mirror of https://github.com/alibaba/EasyCV.git
349 lines
8.7 KiB
Plaintext
349 lines
8.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c6c6e585-6d14-4b6a-8621-6395518b463e",
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"metadata": {
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"tags": []
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},
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"source": [
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"# EasyCV图像分类-SwinTransformer\n",
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"\n",
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"本文将介绍如何使用EasyCV快速使用[Swin Transformer](https://arxiv.org/abs/2103.14030) 进行图像分类模型的训练、推理"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0512498b-a900-4969-9563-67029a1009e2",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true,
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"tags": []
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},
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"source": [
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"## 运行环境要求\n",
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"\n",
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"PAI-Pytorch镜像 or 原生Pytorch1.5+以上环境 GPU机器， 内存32G以上"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0cafa45c-b04b-4471-b8f0-9c88ce50cc0e",
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"metadata": {
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"tags": []
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},
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"source": [
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"## 安装依赖包\n",
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"\n",
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"注: 在PAI-DSW docker中无需安装相关依赖，可跳过此部分 在本地notebook环境中执行\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c8280983-39b8-414d-a712-c40fd68dadfe",
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"metadata": {},
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"source": [
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"1、 首先，安装pytorch和对应版本的torchvision，支持Pytorch1.5.1以上版本"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9487def6-9441-435c-9da6-633cd09c517f",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# install pytorch and torch vision\n",
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"! conda install --yes pytorch==1.10.0 torchvision==0.11.0 -c pytorch"
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]
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},
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{
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"cell_type": "markdown",
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"id": "57b6b6a2-0106-43df-90a5-7b315e91cd64",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true,
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"tags": []
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},
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"source": [
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"2、 获取torch和cuda版本，安装对应版本的mmcv和nvidia-dali"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f93a5657-fdf1-467b-847d-e48fedb362ef",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true,
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"tags": []
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"import os\n",
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"os.environ['CUDA']='cu' + torch.version.cuda.replace('.', '')\n",
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"os.environ['Torch']='torch'+torch.version.__version__.replace('+PAI', '')\n",
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"!echo \"cuda version: $CUDA\"\n",
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"!echo \"pytorch version: $Torch\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9c0d7f7d-bbdc-45ac-b738-e31e00d5eef4",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# install some python deps\n",
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"! pip install mmcv-full==1.4.4 -f https://download.openmmlab.com/mmcv/dist/${CUDA}/${Torch}/index.html\n",
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"! pip install http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/third_party/nvidia_dali_cuda100-0.25.0-1535750-py3-none-manylinux2014_x86_64.whl"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2a987c04-e123-42b7-9d42-240e835aa410",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true,
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"tags": []
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},
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"source": [
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"3、 安装EasyCV算法包"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ee7590e1-c312-444e-af84-472459174d15",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true,
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"tags": []
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},
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"outputs": [],
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"source": [
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"pip install pai-easycv"
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]
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},
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{
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"cell_type": "markdown",
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"id": "df0fc82c-1f2c-4869-92e6-76d7dfb34164",
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"metadata": {
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"tags": []
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},
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"source": [
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"4、 简单验证"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8b89751f-f0c0-4aad-aced-8f6c77a4a2aa",
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"metadata": {},
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"outputs": [],
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"source": [
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"from easycv.apis import *"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8df2d210-4bcf-4b41-a981-b264c41e91a7",
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"metadata": {
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"tags": []
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},
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"source": [
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"## Cifar10 分类"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fce3f492-cfe0-4121-8f7d-877b31e3438b",
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"metadata": {},
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"source": [
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"下面示例介绍如何利用[cifar10](https://www.cs.toronto.edu/~kriz/cifar.html)数据，使用ResNet50模型快速进行图像分类模型的训练评估、模型预测过程"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6e769ad6-3ae0-4d5b-bf23-55673a239aa8",
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"metadata": {},
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"source": [
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"### 数据准备\n",
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"下载cifar10数据，解压到`data/cifar`目录， 目录结构如下\n",
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"\n",
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"```text\n",
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"data/cifar\n",
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"└── cifar-10-batches-py\n",
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" ├── batches.meta\n",
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" ├── data_batch_1\n",
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" ├── data_batch_2\n",
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" ├── data_batch_3\n",
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" ├── data_batch_4\n",
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" ├── data_batch_5\n",
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" ├── readme.html\n",
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" ├── read.py\n",
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" └── test_batch\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "62eeff3d-274d-4ff9-8123-8e11d97c0d38",
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"metadata": {},
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"outputs": [],
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"source": [
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"! mkdir -p data/cifar && wget http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/cifar10/cifar-10-python.tar.gz && tar -zxf cifar-10-python.tar.gz -C data/cifar/"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ed28f2be-1519-4d81-9d03-42891d1e9bc7",
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"metadata": {},
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"source": [
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"### 训练模型\n",
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"下载训练配置文件"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bd8b6d19-652c-4a58-89d3-38394dbb9a20",
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"metadata": {},
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"outputs": [],
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"source": [
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"! rm -rf r50.py\n",
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"!wget https://raw.githubusercontent.com/alibaba/EasyCV/master/configs/classification/cifar10/swintiny_b64_5e_jpg.py"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3d2ed375-f5ef-475e-9dc8-3386d328b8b0",
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"metadata": {},
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"source": [
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"使用单卡gpu进行训练和验证集评估"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9b6ec6da-d125-4ebe-b301-0d8cc8881512",
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"metadata": {
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"scrolled": true,
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"tags": []
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},
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"outputs": [],
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"source": [
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"! python -m easycv.tools.train swintiny_b64_5e_jpg.py --work_dir work_dirs/classification/cifar10/swin_tiny"
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]
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},
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{
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"cell_type": "markdown",
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"id": "82b04e66-ef62-4d2e-963f-34015a588786",
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"metadata": {},
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"source": [
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"### 导出模型\n",
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"\n",
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"模型训练完成，使用export命令导出模型进行推理，导出的模型包含推理时所需的预处理信息、后处理信息"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1d4f7868-88aa-428c-9852-22e3eca45a07",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 查看训练产生的pt文件\n",
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"! ls work_dirs/classification/cifar10/swin_tiny*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a5dc516f-fe35-4b1f-acbe-9ec96b72cc95",
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"metadata": {},
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"source": [
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"ClsEvaluator_neck_top1_best.pth 是训练过程中产生的acc最高的pth,导出该模型"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "67aa8262-834a-4afc-a6cd-fd882a181371",
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"metadata": {},
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"outputs": [],
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"source": [
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"! python -m easycv.tools.export swintiny_b64_5e_jpg.py work_dirs/classification/cifar10/swin_tiny/ClsEvaluator_neck_top1_best.pth work_dirs/classification/cifar10/swin_tiny/best_export.pth"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72d78ef4-0d90-4f3a-a1d9-c532e17d065b",
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"metadata": {},
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"source": [
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"### 预测\n",
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"下载测试图片"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "732fa5b4-fe0f-4a9b-ba19-d73d3ebd1736",
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"metadata": {},
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"outputs": [],
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"source": [
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"! wget http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/cifar10/qince_data/predict/aeroplane_s_000004.png"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cb3b24c2-7a19-4618-8bdf-42ebe1cdfeb3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"from easycv.predictors.classifier import TorchClassifier\n",
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"\n",
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"output_ckpt = 'work_dirs/classification/cifar10/swin_tiny/best_export.pth'\n",
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"tcls = TorchClassifier(output_ckpt, topk=1)\n",
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"\n",
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"img = cv2.imread('aeroplane_s_000004.png')\n",
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"# input image should be RGB order\n",
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"img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
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"output = tcls.predict([img])\n",
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"print(output)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5e617239-b034-4760-8c55-326cf3a6e475",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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