mirror of https://github.com/alibaba/EasyCV.git
400 lines
10 KiB
Plaintext
400 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c36aa9bf-8ee4-4f93-bb52-73be853b1c98",
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"metadata": {},
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"source": [
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"# EasyCV图像自监督训练-MOCOv2\n",
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"\n",
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"本文讲介绍如何利用EasyCV使用自监督算法[MOCOv2](https://arxiv.org/abs/2003.04297)进行图像自监督模型的训练"
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]
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},
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{
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"cell_type": "markdown",
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"id": "724db9f4-fa6f-4391-bbbf-cf18945875a2",
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"metadata": {
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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": "501228c1-bd16-47bc-9c02-9cb226b5c09f",
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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": "6e834aa3-682b-4975-8703-50ff00084219",
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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": "657b676e-135f-45ad-ab03-5e763871f1df",
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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": "cd2a2b02-4213-4de7-8fbb-0b127bade823",
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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": "1b3c2b71-cd90-423c-a320-994bbc65bf67",
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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: $Torch\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "389c89d3-edc5-4484-8d51-98f609078ab0",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# install some python deps\n",
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"! pip install mmcv-full==1.4.4 -f https://download.openmmlab.com/mmcv/dist/${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": "28e6dda6-946d-4cd8-ad63-ae85023d635c",
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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": "cff6021a-e1f1-479a-ab3a-9e68c0157be8",
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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": "d75a2f59-b218-4688-a6c3-56d1df0b0ecd",
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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": "c652ae9f-18fb-4da6-bff4-761ee970bd8a",
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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": "97cc7722-1a6d-4dea-a815-be76029a7faf",
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"metadata": {},
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"source": [
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"## EasyCV自监督训练"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c640853a-c698-4eba-833f-b11f562bdf99",
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"metadata": {},
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"source": [
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"### 数据准备\n",
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"\n",
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"自监督训练只需要提供无标注图片即可进行， 你可以下载[ImageNet](http://www.image-net.org/download-images) 数据，或者使用你自己的图片数据。需要提供一个包含若干图片的文件夹路径`p`，以及一个文件列表，文件列表中是每个图片相对图片目录`p`的路径\n",
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"\n",
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"图片文件夹结构示例如下, 文件夹路径为`./images`\n",
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"\n",
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"```shell\n",
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"images/\n",
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"├── 0001.jpg\n",
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"├── 0002.jpg\n",
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"├── 0003.jpg\n",
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"|...\n",
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"└── 9999.jpg\n",
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"```\n",
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"\n",
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"文件列表内容示例如下\n",
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"```text\n",
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"0001.jpg\n",
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"0002.jpg\n",
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"0003.jpg\n",
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"...\n",
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"9999.jpg\n",
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"```\n",
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"\n",
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"为了快速走通流程，我们也提供了一个小的示例数据集，执行如下命令下载解压"
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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": "4feca89e-8d09-4393-a24c-50038689605a",
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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/imagenet_raw_demo/imagenet_raw_demo.tar.gz && tar -zxf imagenet_raw_demo.tar.gz"
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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": "88471c98-bcc2-4e2c-8783-a92cdfc42d4f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 重命名文件夹\n",
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"! mv imagenet_raw_demo imagenet_raw"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8d951808-6488-4d95-85a3-f917b9dfae29",
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"metadata": {},
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"source": [
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"### 模型训练\n",
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"\n",
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"这个Demo中我们采用[mocov2](https://arxiv.org/abs/2003.04297)自监督算法训练ResNet50 主干网络， 下载示例配置文件"
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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": "6f0045e5-7f2f-4c48-80be-277f002b548f",
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"metadata": {},
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"outputs": [],
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"source": [
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"! rm -rf mocov2_rn50_8xb32_200e_jpg.py\n",
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"! wget https://raw.githubusercontent.com/alibaba/EasyCV/master/configs/selfsup/mocov2/mocov2_rn50_8xb32_200e_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": "20a6b241-6d4e-4526-a533-b100a3cebb7f",
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"metadata": {},
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"source": [
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"为了缩短训练时间，打开配置文件 `mocov2_rn50_8xb32_200e_jpg.py`，修改`total_epoch`参数为20， 每隔1次迭代打印一次日志。\n",
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"\n",
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"```python\n",
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"# runtime settings\n",
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"total_epochs = 20\n",
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"\n",
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"# log config\n",
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"log_config=dict(interval=1)\n",
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"```\n",
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"\n",
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"正式训练时，建议使用`单机8卡`配合该配置文件使用，如果要使用单机单卡，建议调小`optimizer.lr`初始学习率\n",
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"\n"
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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": "af199c86-a0a5-4fac-96c6-8f5a357a6271",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 查看easycv安装位置\n",
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"import easycv\n",
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"print(easycv.__file__)"
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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": "d4aa1091-2c31-429a-b516-961b03356a22",
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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 torch.distributed.launch --nproc_per_node=1 --master_port=29930 \\\n",
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"/home/pai/lib/python3.6/site-packages/easycv/tools/train.py mocov2_rn50_8xb32_200e_jpg.py --work_dir work_dir/selfsup/jpg/rn50_mocov2 --launcher 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": "bc8fb5d9-5afa-4bd2-9bdf-949c45034279",
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"metadata": {},
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"source": [
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"### 使用自监督模型进行特征抽取"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a881a8d3-f767-4b70-a734-fca88fb75298",
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"metadata": {
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"tags": []
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},
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"source": [
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"#### 模型导出"
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]
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},
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{
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"cell_type": "markdown",
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"id": "efdd36dd-5d58-42e0-bf5c-c9b2f1e41d88",
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"metadata": {},
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"source": [
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"模型导出会对自监督模型信息裁剪，保留特征抽取必要的backbone和head"
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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": "6b566b5c-818d-42a8-814f-5288c1814ab0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 查看训练产生的模型文件\n",
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"!ls work_dir/selfsup/jpg/rn50_mocov2/*.pth"
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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": "f6e7d1fa-1620-4268-a6f3-fb75b683a011",
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"metadata": {},
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"outputs": [],
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"source": [
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"! python -m easycv.tools.export mocov2_rn50_8xb32_200e_jpg.py work_dir/selfsup/jpg/rn50_mocov2/epoch_20.pth work_dir/selfsup/jpg/rn50_mocov2/export.pth"
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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": "fd80ddd2-33a6-4972-88fe-ebb442c6c629",
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"metadata": {},
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"outputs": [],
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"source": [
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"#下载测试图片\n",
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"! wget http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/pretrained_models/easycv/product_detection/248347732153_1040.jpg"
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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": "84150726-c90c-4ab4-9a5d-c46d223d403d",
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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.feature_extractor import TorchFeatureExtractor\n",
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"\n",
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"# 修改output_ckpt指向\n",
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"output_ckpt = 'work_dir/selfsup/jpg/rn50_mocov2/export.pth'\n",
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"fe = TorchFeatureExtractor(output_ckpt)\n",
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"\n",
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"img = cv2.imread('248347732153_1040.jpg')\n",
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"img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
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"feature = fe.predict([img])\n",
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"print(feature[0]['feature'].shape)\n",
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"print(feature[0])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b72aae9b-79b6-447b-9e40-9bece4d0229d",
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"metadata": {},
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"source": [
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"### 使用自监督模型进行分类任务finetuning"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6dee5721-ea78-4177-b74c-f00006e9a542",
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"metadata": {},
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"source": [
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"参考EasyCV图像分类的demo， 在训练时加上--load_from 参数，使用自监督预训练的模型权重， 注意这里不需要使用"
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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": "b1e6ae4f-7a1c-416a-ae65-3e756c1d8551",
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"metadata": {},
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"outputs": [],
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"source": [
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"! python -m easycv.tools.train r50.py --work_dir work_dirs/classification/cifar10/r50 --load_from work_dir/selfsup/jpg/rn50_mocov2/epoch_20.pth"
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]
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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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}
|