From 4d4a319d5559c03bde3545395cc10ad59ac81f8f Mon Sep 17 00:00:00 2001 From: hymhym4321 Date: Sat, 13 Jan 2024 03:18:45 +0800 Subject: [PATCH 1/2] update:delete environment info --- .../built-in/cv/FLAVR_for_PyTorch/README.md | 16 ---------------- .../built-in/cv/Flownet2_for_Pytorch/README.md | 16 ---------------- .../cv/GoogleNet_for_Pytorch/README.md | 14 ++++---------- .../public_address_statement.md | 4 ++++ .../cv/HRNet_mmlab_for_pytorch/README.md | 15 --------------- .../public_address_statement.md | 3 ++- .../built-in/cv/I3D_for_Pytorch/README.md | 13 ------------- ACL_PyTorch/built-in/cv/I3D_nonlocal/README.md | 16 +--------------- .../cv/InceptionV3_for_Pytorch/README.md | 12 +----------- .../cv/InceptionV4_for_Pytorch/README.md | 13 +------------ .../public_address_statement.md | 3 ++- .../built-in/cv/LPRNet_for_PyTorch/README.md | 13 +------------ .../cv/MobileNetV2_for_Pytorch/README.md | 13 ------------- .../public_address_statement.md | 2 ++ .../cv/MobileNetV3_for_Pytorch/README.md | 17 +++++------------ .../public_address_statement.md | 3 +++ .../built-in/cv/PSENet_for_Pytorch/README.md | 15 +-------------- .../public_address_statement.md | 3 +++ .../built-in/cv/Pelee_for_Pytorch/ReadMe.md | 9 --------- .../cv/Res2Net_v1b_101_for_PyTorch/README.md | 14 +------------- .../cv/ResNeXt50_for_Pytorch/ReadMe.md | 2 ++ .../public_address_statement.md | 3 +++ .../cv/Resnet101_Pytorch_Infer/README.md | 8 -------- .../built-in/cv/Resnet18_for_PyTorch/README.md | 9 +-------- .../cv/Resnet50_Pytorch_Infer/README.md | 15 +-------------- .../cv/Resnet50_Pytorch_Infer_poc/README.md | 14 +------------- .../built-in/cv/Resnet50_mlperf/README.md | 13 +------------ .../built-in/cv/Retinanet_Resnet18/README.md | 13 +------------ .../cv/Retinanet_for_Pytorch/README.md | 13 +------------ ACL_PyTorch/built-in/cv/SAM/README.md | 15 +-------------- ACL_PyTorch/built-in/cv/SCNet/README.md | 14 +------------- .../built-in/cv/SE-SSD_for_PyTorch/readme.md | 12 +----------- .../cv/SE_ResNet50_Pytorch_Infer/README.md | 15 +-------------- .../built-in/cv/SFA3D_for_Pytorch/README.md | 15 +-------------- .../built-in/cv/SSD_resnet34_for_POC/README.md | 18 ++++++------------ .../public_address_statement.md | 3 +++ .../built-in/cv/STGCN_for_Pytorch/README.md | 16 +--------------- .../cv/Shufflenetv2_for_Pytorch/ReadMe.md | 15 +-------------- .../README.md | 14 +------------- .../cv/resnet50_mmlab_for_pytorch/README.md | 13 ------------- .../README.md | 14 +------------- 41 files changed, 60 insertions(+), 408 deletions(-) create mode 100644 ACL_PyTorch/built-in/cv/GoogleNet_for_Pytorch/public_address_statement.md create mode 100644 ACL_PyTorch/built-in/cv/MobileNetV3_for_Pytorch/public_address_statement.md create mode 100644 ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch/public_address_statement.md create mode 100644 ACL_PyTorch/built-in/cv/SSD_resnet34_for_POC/public_address_statement.md diff --git a/ACL_PyTorch/built-in/cv/FLAVR_for_PyTorch/README.md b/ACL_PyTorch/built-in/cv/FLAVR_for_PyTorch/README.md index f01218d883..4bb006adb9 100644 --- a/ACL_PyTorch/built-in/cv/FLAVR_for_PyTorch/README.md +++ b/ACL_PyTorch/built-in/cv/FLAVR_for_PyTorch/README.md @@ -4,8 +4,6 @@ - [概述](#ZH-CN_TOPIC_0000001172161501) - [输入输出数据](#section540883920406) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) - - [快速上手](#ZH-CN_TOPIC_0000001126281700) - [获取源码](#section4622531142816) @@ -57,20 +55,6 @@ FLAVR使用3D卷积来学习帧间运动信息，是一种无光流估计的单 -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - - | 配套 | 版本 | 环境准备指导 | - | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | - | 固件与驱动 | 1.0.17 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.1.RC1 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.12.1 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | - # 快速上手 diff --git a/ACL_PyTorch/built-in/cv/Flownet2_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/Flownet2_for_Pytorch/README.md index f2ea0204d7..3a6d3f88dd 100644 --- a/ACL_PyTorch/built-in/cv/Flownet2_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/Flownet2_for_Pytorch/README.md @@ -7,8 +7,6 @@ -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) - - [快速上手](#ZH-CN_TOPIC_0000001126281700) - [获取源码](#section4622531142816) @@ -54,20 +52,6 @@ FlowNet提出了第一个基于CNN的光流预测算法，虽然具有快速的 -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - - | 配套 | 版本 | 环境准备指导 | - | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | - | 固件与驱动 | 22.0.2 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.9.0 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | - diff --git a/ACL_PyTorch/built-in/cv/GoogleNet_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/GoogleNet_for_Pytorch/README.md index dff9c08aff..6c463a9166 100644 --- a/ACL_PyTorch/built-in/cv/GoogleNet_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/GoogleNet_for_Pytorch/README.md @@ -32,16 +32,6 @@ commit_id=7d955df73fe0e9b47f7d6c77c699324b256fc41f | output1 | batchsize x 1000 | FLOAT32 | ND | -### 推理环境准备 - -- 该模型需要以下插件与驱动 - - | 配套 | 版本 | 环境准备指导 | - | ---- | ---- | ---------- | - | 固件与驱动 | 22.0.1| [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | | - | PyTorch | 1.13.1 | | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 ||| # 快速上手 @@ -207,3 +197,7 @@ python3 vision_metric_ImageNet.py result/ ImageNet/val_label.txt ./ result.json | 64 | | 4886.97 | 877.98 | | | **最优性能** | **6308.38** | **1258.53** | + +# 公网地址说明 +代码涉及公网地址参考 public_address_statement.md + diff --git a/ACL_PyTorch/built-in/cv/GoogleNet_for_Pytorch/public_address_statement.md b/ACL_PyTorch/built-in/cv/GoogleNet_for_Pytorch/public_address_statement.md new file mode 100644 index 0000000000..2f276a2b64 --- /dev/null +++ b/ACL_PyTorch/built-in/cv/GoogleNet_for_Pytorch/public_address_statement.md @@ -0,0 +1,4 @@ + +| 类型 | 开源代码地址 | 文件名 | 公网IP地址/公网URL地址/域名/邮箱地址 | 用途说明 | +| ---- | ------------ | ------ | ------------------------------------ | -------- | +|开发引入|/|googlenet_pth2onnx.py|https://github.com/pytorch/vision/blob/master/torchvision/models/googlenet.py|注释说明| \ No newline at end of file diff --git a/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/README.md b/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/README.md index 075ecd1f29..8938f23a60 100644 --- a/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/README.md @@ -7,8 +7,6 @@ -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) - - [快速上手](#ZH-CN_TOPIC_0000001126281700) - [获取源码](#section4622531142816) @@ -51,19 +49,6 @@ -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - - | 配套 | 版本 | 环境准备指导 | - |---------| ------- | ------------------------------------------------------------ | - | 固件与驱动 | 22.0.3 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.10.1 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | diff --git a/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/public_address_statement.md b/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/public_address_statement.md index c522bb9e83..29efc1c7b7 100644 --- a/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/public_address_statement.md +++ b/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/public_address_statement.md @@ -1,4 +1,5 @@ | 类型 | 开源代码地址 | 文件名 | 公网IP地址/公网URL地址/域名/邮箱地址 | 用途说明 | | ---- | ------------ | ------ | ------------------------------------ | -------- | -|开发引入|/|HRNet_mmlab_for_pytorch/url.ini|https://github.com/open-mmlab/mmdeploy|获取源码| \ No newline at end of file +|开发引入|/|HRNet_mmlab_for_pytorch/url.ini|https://github.com/open-mmlab/mmdeploy|获取源码| +|开发引入|/|associative_embedding.py|https://github.com/open-mmlab/mmpose/pull/382|注释说明| \ No newline at end of file diff --git a/ACL_PyTorch/built-in/cv/I3D_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/I3D_for_Pytorch/README.md index d497fd873f..3c47860db6 100644 --- a/ACL_PyTorch/built-in/cv/I3D_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/I3D_for_Pytorch/README.md @@ -26,19 +26,6 @@ url=https://github.com/open-mmlab/mmaction2 | output | FLOAT32 | batchsize x 30 x 400 | ND | -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - - | 配套 | 版本 | 环境准备指导 | - | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | - | 固件与驱动 | 22.0.2 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | [CANN推理架构准备](https://www/hiascend.com/software/cann/commercial) | - | Python | 3.7.5 | 创建anaconda环境时指定python版本即可，conda create -n ${your_env_name} python==3.7.5 | - | PyTorch | 1.8.0 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | # 快速上手 diff --git a/ACL_PyTorch/built-in/cv/I3D_nonlocal/README.md b/ACL_PyTorch/built-in/cv/I3D_nonlocal/README.md index 7177f2bd08..83d4018fce 100644 --- a/ACL_PyTorch/built-in/cv/I3D_nonlocal/README.md +++ b/ACL_PyTorch/built-in/cv/I3D_nonlocal/README.md @@ -5,7 +5,7 @@ -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -41,20 +41,6 @@ url=https://github.com/open-mmlab/mmaction2 | output | FLOAT32 | batchsize x 10 x 400 | ND | -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - - | 配套 | 版本 | 环境准备指导 | - | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | - | 固件与驱动 | 22.0.2 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | [CANN推理架构准备](https://www/hiascend.com/software/cann/commercial) | - | Python | 3.7.5 | 创建anaconda环境时指定python版本即可，conda create -n ${your_env_name} python==3.7.5 | - | PyTorch | 1.8.0 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | - # 快速上手 ## 获取源码 diff --git a/ACL_PyTorch/built-in/cv/InceptionV3_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/InceptionV3_for_Pytorch/README.md index 0c5024bac1..078b3a4c2e 100644 --- a/ACL_PyTorch/built-in/cv/InceptionV3_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/InceptionV3_for_Pytorch/README.md @@ -2,7 +2,7 @@ - [概述](#概述) - [输入输出数据](#输入输出数据) -- [推理环境](#推理环境) + - [快速上手](#快速上手) - [获取源码](#获取源码) - [准备数据集](#准备数据集) @@ -35,17 +35,7 @@ InceptionV3 模型是谷歌 Inception 系列里面的第三代模型，在 Incep ---- -# 推理环境 - -- 该模型推理所需配套的软件如下： - | 配套 | 版本 | 环境准备指导 | - | --------- | ------- | ---------- | - | 固件与驱动 | 1.0.17 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | - | - | Python | 3.7.5 | - | - - 说明：请根据推理卡型号与 CANN 版本选择相匹配的固件与驱动版本。 ---- diff --git a/ACL_PyTorch/built-in/cv/InceptionV4_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/InceptionV4_for_Pytorch/README.md index 0fc37f4755..3c80f5abae 100644 --- a/ACL_PyTorch/built-in/cv/InceptionV4_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/InceptionV4_for_Pytorch/README.md @@ -2,7 +2,7 @@ - [概述](#概述) - [输入输出数据](#输入输出数据) -- [推理环境](#推理环境) + - [快速上手](#快速上手) - [获取源码](#获取源码) - [准备数据集](#准备数据集) @@ -38,17 +38,6 @@ InceptionV4中基本的Inception module还是沿袭了Inception v2/v3的结构 ---- -# 推理环境 - -- 该模型推理所需配套的软件如下： - - | 配套 | 版本 | 环境准备指导 | - | --------- | ------- | ---------- | - | 固件与驱动 | 1.0.17 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | - | - | Python | 3.7.5 | - | - - 说明：请根据推理卡型号与 CANN 版本选择相匹配的固件与驱动版本。 ---- diff --git a/ACL_PyTorch/built-in/cv/InceptionV4_for_Pytorch/public_address_statement.md b/ACL_PyTorch/built-in/cv/InceptionV4_for_Pytorch/public_address_statement.md index 76e63736a6..0393b65d59 100644 --- a/ACL_PyTorch/built-in/cv/InceptionV4_for_Pytorch/public_address_statement.md +++ b/ACL_PyTorch/built-in/cv/InceptionV4_for_Pytorch/public_address_statement.md @@ -1,3 +1,4 @@ | 类型 | 开源代码地址 | 文件名 | 公网IP地址/公网URL地址/域名/邮箱地址 | 用途说明 | | ---- | ------------ | ------ | ------------------------------------ | -------- | -|开发引入|/|InceptionV4_for_Pytorch/url.ini|http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth|下载权重| \ No newline at end of file +|开发引入|/|InceptionV4_for_Pytorch/url.ini|http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth|下载权重| +|开发引入|/|inceptionv4_pth2onnx.py|https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/inceptionv4.py|注释说明| \ No newline at end of file diff --git a/ACL_PyTorch/built-in/cv/LPRNet_for_PyTorch/README.md b/ACL_PyTorch/built-in/cv/LPRNet_for_PyTorch/README.md index 84e548a4b0..1a1d4b2c0d 100644 --- a/ACL_PyTorch/built-in/cv/LPRNet_for_PyTorch/README.md +++ b/ACL_PyTorch/built-in/cv/LPRNet_for_PyTorch/README.md @@ -49,21 +49,10 @@ LPRNet(License Plate Recognition Network)是一个实时的轻量化、高质量 # 推理环境准备 -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - - | 配套 | 版本 | 环境准备指导 | - | ---------- | ------- | ---------------------------------------------------------- | - | 固件与驱动 | 22.0.2 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.0 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.8.0 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | - 该模型需要以下依赖 - **表 2** 依赖列表 + **表 1** 依赖列表 | 依赖名称 | 版本 | | --------------------- | ----------------------- | diff --git a/ACL_PyTorch/built-in/cv/MobileNetV2_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/MobileNetV2_for_Pytorch/README.md index 0c5f083e65..6af144d65d 100644 --- a/ACL_PyTorch/built-in/cv/MobileNetV2_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/MobileNetV2_for_Pytorch/README.md @@ -3,7 +3,6 @@ - [概述](#ZH-CN_TOPIC_0000001172161501) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -47,19 +46,7 @@ mobileNetV2是对mobileNetV1的改进，是一种轻量级的神经网络。mobi -# 推理环境准备\[所有版本\] -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - -| 配套 | 版本 | 环境准备指导 | -| ------------------------------------------------------------ |---------| ------------------------------------------------------------ | -| 固件与驱动 | 22.0.3 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | -| CANN | 6.0.RC1 | - | -| Python | 3.7.5 | - | -| PyTorch | 1.8.0 | - | -| 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | # 快速上手 diff --git a/ACL_PyTorch/built-in/cv/MobileNetV2_for_Pytorch/public_address_statement.md b/ACL_PyTorch/built-in/cv/MobileNetV2_for_Pytorch/public_address_statement.md index b6c42cc398..788db266c7 100644 --- a/ACL_PyTorch/built-in/cv/MobileNetV2_for_Pytorch/public_address_statement.md +++ b/ACL_PyTorch/built-in/cv/MobileNetV2_for_Pytorch/public_address_statement.md @@ -1,3 +1,5 @@ | 类型 | 开源代码地址 | 文件名 | 公网IP地址/公网URL地址/域名/邮箱地址 | 用途说明 | | ---- | ------------ | ------ | ------------------------------------ | -------- | |开发引入|/|MobileNetV2_for_Pytorch/url.ini|https://download.pytorch.org/models/mobilenet_v2-b0353104.pth|下载权重| +|开发引入|/|mobilenet.py|https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py|注释说明| +|开发引入|/|mobilenet.py|`"MobileNetV2: Inverted Residuals and Linear Bottlenecks" `_.|注释说明| \ No newline at end of file diff --git a/ACL_PyTorch/built-in/cv/MobileNetV3_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/MobileNetV3_for_Pytorch/README.md index 7e2c4d0eb3..c01e399bdf 100644 --- a/ACL_PyTorch/built-in/cv/MobileNetV3_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/MobileNetV3_for_Pytorch/README.md @@ -1,7 +1,7 @@ # MobileNetV3-推理指导 - [概述](#概述) -- [推理环境准备](#推理环境准备) + - [快速上手](#快速上手) - [获取源码](#获取源码) - [准备数据集](#准备数据集) @@ -39,17 +39,6 @@ MobileNetV3引入了MobileNetV1的深度可分离卷积，MobileNetV2的具有 | output | FLOAT16 | batchsize x 1000 | ND | -# 推理环境准备 -- 该模型需要以下插件与驱动 - **表 1** 版本配套表 - -| 配套 | 版本 | 环境准备指导 | -| ------------------------------------------------------- |---------| ------------------------------------------------------------ | -| 固件与驱动 | 22.0.3 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | -| CANN | 6.0.RC1 | - | -| Python | 3.7.5 | - | -| PyTorch | 1.10.1 | - | -| 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | # 快速上手 @@ -184,3 +173,7 @@ MobileNetV3引入了MobileNetV1的深度可分离卷积，MobileNetV2的具有 | Ascend310P3 | 32 | ImageNet | 65.094/Top1 85.432/Top5 | 15442.12 fps | | Ascend310P3 | 64 | ImageNet | 65.079/Top1 85.417/Top5 | 14863.88 fps | + +# 公网地址说明 +代码涉及公网地址参考 public_address_statement.md + diff --git a/ACL_PyTorch/built-in/cv/MobileNetV3_for_Pytorch/public_address_statement.md b/ACL_PyTorch/built-in/cv/MobileNetV3_for_Pytorch/public_address_statement.md new file mode 100644 index 0000000000..7610f1dd5d --- /dev/null +++ b/ACL_PyTorch/built-in/cv/MobileNetV3_for_Pytorch/public_address_statement.md @@ -0,0 +1,3 @@ +| 类型 | 开源代码地址 | 文件名 | 公网IP地址/公网URL地址/域名/邮箱地址 | 用途说明 | +| ---- | ------------ | ------ | ------------------------------------ | -------- | +|开发引入|/|data/dataset.py|"""See http://www.codinghorror.com/blog/archives/001018.html"""|注释说明| \ No newline at end of file diff --git a/ACL_PyTorch/built-in/cv/PSENet_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/PSENet_for_Pytorch/README.md index 89c19f6d9c..d4fbedf1d8 100644 --- a/ACL_PyTorch/built-in/cv/PSENet_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/PSENet_for_Pytorch/README.md @@ -5,7 +5,7 @@ - [输入输出数据](#section540883920406) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -52,19 +52,6 @@ PSENet(渐进式的尺度扩张网络)是一种文本检测器，能够很好地 -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - - | 配套 | 版本 | 环境准备指导 | - | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | - | 固件与驱动 | 22.0.3 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.6.0 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | diff --git a/ACL_PyTorch/built-in/cv/PSENet_for_Pytorch/public_address_statement.md b/ACL_PyTorch/built-in/cv/PSENet_for_Pytorch/public_address_statement.md index c4c04cb504..4cc98a0910 100644 --- a/ACL_PyTorch/built-in/cv/PSENet_for_Pytorch/public_address_statement.md +++ b/ACL_PyTorch/built-in/cv/PSENet_for_Pytorch/public_address_statement.md @@ -6,3 +6,6 @@ |开发引入|/|PSENet_for_Pytorch/url.ini|https://download.pytorch.org/models/resnet50-19c8e357.pth|下载权重| |开发引入|/|PSENet_for_Pytorch/url.ini|https://download.pytorch.org/models/resnet101-5d3mb4d8f.pth|下载权重| |开发引入|/|PSENet_for_Pytorch/url.ini|https://download.pytorch.org/models/resnet152-b121ed2d.pth|下载权重| +|开发引入|/|fpn_resnet_nearest.py|http://www.apache.org/licenses/|license| +|开发引入|/|fpn_resnet_nearest.py|http://www.apache.org/licenses/LICENSE-2.0|license| +|开发引入|/|Post-processing/Algorithm_DetEva.py|It is slightly different from original algorithm(see https://perso.liris.cnrs.fr/christian.wolf/software/deteval/index.html)|注释说明| diff --git a/ACL_PyTorch/built-in/cv/Pelee_for_Pytorch/ReadMe.md b/ACL_PyTorch/built-in/cv/Pelee_for_Pytorch/ReadMe.md index 3221cca847..54b6a60ac1 100644 --- a/ACL_PyTorch/built-in/cv/Pelee_for_Pytorch/ReadMe.md +++ b/ACL_PyTorch/built-in/cv/Pelee_for_Pytorch/ReadMe.md @@ -35,15 +35,6 @@ commit_id=1eab4106330f275ab3c5dfb910ddd79a5bac95ef ## 推理环境准备 -该模型需要以下插件与驱动 - -| 配套 | 版本 | 环境准备指导 | -| ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | -| 固件与驱动 | [1.0.15](https://www.hiascend.com/hardware/firmware-drivers?tag=commercial) | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | -| CANN | [5.1.RC1](https://www.hiascend.com/software/cann/commercial?version=5.1.RC1) | | -| PyTorch | [1.5.0](https://github.com/pytorch/pytorch/tree/v1.5.0) | | -| 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | | | - | 依赖名称 | 版本 | diff --git a/ACL_PyTorch/built-in/cv/Res2Net_v1b_101_for_PyTorch/README.md b/ACL_PyTorch/built-in/cv/Res2Net_v1b_101_for_PyTorch/README.md index bdbd02bbc9..df721ff9be 100755 --- a/ACL_PyTorch/built-in/cv/Res2Net_v1b_101_for_PyTorch/README.md +++ b/ACL_PyTorch/built-in/cv/Res2Net_v1b_101_for_PyTorch/README.md @@ -7,7 +7,7 @@ -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -54,19 +54,7 @@ -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - | 配套 | 版本 | 环境准备指导 | - | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | - | 固件与驱动 | 22.0.2 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 5.1.RC1 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.5.0 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | diff --git a/ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch/ReadMe.md b/ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch/ReadMe.md index 0c3fb44c82..00cb04e6fe 100644 --- a/ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch/ReadMe.md +++ b/ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch/ReadMe.md @@ -60,3 +60,5 @@ 验证推理结果 +# 公网地址说明 +代码涉及公网地址参考 public_address_statement.md \ No newline at end of file diff --git a/ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch/public_address_statement.md b/ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch/public_address_statement.md new file mode 100644 index 0000000000..a2038173d1 --- /dev/null +++ b/ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch/public_address_statement.md @@ -0,0 +1,3 @@ +| 类型 | 开源代码地址 | 文件名 | 公网IP地址/公网URL地址/域名/邮箱地址 | 用途说明 | +| ---- | ------------ | ------ | ------------------------------------ | -------- | +|开发引入|/|resnext50_pth2onnx.py|https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py|注释说明| \ No newline at end of file diff --git a/ACL_PyTorch/built-in/cv/Resnet101_Pytorch_Infer/README.md b/ACL_PyTorch/built-in/cv/Resnet101_Pytorch_Infer/README.md index dbd2d7c9f7..7d1a05e637 100644 --- a/ACL_PyTorch/built-in/cv/Resnet101_Pytorch_Infer/README.md +++ b/ACL_PyTorch/built-in/cv/Resnet101_Pytorch_Infer/README.md @@ -43,14 +43,6 @@ commit_id=7d955df73fe0e9b47f7d6c77c699324b256fc41f ### 推理环境准备 -- 该模型需要以下插件与驱动 - - | 配套 | 版本 | 环境准备指导 | - | ----------------------------------------- | ------- | --------------------------------------------------------------------------------------------- | - | 固件与驱动 | 22.0.4 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | | - | PyTorch | 1.5.1 | | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | | | - 该模型需要以下依赖。 diff --git a/ACL_PyTorch/built-in/cv/Resnet18_for_PyTorch/README.md b/ACL_PyTorch/built-in/cv/Resnet18_for_PyTorch/README.md index ab6a788fd8..b442f62f32 100644 --- a/ACL_PyTorch/built-in/cv/Resnet18_for_PyTorch/README.md +++ b/ACL_PyTorch/built-in/cv/Resnet18_for_PyTorch/README.md @@ -27,14 +27,7 @@ ## 推理环境准备 -- 该模型需要以下插件与驱动 - - | 配套 | 版本 | 环境准备指导 | - | ----------------------------------------- | ------- | --------------------------------------------------------------------------------------------- | - | 固件与驱动 | 22.0.4 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | | - | PyTorch | 1.5.1 | | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | | | + - 该模型需要以下依赖。 diff --git a/ACL_PyTorch/built-in/cv/Resnet50_Pytorch_Infer/README.md b/ACL_PyTorch/built-in/cv/Resnet50_Pytorch_Infer/README.md index cee20cc10e..f8a0cc54b7 100644 --- a/ACL_PyTorch/built-in/cv/Resnet50_Pytorch_Infer/README.md +++ b/ACL_PyTorch/built-in/cv/Resnet50_Pytorch_Infer/README.md @@ -3,7 +3,7 @@ - [概述](#ZH-CN_TOPIC_0000001172161501) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -54,19 +54,6 @@ Resnet是残差网络(Residual Network)的缩写,该系列网络广泛用于目 -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - -| 配套 | 版本 | 环境准备指导 | -| ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | -| 固件与驱动 | 1.0.15 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | -| CANN | 5.1.RC2 | - | -| Python | 3.7.5 | - | -| PyTorch | >1.5.0 | - | -| 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | # 快速上手 diff --git a/ACL_PyTorch/built-in/cv/Resnet50_Pytorch_Infer_poc/README.md b/ACL_PyTorch/built-in/cv/Resnet50_Pytorch_Infer_poc/README.md index b9502c0add..afe59a1c9c 100644 --- a/ACL_PyTorch/built-in/cv/Resnet50_Pytorch_Infer_poc/README.md +++ b/ACL_PyTorch/built-in/cv/Resnet50_Pytorch_Infer_poc/README.md @@ -3,7 +3,7 @@ - [概述](#ZH-CN_TOPIC_0000001172161501) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -54,19 +54,7 @@ Resnet是残差网络(Residual Network)的缩写,该系列网络广泛用于目 -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 -| 配套 | 版本 | 环境准备指导 | -| ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | -| 固件与驱动 | 23.0.RC2 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | -| CANN | 6.3.203 | - | -| Python | 3.7.5 | - | -| PyTorch | >1.5.0 | - | -| 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | # 快速上手 diff --git a/ACL_PyTorch/built-in/cv/Resnet50_mlperf/README.md b/ACL_PyTorch/built-in/cv/Resnet50_mlperf/README.md index 3f958e8709..44c4971daa 100644 --- a/ACL_PyTorch/built-in/cv/Resnet50_mlperf/README.md +++ b/ACL_PyTorch/built-in/cv/Resnet50_mlperf/README.md @@ -3,7 +3,7 @@ - [概述](#ZH-CN_TOPIC_0000001172161501) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -53,18 +53,7 @@ Resnet是残差网络(Residual Network)的缩写,该系列网络广泛用于目 | output | batchsize | INT64 | -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 -| 配套 | 版本 | 环境准备指导 | -| ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | -| 固件与驱动 | 1.0.15 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | -| CANN | 6.3.RC1 | - | -| Python | 3.7.5 | - | -| 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | # 快速上手 diff --git a/ACL_PyTorch/built-in/cv/Retinanet_Resnet18/README.md b/ACL_PyTorch/built-in/cv/Retinanet_Resnet18/README.md index e7c168b453..af2daae634 100644 --- a/ACL_PyTorch/built-in/cv/Retinanet_Resnet18/README.md +++ b/ACL_PyTorch/built-in/cv/Retinanet_Resnet18/README.md @@ -4,7 +4,7 @@ - [输入输出数据](#section540883920406) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -44,18 +44,7 @@ | dets | FLOAT32 | 1 x 5 x 100 | ND | | labels | INT64 | 1 x 100 | ND | -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - | 配套 | 版本 | 环境准备指导 | - | ---------- | ------- | ----------------------------------------------------------------------------------------------------- | - | 固件与驱动 | 1.0.17 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 7.0.RC1 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.10.0 | - | # 快速上手 diff --git a/ACL_PyTorch/built-in/cv/Retinanet_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/Retinanet_for_Pytorch/README.md index 52547cd95f..de146d6104 100644 --- a/ACL_PyTorch/built-in/cv/Retinanet_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/Retinanet_for_Pytorch/README.md @@ -4,7 +4,7 @@ - [输入输出数据](#section540883920406) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -44,18 +44,7 @@ | dets | FLOAT32 | 1 x 5 x 100 | ND | | labels | INT64 | 1 x 100 | ND | -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - | 配套 | 版本 | 环境准备指导 | - | ---------- | ------- | ----------------------------------------------------------------------------------------------------- | - | 固件与驱动 | 1.0.17 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.10.0 | - | # 快速上手 diff --git a/ACL_PyTorch/built-in/cv/SAM/README.md b/ACL_PyTorch/built-in/cv/SAM/README.md index 82a1048f09..a822b9e272 100644 --- a/ACL_PyTorch/built-in/cv/SAM/README.md +++ b/ACL_PyTorch/built-in/cv/SAM/README.md @@ -3,7 +3,7 @@ - [概述](#ZH-CN_TOPIC_0000001172161501) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -72,19 +72,6 @@ SAM 首先会自动分割图像中的所有内容，但是如果你需要分割 | low_res_masks | FLOAT32 | -1 x 1 x -1 x -1 | ND | -# 推理环境准备\[所有版本\] - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - -| 配套 | 版本 | 环境准备指导 | -| ------------------------------------------------------------ |---------| ------------------------------------------------------------ | -| 固件与驱动 | 23.0.rc3.b070 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | -| CANN | 7.0.T10 | - | -| Python | 3.8.13 | - | -| PyTorch | 1.13.1 | - | -| 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | # 快速上手 diff --git a/ACL_PyTorch/built-in/cv/SCNet/README.md b/ACL_PyTorch/built-in/cv/SCNet/README.md index a78abff04c..6a6ac691fb 100644 --- a/ACL_PyTorch/built-in/cv/SCNet/README.md +++ b/ACL_PyTorch/built-in/cv/SCNet/README.md @@ -7,7 +7,7 @@ -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -51,19 +51,7 @@ -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - | 配套 | 版本 | 环境准备指导 | - |---------| ------- | ------------------------------------------------------------ | - | 固件与驱动 | 22.0.3 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.8.1 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | diff --git a/ACL_PyTorch/built-in/cv/SE-SSD_for_PyTorch/readme.md b/ACL_PyTorch/built-in/cv/SE-SSD_for_PyTorch/readme.md index 89b492bb67..724f78a1de 100644 --- a/ACL_PyTorch/built-in/cv/SE-SSD_for_PyTorch/readme.md +++ b/ACL_PyTorch/built-in/cv/SE-SSD_for_PyTorch/readme.md @@ -4,7 +4,7 @@ - [输入输出数据](#section540883920406) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -44,17 +44,7 @@ SE-SSD（Self-Ensembling Single-Stage Object Detector）是一种基于自集成 | dir_cls_preds | FLOAT32 | batchsize x 200 x 176 x 4 | ND | | iou_preds | FLOAT32 | batchsize x 200 x 176 x 2 | ND | -# 推理环境准备 - -- 该模型需要以下插件与驱动: - | 配套 | 版本 | 环境准备指导 | - | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | - | 固件与驱动 | 23.0.RC1 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.3.RC1 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.13.1 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | | | # 快速上手 diff --git a/ACL_PyTorch/built-in/cv/SE_ResNet50_Pytorch_Infer/README.md b/ACL_PyTorch/built-in/cv/SE_ResNet50_Pytorch_Infer/README.md index 522873d50a..1ff2c8657f 100644 --- a/ACL_PyTorch/built-in/cv/SE_ResNet50_Pytorch_Infer/README.md +++ b/ACL_PyTorch/built-in/cv/SE_ResNet50_Pytorch_Infer/README.md @@ -5,7 +5,7 @@ - [输入输出数据](#section540883920406) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -48,19 +48,6 @@ -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - - | 配套 | 版本 | 环境准备指导 | - | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | - | 固件与驱动 | 22.0.3 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.6.0 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | diff --git a/ACL_PyTorch/built-in/cv/SFA3D_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/SFA3D_for_Pytorch/README.md index 87252375ce..14131bce0a 100644 --- a/ACL_PyTorch/built-in/cv/SFA3D_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/SFA3D_for_Pytorch/README.md @@ -2,7 +2,7 @@ - [概述](#00) - [输入输出数据](#00_1) -- [推理环境准备](#01) + - [快速上手](#1) - [获取源码](#1_0) - [准备数据集](#1_1) @@ -45,19 +45,6 @@ SFA3D（Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clou -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - - | 配套 | 版本 | 环境准备指导 | - | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | - | 固件与驱动 | 22.0.3 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | | - | Python | 3.7.5 | - | - | PyTorch | 1.6.0 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | diff --git a/ACL_PyTorch/built-in/cv/SSD_resnet34_for_POC/README.md b/ACL_PyTorch/built-in/cv/SSD_resnet34_for_POC/README.md index 069305922c..209976e07b 100644 --- a/ACL_PyTorch/built-in/cv/SSD_resnet34_for_POC/README.md +++ b/ACL_PyTorch/built-in/cv/SSD_resnet34_for_POC/README.md @@ -7,7 +7,7 @@ -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -57,18 +57,7 @@ SSD模型是用于图像检测的模型，通过基于Resnet34残差卷积网络 | scores | FLOAT32 | 1 x 200 | ND | -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - | 配套 | 版本 | 环境准备指导 | - | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | - | 固件与驱动 | 22.0.2 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.3.RC2 | - | - | Python | 3.7.5 | - | - @@ -267,4 +256,9 @@ SSD模型是用于图像检测的模型，通过基于Resnet34残差卷积网络 | Model | batchsize | Accuracy | | ----------- | --------- | -------- | | ssd_resnet34 | 1 | map = 20% | + + +# 公网地址说明 +代码涉及公网地址参考 public_address_statement.md + \ No newline at end of file diff --git a/ACL_PyTorch/built-in/cv/SSD_resnet34_for_POC/public_address_statement.md b/ACL_PyTorch/built-in/cv/SSD_resnet34_for_POC/public_address_statement.md new file mode 100644 index 0000000000..bd450e0c8b --- /dev/null +++ b/ACL_PyTorch/built-in/cv/SSD_resnet34_for_POC/public_address_statement.md @@ -0,0 +1,3 @@ +| 类型 | 开源代码地址 | 文件名 | 公网IP地址/公网URL地址/域名/邮箱地址 | 用途说明 | +| ---- | ------------ | ------ | ------------------------------------ | -------- | +|开发引入|/|ssd_preprocess.py|# use the scales here: https://github.com/amdegroot/ssd.pytorch/blob/master/data/config.py|注释说明| \ No newline at end of file diff --git a/ACL_PyTorch/built-in/cv/STGCN_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/STGCN_for_Pytorch/README.md index 0530a9f1e7..39f98d7cca 100644 --- a/ACL_PyTorch/built-in/cv/STGCN_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/STGCN_for_Pytorch/README.md @@ -2,7 +2,7 @@ - [概述](#概述) - [输入输出数据](#输入输出数据) -- [推理环境](#推理环境) + - [快速上手](#快速上手) - [获取源码](#获取源码) - [准备数据集](#准备数据集) @@ -37,20 +37,6 @@ ST-GCN是一种图卷积神经网络，该模型可以实现对人体骨架图 ---- -# 推理环境 - -- 该模型推理所需配套的软件如下： - - | 配套 | 版本 | 环境准备指导 | - | --------- | ------- | ---------- | - | 固件与驱动 | 1.0.17 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | - | - | Nvidia-Driver | 460.67 | | - | CUDA | 10.0 | - | - | CUDNN | 7.6.5.32 | - | - | Python | 3.7.5 | - | - - 说明：请根据推理卡型号与 CANN 版本选择相匹配的固件与驱动版本。 ---- diff --git a/ACL_PyTorch/built-in/cv/Shufflenetv2_for_Pytorch/ReadMe.md b/ACL_PyTorch/built-in/cv/Shufflenetv2_for_Pytorch/ReadMe.md index dfdeba0a53..026b28cb13 100644 --- a/ACL_PyTorch/built-in/cv/Shufflenetv2_for_Pytorch/ReadMe.md +++ b/ACL_PyTorch/built-in/cv/Shufflenetv2_for_Pytorch/ReadMe.md @@ -4,7 +4,7 @@ - [概述](#ZH-CN_TOPIC_0000001172161501) - [输入输出数据](#section540883920406) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -51,19 +51,6 @@ Shufflenetv2是Shufflenet的升级版本，作为轻量级网络，通过遵循 -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - - | 配套 | 版本 | 环境准备指导 | - | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | - | 固件与驱动 | 22.0.3 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | - | CANN | 6.0.RC1 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.8.0 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | diff --git a/ACL_PyTorch/built-in/cv/SuperGlue_with_SuperPoint_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/SuperGlue_with_SuperPoint_for_Pytorch/README.md index 6f9780f464..e81755ac16 100644 --- a/ACL_PyTorch/built-in/cv/SuperGlue_with_SuperPoint_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/SuperGlue_with_SuperPoint_for_Pytorch/README.md @@ -4,7 +4,7 @@ - [输入输出数据](#section540883920406) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) - [获取源码](#section4622531142816) @@ -66,19 +66,7 @@ SuperGlue网络用于特征匹配与外点剔除，其使用图神经网络对 | matching_scores0 | FLOAT32 | points_num0 x 1 | ND | | matching_scores1 | FLOAT32 | points_num1 x 1 | ND | -# 推理环境准备 - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - | 配套 | 版本 | 环境准备指导 | - | ------------------------------------------------------------ | ------ | ------------------------------------------------------------ | - | 固件与驱动 | 1.0.17 | [Pytorch框架推理环境准备](https://gitee.com/link?target=https%3A%2F%2Fwww.hiascend.com%2Fdocument%2Fdetail%2Fzh%2FModelZoo%2Fpytorchframework%2Fpies) | - | CANN | 6.3RC1 | - | - | Python | 3.7.5 | - | - | PyTorch | 1.12.0 | - | - | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | # 快速上手 diff --git a/ACL_PyTorch/built-in/cv/resnet50_mmlab_for_pytorch/README.md b/ACL_PyTorch/built-in/cv/resnet50_mmlab_for_pytorch/README.md index 160cdfdbb8..2037be4c6b 100644 --- a/ACL_PyTorch/built-in/cv/resnet50_mmlab_for_pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/resnet50_mmlab_for_pytorch/README.md @@ -3,7 +3,6 @@ - [概述](#ZH-CN_TOPIC_0000001172161501) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -46,19 +45,7 @@ ResNet50是针对移动端专门定制的轻量级卷积神经网络，该网络 -# 推理环境准备\[所有版本\] -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 - -| 配套 | 版本 | 环境准备指导 | -| ------------------------------------------------------------ |---------| ------------------------------------------------------------ | -| 固件与驱动 | 22.0.3 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | -| CANN | 6.0.RC1 | - | -| Python | 3.7.5 | - | -| PyTorch | 1.8.0 | - | -| 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | # 快速上手 diff --git a/ACL_PyTorch/built-in/cv/resnet50_mmlab_for_pytorch_for_POC/README.md b/ACL_PyTorch/built-in/cv/resnet50_mmlab_for_pytorch_for_POC/README.md index 9fdba2bf21..85166d84df 100644 --- a/ACL_PyTorch/built-in/cv/resnet50_mmlab_for_pytorch_for_POC/README.md +++ b/ACL_PyTorch/built-in/cv/resnet50_mmlab_for_pytorch_for_POC/README.md @@ -3,7 +3,7 @@ - [概述](#ZH-CN_TOPIC_0000001172161501) -- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + - [快速上手](#ZH-CN_TOPIC_0000001126281700) @@ -46,19 +46,7 @@ ResNet50是针对移动端专门定制的轻量级卷积神经网络，该网络 -# 推理环境准备\[所有版本\] - -- 该模型需要以下插件与驱动 - - **表 1** 版本配套表 -| 配套 | 版本 | 环境准备指导 | -| ------------------------------------------------------------ |---------| ------------------------------------------------------------ | -| 固件与驱动 | 23.0.rc2 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | -| CANN | 6.3.RC1 | - | -| Python | 3.7.5 | - | -| PyTorch | 1.13.1 | - | -| 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | # 快速上手 -- Gitee From b3920436f3e99e176ddb74743e5f28bf08c08fd9 Mon Sep 17 00:00:00 2001 From: hymhym4321 Date: Thu, 18 Jan 2024 03:50:38 +0800 Subject: [PATCH 2/2] update:delete environment info --- .../cv/HRNet_mmlab_for_pytorch/README.md | 8 ++++---- .../cv/HRNet_mmlab_for_pytorch/run_infer.py | 2 +- .../built-in/cv/I3D_for_Pytorch/README.md | 6 +++--- .../built-in/cv/R(2+1)D_for_Pytorch/README.md | 18 +++++++++--------- .../cv/ResNeXt50_for_Pytorch/ReadMe.md | 2 +- .../built-in/cv/Resnet34_for_Pytorch/ReadMe.md | 12 ++++++------ ACL_PyTorch/built-in/cv/SCNet/README.md | 10 +++++----- .../built-in/cv/SFA3D_for_Pytorch/README.md | 2 +- 8 files changed, 30 insertions(+), 30 deletions(-) diff --git a/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/README.md b/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/README.md index 8938f23a60..fc119f3b4e 100644 --- a/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/README.md @@ -97,7 +97,7 @@ 2. 运行preprocess.py处理数据集 ``` cd mmpose - python3.7 tools/preprocess.py configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py --pre_data ./pre_data + python3 tools/preprocess.py configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py --pre_data ./pre_data ``` - 参数说明： @@ -127,7 +127,7 @@ ``` cd mmpose - python3.7 tools/deployment/pytorch2onnx.py configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py ./hrnet_w32_coco_512x512-bcb8c247_20200816.pth --output-file ./hrnet.onnx + python3 tools/deployment/pytorch2onnx.py configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py ./hrnet_w32_coco_512x512-bcb8c247_20200816.pth --output-file ./hrnet.onnx ``` - 参数说明： @@ -189,7 +189,7 @@ a. 使用run_infer.py进行推理, 该文件调用aclruntime的后端封装的python的whl包进行推理。 ``` - python3.7 run_infer.py --data_path ./pre_data --out_put ./output --result ./result --batch_size 1 --device_id 0 + python3 run_infer.py --data_path ./pre_data --out_put ./output --result ./result --batch_size 1 --device_id 0 ``` - 参数说明： @@ -204,7 +204,7 @@ b. 精度验证。 ``` - python3.7 tools/postprocess.py configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py --dataset ./result --eval mAP --label_dir ./pre_data/label.json + python3 tools/postprocess.py configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py --dataset ./result --eval mAP --label_dir ./pre_data/label.json ``` - 参数说明： diff --git a/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/run_infer.py b/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/run_infer.py index 22cc55d33e..12c7a720fb 100644 --- a/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/run_infer.py +++ b/ACL_PyTorch/built-in/cv/HRNet_mmlab_for_pytorch/run_infer.py @@ -47,7 +47,7 @@ def main(): shape = file.split('_') shape_list.append([shape[0], shape[1]]) - command = 'python3.7 -m ais_bench --model "model/hrnet_bs{}.om" --input "{}/{}_{}" --output "{}" --outfmt NPY ' \ + command = 'python3 -m ais_bench --model "model/hrnet_bs{}.om" --input "{}/{}_{}" --output "{}" --outfmt NPY ' \ '--dymDims x:{},3,{},{} --device {}' for i in shape_list: command1 = command.format(arg.batch_size, data_path1, i[0], i[1], arg.out_put, arg.batch_size, i[0], i[1], diff --git a/ACL_PyTorch/built-in/cv/I3D_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/I3D_for_Pytorch/README.md index 3c47860db6..e79829c8d5 100644 --- a/ACL_PyTorch/built-in/cv/I3D_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/I3D_for_Pytorch/README.md @@ -50,7 +50,7 @@ url=https://github.com/open-mmlab/mmaction2 ``` cd .. - pip3.7 install -r requirements.txt + pip3 install -r requirements.txt ``` ## 准备数据集 @@ -176,7 +176,7 @@ url=https://github.com/open-mmlab/mmaction2 运行命令获取top1_acc，top5_acc和mean_acc，如出现找不到mmaction的错误，可将mmaction2下的mmaction文件移到mmaction2/tools。 ```sh mv ../i3d_inference.py ./ - python i3d_inference.py ./configs/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb.py --eval top_k_accuracy mean_class_accuracy --out result.json --batch_size 1 --model ../i3d_bs1.om --device_id 0 --show True + python3 i3d_inference.py ./configs/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb.py --eval top_k_accuracy mean_class_accuracy --out result.json --batch_size 1 --model ../i3d_bs1.om --device_id 0 --show True ``` - 参数说明： - --eval：精度指标。 @@ -190,7 +190,7 @@ url=https://github.com/open-mmlab/mmaction2 可使用ais_bench推理工具的纯推理模型验证模型的性能，参考命令如下： ``` - python3.7 -m ais_bench --model=i3d_bs1.om --batchsize=1 + python3 -m ais_bench --model=i3d_bs1.om --batchsize=1 ``` # 模型推理性能&精度 diff --git a/ACL_PyTorch/built-in/cv/R(2+1)D_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/R(2+1)D_for_Pytorch/README.md index 368d10ba04..c28447e699 100644 --- a/ACL_PyTorch/built-in/cv/R(2+1)D_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/R(2+1)D_for_Pytorch/README.md @@ -5,7 +5,7 @@ - **1.1 安装必要的依赖，测试环境可能已经安装其中的一些不同版本的库了，故手动测试时不推荐使用该命令安装** ``` -pip3.7 install -r requirements.txt +pip3 install -r requirements.txt ``` - **1.2 获取，修改与安装开源模型代码** @@ -20,7 +20,7 @@ cd .. git clone https://github.com/open-mmlab/mmaction2 -b master cd mmaction2 git reset --hard acce52d21a2545d9351b1060853c3bcd171b7158 -python3.7 setup.py develop +python3 setup.py develop ``` 注：若上述命令不能下载源码，则将https替换为git（如：git clone git://github.com/open-mmlab/mmcv -b master ） @@ -44,7 +44,7 @@ mkdir -p ./data/ucf101/videos 将/root/datasets/ucf101文件夹下的视频文件夹复制到videos下 cp -r /root/datasets/ucf101/* ./data/ucf101/videos -python3.7 ./mmaction2/tools/data/build_rawframes.py ./data/ucf101/videos/ ./data/ucf101/rawframes/ --task rgb --level 2 --ext avi --use-opencv +python3 ./mmaction2/tools/data/build_rawframes.py ./data/ucf101/videos/ ./data/ucf101/rawframes/ --task rgb --level 2 --ext avi --use-opencv DATA_DIR_AN="./data/ucf101/annotations" @@ -66,9 +66,9 @@ PYTHONPATH=. python3.7 ./mmaction2/tools/data/build_file_list.py ucf101 data/ucf ``` - **2.1 pth转ONNX** ``` -python3.7 ./mmaction2/tools/deployment/pytorch2onnx.py ./mmaction2/configs/recognition/r2plus1d/r2plus1d_r34_8x8x1_180e_ucf101_rgb2.py best_top1_acc_epoch_35.pth --verify --output-file=r2plus1d.onnx --shape 1 3 3 8 256 256 +python3 ./mmaction2/tools/deployment/pytorch2onnx.py ./mmaction2/configs/recognition/r2plus1d/r2plus1d_r34_8x8x1_180e_ucf101_rgb2.py best_top1_acc_epoch_35.pth --verify --output-file=r2plus1d.onnx --shape 1 3 3 8 256 256 -python3.7 -m onnxsim --input-shape="1,3,3,8,256,256" --dynamic-input-shape r2plus1d.onnx r2plus1d_sim.onnx +python3 -m onnxsim --input-shape="1,3,3,8,256,256" --dynamic-input-shape r2plus1d.onnx r2plus1d_sim.onnx ``` - **2.2 ONNX转om** 1. 配置环境变量 @@ -91,18 +91,18 @@ python3.7 -m onnxsim --input-shape="1,3,3,8,256,256" --dynamic-input-shape r2plu - **2.3 数据预处理** ``` -python3.7 r2plus1d_preprocess.py --config=./mmaction2/configs/recognition/r2plus1d/r2plus1d_r34_8x8x1_180e_ucf101_rgb2.py --bts=1 --output_path=./predata_bts1/ +python3 r2plus1d_preprocess.py --config=./mmaction2/configs/recognition/r2plus1d/r2plus1d_r34_8x8x1_180e_ucf101_rgb2.py --bts=1 --output_path=./predata_bts1/ ``` - **2.4 模型性能测试** ``` -python3.7.5 -m ais_bench --model ./r2plus1d_bs4.om --loop 50 +python3 -m ais_bench --model ./r2plus1d_bs4.om --loop 50 ``` - **2.5 模型精度测试** 模型推理数据集 ``` -python3.7.5 -m ais_bench --model ./r2plus1d_bs4.om --input ./predata_bts1/ --output ./lcmout/ --outfmt NPY +python3 -m ais_bench --model ./r2plus1d_bs4.om --input ./predata_bts1/ --output ./lcmout/ --outfmt NPY --model：模型地址 --input：预处理完的数据集文件夹 --output：推理结果保存地址 @@ -110,7 +110,7 @@ python3.7.5 -m ais_bench --model ./r2plus1d_bs4.om --input ./predata_bts1/ --out ``` 精度验证 ``` -python3.7 r2plus1d_postprocess.py --result_path=./lcmout/2022_xx_xx-xx_xx_xx/sumary.json +python3 r2plus1d_postprocess.py --result_path=./lcmout/2022_xx_xx-xx_xx_xx/sumary.json --result_path：推理结果中的json文件 ``` diff --git a/ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch/ReadMe.md b/ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch/ReadMe.md index 00cb04e6fe..114b2a40a9 100644 --- a/ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch/ReadMe.md +++ b/ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch/ReadMe.md @@ -56,7 +56,7 @@ -（6）python3.7 vision_metric_ImageNet.py result/dumpOutput_device0/ ./val_label.txt ./ result.json +（6）python3 vision_metric_ImageNet.py result/dumpOutput_device0/ ./val_label.txt ./ result.json 验证推理结果 diff --git a/ACL_PyTorch/built-in/cv/Resnet34_for_Pytorch/ReadMe.md b/ACL_PyTorch/built-in/cv/Resnet34_for_Pytorch/ReadMe.md index 3dfd2a1ddb..42de679f62 100644 --- a/ACL_PyTorch/built-in/cv/Resnet34_for_Pytorch/ReadMe.md +++ b/ACL_PyTorch/built-in/cv/Resnet34_for_Pytorch/ReadMe.md @@ -18,19 +18,19 @@ 1. 数据预处理，把ImageNet 50000张图片转为二进制文件（.bin） ```shell - python3.7 pytorch_transfer.py resnet /home/HwHiAiUser/dataset/ImageNet/ILSVRC2012_img_val ./prep_bin + python3 pytorch_transfer.py resnet /home/HwHiAiUser/dataset/ImageNet/ILSVRC2012_img_val ./prep_bin ``` 2. 生成数据集info文件 ```shell - python3.7 get_info.py bin ./prep_bin ./BinaryImageNet.info 256 256 + python3 get_info.py bin ./prep_bin ./BinaryImageNet.info 256 256 ``` 3. 从torchvision下载[resnet34模型](https://ascend-repo-modelzoo.obs.cn-east-2.myhuaweicloud.com/model/1_PyTorch_PTH/ResNet34/PTH/resnet34-b627a593.pth)或者指定自己训练好的pth文件路径，通过pth2onnx.py脚本转化为onnx模型 ```shell - python3.7 pth2onnx.py ./resnet34-333f7ec4.pth ./resnet34_dynamic.onnx + python3 pth2onnx.py ./resnet34-333f7ec4.pth ./resnet34_dynamic.onnx ``` 4. 支持脚本将.onnx文件转为离线推理模型文件.om文件 @@ -51,14 +51,14 @@ 6. 精度验证，调用vision_metric_ImageNet.py脚本与数据集标签val_label.txt比对，可以获得Accuracy数据，结果保存在result.json中 ```shell - python3.7 vision_metric_ImageNet.py result/dumpOutput_device0/ ./val_label.txt ./ result.json + python3 vision_metric_ImageNet.py result/dumpOutput_device0/ ./val_label.txt ./ result.json ``` 7. 模型量化： a.生成量化数据： '''shell mkdir amct_prep_bin - python3.7 pytorch_transfer_amct.py /home/HwHiAiUser/dataset/ImageNet/ILSVRC2012_img_val ./amct_prep_bin + python3 pytorch_transfer_amct.py /home/HwHiAiUser/dataset/ImageNet/ILSVRC2012_img_val ./amct_prep_bin mkdir data_bs64 - python3.7 calibration_bin ./amct_prep_bin data_bs64 64 + python3 calibration_bin ./amct_prep_bin data_bs64 64 b. 量化模型转换： amct_onnx calibration --model resnet34_dynamic.onnx --save_path ./result/resnet34 --input_shape="actual_input_1:64,3,224,224" --data_dir "./data_bs64/" --data_type "float32" \ No newline at end of file diff --git a/ACL_PyTorch/built-in/cv/SCNet/README.md b/ACL_PyTorch/built-in/cv/SCNet/README.md index 6a6ac691fb..52f8730eb1 100644 --- a/ACL_PyTorch/built-in/cv/SCNet/README.md +++ b/ACL_PyTorch/built-in/cv/SCNet/README.md @@ -88,7 +88,7 @@ 执行imagenet_torch_preprocess.py脚本，完成预处理。 ``` - python3.7 imagenet_torch_preprocess.py /local/SCNet/imagenet/val ./pre_dataset + python3 imagenet_torch_preprocess.py /local/SCNet/imagenet/val ./pre_dataset ``` @@ -117,7 +117,7 @@ 运行pth2onnx.py脚本。 ``` - python3.7 pth2onnx.py scnet50_v1d-4109d1e1.pth scnet.onnx + python3 pth2onnx.py scnet50_v1d-4109d1e1.pth scnet.onnx ``` 获得scnet.onnx文件。 @@ -197,7 +197,7 @@ 调用vision_metric_ImageNet.py脚本与label比对，可以获得Accuracy Top5数据，结果保存在result.txt中。 ``` - python3.7 vision_metric.py --benchmark_out ./output/subdir/ --anno_file /local/SCNet/imagenet/val_label.txt --result_file ./result.txt + python3 vision_metric.py --benchmark_out ./output/subdir/ --anno_file /local/SCNet/imagenet/val_label.txt --result_file ./result.txt ``` - 参数说明： @@ -213,9 +213,9 @@ 可使用ais_bench推理工具的纯推理模式验证不同batch_size的om模型的性能，参考命令如下： ``` - python3.7 -m ais_bench --model=./scnet_bs{batch size}.om --loop=1000 --batchsize={batch size} + python3 -m ais_bench --model=./scnet_bs{batch size}.om --loop=1000 --batchsize={batch size} 示例 - python3.7 -m ais_bench --model=./scnet_bs1.om --loop=1000 --batchsize=1 + python3 -m ais_bench --model=./scnet_bs1.om --loop=1000 --batchsize=1 ``` - 参数说明： diff --git a/ACL_PyTorch/built-in/cv/SFA3D_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/SFA3D_for_Pytorch/README.md index 14131bce0a..00f29bcaf8 100644 --- a/ACL_PyTorch/built-in/cv/SFA3D_for_Pytorch/README.md +++ b/ACL_PyTorch/built-in/cv/SFA3D_for_Pytorch/README.md @@ -292,7 +292,7 @@ SFA3D（Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clou 可使用ais_bench推理工具的纯推理模式验证不同batch_size的om模型的性能，参考命令如下： ``` - python3.7 -m ais_bench --model=${om_model_path} --loop=20 --batchsize=${batch_size} + python3 -m ais_bench --model=${om_model_path} --loop=20 --batchsize=${batch_size} ``` 2. trt纯推理。 -- Gitee