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暑 期 充 电 季
百度飞桨领航团全新推出“AI达人创造营” 十位飞桨开发者技术专家（PPDE）手把手教大家完成项目从idea思考到部署落地的全流程实战 最终让每位参与者都有一个可以给自己简历加分的项目 7月26日-8月16日，每晚 19:00-21:00 直播讲解、十位飞桨开发者技术专家（PPDE）手把手教助你成为AI达人 报名后，请加入课程 QQ 群861942585，QQ群用于直播提醒、实时答疑、交流互动等
PS:
本项目属于本次课程的大作业的一部分，希望大家可以学会使用paddleclas实现图像分类。
1、选择一个心仪的数据集
2、运行项目，能够跑通项目即可达到结业要求
3、记得生成新版本，公开项目哦~
加分项：
4、更换网络、进行数据预处理和调参
正如标题，采用paddleclas套件实现分类30分钟玩转PaddleClas（尝鲜版）
查看套件，可以知道
实现分类，仅仅需要我们将数据集提取为如下这种格式的txt文件即可（当然我们并不需要更大的训练集）
PS：
如有需要参考项目可看：
基于PaddleClas2.2的从零到落地安卓部署的奥特曼分类实战
iFLYTEK基于PaddleClas2.2的广告分类baseline非官方
本次数据集有五个可供大家选择。分别是：
- 猫12分类
- 垃圾40分类
- 场景5分类
- 食物5分类
- 蝴蝶20分类
数据集：都是不同类别的文件夹下放置了对应文件夹名字的类别图片
# 先导入库
from sklearn.utils import shuffle
import os
import pandas as pd
import numpy as np
from PIL import Image
import paddle
import paddle.nn as nn
import random# 忽略（垃圾）警告信息
# 在python中运行代码经常会遇到的情况是——代码可以正常运行但是会提示警告，有时特别讨厌。
# 那么如何来控制警告输出呢？其实很简单，python通过调用warnings模块中定义的warn()函数来发出警告。我们可以通过警告过滤器进行控制是否发出警告消息。
import warnings
warnings.filterwarnings("ignore")# 项目挂载的数据集先解压出来，待解压完毕，刷新后可发现左侧文件夹根目录出现五个zip
!unzip -oq /home/aistudio/data/data103736/五种图像分类数据集.zip左侧可以看到如图所示五个zip
# 本项目以食物分类为例进行介绍，因为分类大多数情况下是不存在标签文件的，猫分类已经有了标签，省去了数据处理的操作
# (此处需要你根据自己的选择进行解压对应的文件)
# 解压完毕左侧出现文件夹，即为需要分类的文件
!unzip -oq /home/aistudio/食物5分类.zip# 查看结构，正为一个类别下有一系列对应的图片
!tree foods/5 directories, 5000 files
五类食物图片
- beef_carpaccio
- baby_back_ribs
- beef_tartare
- apple_pie
- baklava
具体结构如下：
foods/
├── apple_pie
│ ├── 1005649.jpg
│ ├── 1011328.jpg
│ ├── 101251.jpg
import os
# -*- coding: utf-8 -*-
# 根据官方paddleclas的提示，我们需要把图像变为两个txt文件
# train_list.txt（训练集）
# val_list.txt（验证集）
# 先把路径搞定 比如：foods/beef_carpaccio/855780.jpg ,读取到并写入txt
# 根据左侧生成的文件夹名字来写根目录
dirpath = "foods"
# 先得到总的txt后续再进行划分，因为要划分出验证集，所以要先打乱，因为原本是有序的
def get_all_txt():
all_list = []
i = 0 # 标记总文件数量
j = 0 # 标记文件类别
for root,dirs,files in os.walk(dirpath): # 分别代表根目录、文件夹、文件
for file in files:
i = i + 1
# 文件中每行格式： 图像相对路径 图像的label_id（数字类别）（注意：中间有空格）。
imgpath = os.path.join(root,file)
all_list.append(imgpath+" "+str(j)+"\n")
j = j + 1
allstr = ''.join(all_list)
f = open('all_list.txt','w',encoding='utf-8')
f.write(allstr)
return all_list , i
all_list,all_lenth = get_all_txt()
print(all_lenth)5000
# 把数据打乱
all_list = shuffle(all_list)
allstr = ''.join(all_list)
f = open('all_list.txt','w',encoding='utf-8')
f.write(allstr)
print("打乱成功，并重新写入文本")打乱成功，并重新写入文本
# 按照比例划分数据集 食品的数据有5000张图片，不算大数据，一般9:1即可
train_size = int(all_lenth * 0.9)
train_list = all_list[:train_size]
val_list = all_list[train_size:]
print(len(train_list))
print(len(val_list))4500
500
# 运行cell，生成训练集txt
train_txt = ''.join(train_list)
f_train = open('train_list.txt','w',encoding='utf-8')
f_train.write(train_txt)
f_train.close()
print("train_list.txt 生成成功！")
# 运行cell，生成验证集txt
val_txt = ''.join(val_list)
f_val = open('val_list.txt','w',encoding='utf-8')
f_val.write(val_txt)
f_val.close()
print("val_list.txt 生成成功！")train_list.txt 生成成功！
val_list.txt 生成成功！
数据集核实完搞定成功的前提下，可以准备更改原文档的参数进行实现自己的图片分类了！
这里采用paddleclas的2.2版本，好用！
# 先把paddleclas安装上再说
# 安装paddleclas以及相关三方包(好像studio自带的已经够用了，无需安装了)
!git clone https://gitee.com/paddlepaddle/PaddleClas.git -b release/2.2
# 我这里安装相关包时，花了30几分钟还有错误提示，不管他即可
!pip install --upgrade -r PaddleClas/requirements.txt -i https://mirror.baidu.com/pypi/simpleCloning into 'PaddleClas'...
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#因为后续paddleclas的命令需要在PaddleClas目录下，所以进入PaddleClas根目录，执行此命令
%cd PaddleClas
!ls/home/aistudio/PaddleClas
dataset hubconf.py MANIFEST.in README_ch.md requirements.txt
deploy __init__.py paddleclas.py README_en.md setup.py
docs LICENSE ppcls README.md tools
# 将图片移动到paddleclas下面的数据集里面
# 至于为什么现在移动，也是我的一点小技巧，防止之前移动的话，生成的txt的路径是全路径，反而需要去掉路径的一部分
!mv ../foods/ dataset/# 挪动文件到对应目录
!mv ../all_list.txt dataset/foods
!mv ../train_list.txt dataset/foods
!mv ../val_list.txt dataset/foods主要是以下几点：分类数、图片总量、训练和验证的路径、图像尺寸、数据预处理、训练和预测的num_workers: 0
路径如下：
PaddleClas/ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml
（主要的参数已经进行注释，一定要过一遍）
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
# 使用GPU训练
device: gpu
# 每几个轮次保存一次
save_interval: 1
eval_during_train: True
# 每几个轮次验证一次
eval_interval: 1
# 训练轮次
epochs: 20
print_batch_step: 1
use_visualdl: True #开启可视化（目前平台不可用）
# used for static mode and model export
# 图像大小
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# training model under @to_static
to_static: False
# model architecture
Arch:
# 采用的网络
name: ResNet50
# 类别数 多了个0类 0-5 0无用
class_num: 6
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Piecewise
learning_rate: 0.015
decay_epochs: [30, 60, 90]
values: [0.1, 0.01, 0.001, 0.0001]
regularizer:
name: 'L2'
coeff: 0.0005
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
# 根路径
image_root: ./dataset/
# 前面自己生产得到的训练集文本路径
cls_label_path: ./dataset/foods/train_list.txt
# 数据预处理
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- RandFlipImage:
flip_code: 1
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: True
loader:
num_workers: 0
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
# 根路径
image_root: ./dataset/
# 前面自己生产得到的验证集文本路径
cls_label_path: ./dataset/foods/val_list.txt
# 数据预处理
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: True
loader:
num_workers: 0
use_shared_memory: True
Infer:
infer_imgs: ./dataset/foods/beef_carpaccio/855780.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: Topk
# 输出的可能性最高的前topk个
topk: 5
# 标签文件 需要自己新建文件
class_id_map_file: ./dataset/label_list.txt
Metric:
Train:
- TopkAcc:
topk: [1, 5]
Eval:
- TopkAcc:
topk: [1, 5]
这个是在预测时生成对照的依据，在上个文件有提到这个
# 标签文件 需要自己新建文件
class_id_map_file: dataset/label_list.txt
按照对应的进行编写：
如食品分类(要对照之前的txt的类别确认无误)
1 beef_carpaccio
2 baby_back_ribs
3 beef_tartare
4 apple_pie
5 baklava
# 提示，运行过程中可能存在坏图的情况，但是不用担心，训练过程不受影响。
# 仅供参考，我只跑了五轮，准确率很低
!python3 tools/train.py \
-c ./ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml[2021/08/15 11:09:50] root INFO: Already save model in ./output/ResNet50/latest
# 更换为你训练的网络，需要预测的文件，上面训练所得到的的最优模型文件
# 我这里是不严谨的，直接使用训练集的图片进行验证，大家可以去百度搜一些相关的图片传上来，进行预测
!python3 tools/infer.py \
-c ./ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml \
-o Infer.infer_imgs=dataset/foods/baby_back_ribs/319516.jpg \
-o Global.pretrained_model=output/ResNet50/best_model/home/aistudio/PaddleClas/ppcls/arch/backbone/model_zoo/vision_transformer.py:15: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
from collections import Callable
[2021/08/15 11:09:53] root INFO:
===========================================================
== PaddleClas is powered by PaddlePaddle ! ==
===========================================================
== ==
== For more info please go to the following website. ==
== ==
== https://github.com/PaddlePaddle/PaddleClas ==
===========================================================
[2021/08/15 11:09:53] root INFO: Arch :
[2021/08/15 11:09:53] root INFO: class_num : 6
[2021/08/15 11:09:53] root INFO: name : ResNet50
[2021/08/15 11:09:53] root INFO: DataLoader :
[2021/08/15 11:09:53] root INFO: Eval :
[2021/08/15 11:09:53] root INFO: dataset :
[2021/08/15 11:09:53] root INFO: cls_label_path : ./dataset/foods/val_list.txt
[2021/08/15 11:09:53] root INFO: image_root : ./dataset/
[2021/08/15 11:09:53] root INFO: name : ImageNetDataset
[2021/08/15 11:09:53] root INFO: transform_ops :
[2021/08/15 11:09:53] root INFO: DecodeImage :
[2021/08/15 11:09:53] root INFO: channel_first : False
[2021/08/15 11:09:53] root INFO: to_rgb : True
[2021/08/15 11:09:53] root INFO: ResizeImage :
[2021/08/15 11:09:53] root INFO: resize_short : 256
[2021/08/15 11:09:53] root INFO: CropImage :
[2021/08/15 11:09:53] root INFO: size : 224
[2021/08/15 11:09:53] root INFO: NormalizeImage :
[2021/08/15 11:09:53] root INFO: mean : [0.485, 0.456, 0.406]
[2021/08/15 11:09:53] root INFO: order :
[2021/08/15 11:09:53] root INFO: scale : 1.0/255.0
[2021/08/15 11:09:53] root INFO: std : [0.229, 0.224, 0.225]
[2021/08/15 11:09:53] root INFO: loader :
[2021/08/15 11:09:53] root INFO: num_workers : 0
[2021/08/15 11:09:53] root INFO: use_shared_memory : True
[2021/08/15 11:09:53] root INFO: sampler :
[2021/08/15 11:09:53] root INFO: batch_size : 128
[2021/08/15 11:09:53] root INFO: drop_last : False
[2021/08/15 11:09:53] root INFO: name : DistributedBatchSampler
[2021/08/15 11:09:53] root INFO: shuffle : True
[2021/08/15 11:09:53] root INFO: Train :
[2021/08/15 11:09:53] root INFO: dataset :
[2021/08/15 11:09:53] root INFO: cls_label_path : ./dataset/foods/train_list.txt
[2021/08/15 11:09:53] root INFO: image_root : ./dataset/
[2021/08/15 11:09:53] root INFO: name : ImageNetDataset
[2021/08/15 11:09:53] root INFO: transform_ops :
[2021/08/15 11:09:53] root INFO: DecodeImage :
[2021/08/15 11:09:53] root INFO: channel_first : False
[2021/08/15 11:09:53] root INFO: to_rgb : True
[2021/08/15 11:09:53] root INFO: ResizeImage :
[2021/08/15 11:09:53] root INFO: resize_short : 256
[2021/08/15 11:09:53] root INFO: CropImage :
[2021/08/15 11:09:53] root INFO: size : 224
[2021/08/15 11:09:53] root INFO: RandFlipImage :
[2021/08/15 11:09:53] root INFO: flip_code : 1
[2021/08/15 11:09:53] root INFO: NormalizeImage :
[2021/08/15 11:09:53] root INFO: mean : [0.485, 0.456, 0.406]
[2021/08/15 11:09:53] root INFO: order :
[2021/08/15 11:09:53] root INFO: scale : 1.0/255.0
[2021/08/15 11:09:53] root INFO: std : [0.229, 0.224, 0.225]
[2021/08/15 11:09:53] root INFO: loader :
[2021/08/15 11:09:53] root INFO: num_workers : 0
[2021/08/15 11:09:53] root INFO: use_shared_memory : True
[2021/08/15 11:09:53] root INFO: sampler :
[2021/08/15 11:09:53] root INFO: batch_size : 128
[2021/08/15 11:09:53] root INFO: drop_last : False
[2021/08/15 11:09:53] root INFO: name : DistributedBatchSampler
[2021/08/15 11:09:53] root INFO: shuffle : True
[2021/08/15 11:09:53] root INFO: Global :
[2021/08/15 11:09:53] root INFO: checkpoints : None
[2021/08/15 11:09:53] root INFO: device : gpu
[2021/08/15 11:09:53] root INFO: epochs : 20
[2021/08/15 11:09:53] root INFO: eval_during_train : True
[2021/08/15 11:09:53] root INFO: eval_interval : 1
[2021/08/15 11:09:53] root INFO: image_shape : [3, 224, 224]
[2021/08/15 11:09:53] root INFO: output_dir : ./output/
[2021/08/15 11:09:53] root INFO: pretrained_model : output/ResNet50/best_model
[2021/08/15 11:09:53] root INFO: print_batch_step : 1
[2021/08/15 11:09:53] root INFO: save_inference_dir : ./inference
[2021/08/15 11:09:53] root INFO: save_interval : 1
[2021/08/15 11:09:53] root INFO: to_static : False
[2021/08/15 11:09:53] root INFO: use_visualdl : True
[2021/08/15 11:09:53] root INFO: Infer :
[2021/08/15 11:09:53] root INFO: PostProcess :
[2021/08/15 11:09:53] root INFO: class_id_map_file : ./dataset/label_list.txt
[2021/08/15 11:09:53] root INFO: name : Topk
[2021/08/15 11:09:53] root INFO: topk : 5
[2021/08/15 11:09:53] root INFO: batch_size : 10
[2021/08/15 11:09:53] root INFO: infer_imgs : dataset/foods/baby_back_ribs/319516.jpg
[2021/08/15 11:09:53] root INFO: transforms :
[2021/08/15 11:09:53] root INFO: DecodeImage :
[2021/08/15 11:09:53] root INFO: channel_first : False
[2021/08/15 11:09:53] root INFO: to_rgb : True
[2021/08/15 11:09:53] root INFO: ResizeImage :
[2021/08/15 11:09:53] root INFO: resize_short : 256
[2021/08/15 11:09:53] root INFO: CropImage :
[2021/08/15 11:09:53] root INFO: size : 224
[2021/08/15 11:09:53] root INFO: NormalizeImage :
[2021/08/15 11:09:53] root INFO: mean : [0.485, 0.456, 0.406]
[2021/08/15 11:09:53] root INFO: order :
[2021/08/15 11:09:53] root INFO: scale : 1.0/255.0
[2021/08/15 11:09:53] root INFO: std : [0.229, 0.224, 0.225]
[2021/08/15 11:09:53] root INFO: ToCHWImage : None
[2021/08/15 11:09:53] root INFO: Loss :
[2021/08/15 11:09:53] root INFO: Eval :
[2021/08/15 11:09:53] root INFO: CELoss :
[2021/08/15 11:09:53] root INFO: weight : 1.0
[2021/08/15 11:09:53] root INFO: Train :
[2021/08/15 11:09:53] root INFO: CELoss :
[2021/08/15 11:09:53] root INFO: weight : 1.0
[2021/08/15 11:09:53] root INFO: Metric :
[2021/08/15 11:09:53] root INFO: Eval :
[2021/08/15 11:09:53] root INFO: TopkAcc :
[2021/08/15 11:09:53] root INFO: topk : [1, 5]
[2021/08/15 11:09:53] root INFO: Train :
[2021/08/15 11:09:53] root INFO: TopkAcc :
[2021/08/15 11:09:53] root INFO: topk : [1, 5]
[2021/08/15 11:09:53] root INFO: Optimizer :
[2021/08/15 11:09:53] root INFO: lr :
[2021/08/15 11:09:53] root INFO: decay_epochs : [30, 60, 90]
[2021/08/15 11:09:53] root INFO: learning_rate : 0.015
[2021/08/15 11:09:53] root INFO: name : Piecewise
[2021/08/15 11:09:53] root INFO: values : [0.1, 0.01, 0.001, 0.0001]
[2021/08/15 11:09:53] root INFO: momentum : 0.9
[2021/08/15 11:09:53] root INFO: name : Momentum
[2021/08/15 11:09:53] root INFO: regularizer :
[2021/08/15 11:09:53] root INFO: coeff : 0.0005
[2021/08/15 11:09:53] root INFO: name : L2
W0815 11:09:53.427887 9495 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0815 11:09:53.432844 9495 device_context.cc:422] device: 0, cuDNN Version: 7.6.
[2021/08/15 11:09:58] root INFO: train with paddle 2.1.2 and device CUDAPlace(0)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data.dtype == np.object:
[{'class_ids': [3, 5, 2, 4, 1], 'scores': [0.73711, 0.16288, 0.09044, 0.00815, 0.00142], 'file_name': 'dataset/foods/baby_back_ribs/319516.jpg', 'label_names': ['beef_tartare', 'baklava', 'baby_back_ribs', 'apple_pie', 'beef_carpaccio']}]
运行完成，最后几行会得到结果如下形式：
[{'class_ids': [5, 1, 3, 4, 2],
'scores': [0.48433, 0.26765, 0.13903, 0.05609, 0.05162],
'file_name': 'dataset/foods/baby_back_ribs/319516.jpg',
'label_names': ['baklava', 'beef_carpaccio', 'beef_tartare', 'apple_pie', 'baby_back_ribs']}]
可以发现，预测结果不对，准确率很低，但整体的项目流程你已经掌握了！
训练轮数还有很大提升空间，自行变动参数直到预测正确为止~
恭喜你学会了paddleclas图像分类！
- 生成版本
- 有能力者导出markdown发表到github上
- 公开项目
- 祝大家顺利结业~
