
计算机视觉(CV) 语义分割(Semantic Segmentation) Res2net_deeplabv3
计算机视觉(CV) 语义分割(Semantic Segmentation) UNet 3+
计算机视觉(CV) 语义分割(Semantic Segmentation) V-net
计算机视觉(CV) 语义分割(Semantic Segmentation) Autodeeplab
计算机视觉(CV) 姿态估计(Pose Estimation) AlphaPose
计算机视觉(CV) 姿态估计(Pose Estimation) Hourglass
计算机视觉(CV) 姿态估计(Pose Estimation) Simple Baseline
计算机视觉(CV) 图像检索(Image Retrieval) Delf
自然语言处理(NLP) 词嵌入(Word Embedding) Word2Vec Skip-Gram
自然语言处理(NLP) 对话系统(Dialogue Generation) DAM
自然语言处理(NLP) 机器翻译(Machine Translation) Seq2Seq
自然语言处理(NLP) 情感分析(Emotion Classification) Senta
自然语言处理(NLP) 情感分析(Emotion Classification) Attention LSTM
自然语言处理(NLP) 命名实体识别(Named Entity Recognition) LSTM_CRF
自然语言处理(NLP) 文本分类(Text Classification) HyperText
自然语言处理(NLP) 文本分类(Text Classification) TextRCNN
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) ALBert
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) KT-Net
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) LUKE
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) TextRCNN
自然语言处理(NLP) 自然语言理解(Natural Language Understanding) TPRR
自然语言处理(NLP) 知识图谱嵌入(Knowledge Graph Embedding) RotatE
推荐(Recommender) 推荐系统、点击率预估(Recommender System, CTR prediction) AutoDis
推荐(Recommender) 推荐系统、点击率预估(Recommender System, CTR prediction) DeepFFM
推荐(Recommender) 推荐系统、点击率预估(Recommender System, CTR prediction) DIEN
推荐(Recommender) 推荐系统、点击率预估(Recommender System, CTR prediction) DLRM
推荐(Recommender) 推荐系统、点击率预估(Recommender System, CTR prediction) EDCN
推荐(Recommender) 推荐系统、点击率预估(Recommender System, CTR prediction) MMOE
语音(Audio) 音频标注(Audio Tagging) FCN-4
语音(Audio) 关键词识别(Keyword Spotting) DS-CNN
语音(Audio) 语音识别(Speech Recognition) CTCModel
语音(Audio) 语音合成(Speech Synthesis) Wavenet
图神经网络(GNN) 交通预测(Traffic Prediction) STGCN
图神经网络(GNN) 交通预测(Traffic Prediction) TGCN
图神经网络(GNN) 社交信息网络(Social and Information Networks) SGCN
图神经网络(GNN) 图结构数据分类(Graph Classification) DGCN
图神经网络(GNN) 图结构数据分类(Graph Classification) SDNE
高性能计算(HPC) 分子动力学(Molecular Dynamics) DeepPotentialH2O
高性能计算(HPC) 海洋模型(Ocean Model) GOMO

公告

2021.9.15 models独立建仓

models仓库由原mindspore仓库的model_zoo目录独立分离而来,新仓库不继承历史commit记录,如果需要查找历史提2021.9.15之前的提交,请到mindspore仓库进行查询。

关联站点

这里是MindSpore框架提供的可以运行于包括Ascend/GPU/CPU/移动设备等多种设备的模型库。

相应的专属于Ascend平台的多框架模型可以参考昇腾ModelZoo以及对应的代码仓

MindSpore相关的预训练模型可以在MindSpore hub下载中心.

免责声明

MindSpore仅提供下载和预处理公共数据集的脚本。我们不拥有这些数据集,也不对它们的质量负责或维护。请确保您具有在数据集许可下使用该数据集的权限。在这些数据集上训练的模型仅用于非商业研究和教学目的。

致数据集拥有者:如果您不希望将数据集包含在MindSpore中,或者希望以任何方式对其进行更新,我们将根据要求删除或更新所有公共内容。请通过GitHub或Gitee与我们联系。非常感谢您对这个社区的理解和贡献。

MindSpore已获得Apache 2.0许可,请参见LICENSE文件。

许可证

Apache 2.0许可证

FAQ

想要获取更多关于MindSpore框架使用本身的FAQ问题的,可以参考官网FAQ

  • Q: 直接使用models下的模型出现内存不足错误,例如Failed to alloc memory pool memory, 该怎么处理?

    A: 直接使用models下的模型出现内存不足的典型原因是由于运行模式(PYNATIVE_MODE)、运行环境配置、License控制(AI-TOKEN)的不同造成的:

    • PYNATIVE_MODE通常比GRAPH_MODE使用更多内存,尤其是在需要进行反向传播计算的训练图中,当前有2种方法可以尝试解决该问题。 方法1:你可以尝试使用一些更小的batch size; 方法2:添加context.set_context(mempool_block_size="XXGB"),其中,“XX”当前最大有效值可设置为“31”。 如果将方法1与方法2结合使用,效果会更好。
    • 运行环境由于NPU的核数、内存等配置不同也会产生类似问题。
    • License控制(AI-TOKEN)的不同档位会造成执行过程中内存开销不同,也可以尝试使用一些更小的batch size。
  • Q: 一些网络运行中报错接口不存在,例如cannot import,该怎么处理?

    A: 优先检查一下获取网络脚本的分支,与所使用的MindSpore版本是否一致,部分新分支中的模型脚本会使用一些新版本MindSpore才支持的接口,从而在使用老版本MindSpore时会发生报错.

  • Q: 一些模型描述中提到的RANK_TABLE_FILE文件,是什么?

    A: RANK_TABLE_FILE是一个Ascend环境上用于指定分布式集群信息的文件,更多信息可以参考生成工具hccl_toos分布式并行训练教程

  • Q: 在windows环境上要怎么运行网络脚本?

    A: 多数模型都是使用bash作为启动脚本,在Windows环境上无法直接使用bash命令,你可以考虑直接运行python命令而不是bash启动脚本 ,如果你确实想需要使用bash脚本,你可以考虑使用以下几种方法来运行模型:

    1. 使用虚拟环境,可以构造一个linux的虚拟机或docker容器,然后在虚拟环境中运行脚本
    2. 使用WSL,可以开启Windows的linux子系统来在Windows系统中运行linux,然后再WSL中运行脚本。
    3. 使用Windows Bash,需要获取一个可以直接在Windows上运行bash的环境,常见的选择是cygwingit bash
    4. 跳过bash脚本,直接调用python程序。
  • Q: 网络在310推理时出现编译失败,报错信息指向gflags,例如undefined reference to 'google::FlagRegisterer::FlagRegisterer',该怎么处理?

    A: 优先检查一下环境GCC版本和gflags版本是否匹配,可以参考官方链接安装对应的GCC版本,gflags安装gflags。你需要保证所使用的组件之间是ABI兼容的,更多信息可以参考_GLIBCXX_USE_CXX11_ABI

  • Q: 在Mac系统上加载mindrecord格式的数据集出错,例如Invalid file, failed to open files for reading mindrecord files.,该怎么处理?

    A: 优先使用ulimit -a检查系统限制,如果file descriptors数量为256(默认值),需要使用ulimit -n 1024将其设置为1024(或者更大的值)。之后再检查文件是否损坏或者被修改。

  • Q: 我在多台服务器构成的大集群上进行训练,但是得到的精度比预期要低,该怎么办?

    A: 当前模型库中的大部分模型只在单机内进行过验证,最大使用8卡进行训练。由于MindSpore训练时指定的batch_size是单卡的,所以当单机8卡升级到多机时,会导致全局的global_batch_size变大,这就导致需要针对当前多机场景的global_batch_size进行重新调参优化。

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