SK-resNet50

ImageNet training with PyTorch

This implements training of SK-resNet50 on the ImageNet dataset, mainly modified from Github.

SK-resNet50 Detail

Base version of the model from pytorch.torchvision. The training script is adapted from training script on imagenet.

Requirements

• pytorch_ascend, apex_ascend, tochvision Note: pillow recommends installing a newer version. If the corresponding torchvision version cannot be installed directly, you can use the source code to install the corresponding version. The source code reference link: Suggestion the pillow is 9.1.0 and the torchvision is 0.6.0
• Download the ImageNet dataset from http://www.image-net.org/
  • Then, and move validation images to labeled subfolders, using the following shell script.

Training

一、训练流程

单卡训练流程：

1.安装环境
2.修改参数:
   device_list（训练时可见的 device id），建议只配置训练使用的 device_id，比如 --device_list '0'   
3.开始训练
    bash ./test/train_full_1p.sh  --data_path=数据集路径         # 精度训练
    bash ./test/train_performance_1p.sh  --data_path=数据集路径  # 性能训练

​
多卡训练流程

1.安装环境
2.开始训练
    bash ./test/train_full_8p.sh  --data_path=数据集路径         # 精度训练
    bash ./test/train_performance_8p.sh  --data_path=数据集路径  # 性能训练

二、Docker容器训练

1.导入镜像二进制包docker import ubuntuarmpytorch.tar REPOSITORY:TAG, 比如:

docker import ubuntuarmpytorch.tar pytorch:b020

2.执行docker_start.sh后带三个参数：步骤1生成的REPOSITORY:TAG；数据集路径；模型执行路径；比如：

./docker_start.sh pytorch:b020 /train/peta /home/DeepMar

3.执行步骤一训练流程（环境安装除外）

三、测试结果 训练日志路径：网络脚本test下output文件夹内。例如： test/output/devie_id/train_${device_id}.log # 训练脚本原生日志 test/output/devie_id/SkresNet50_bs1024_8p_perf.log # 8p性能训练结果日志 test/output/devie_id/SkresNet50_bs1024_8p_acc.log # 8p精度训练结果日志

训练模型：训练生成的模型默认会写入到和test文件同一目录下。当训练正常结束时，checkpoint.pth.tar为最终结果。

SK-resNet50 training result

Acc@1	FPS	Npu_nums	Epochs	Type
-	820	1	1	O2
76.838	6400	8	100	O2

公网地址说明

代码涉及公网地址参考 public_address_statement.md