PaddleX---YOLOv3目标检测
PaddleX简介：PaddleX是飞桨全流程开发工具，集飞桨核心框架、模型库、工具及组件等深度学习开发所需全部能力于一身，打通深度学习开发全流程，并提供简明易懂的Python API，方便用户根据实际生产需求进行直接调用或二次开发，为开发者提供飞桨全流程开发的最佳实践。目前，该工具代码已开源于GitHub，同时可访问PaddleX在线使用文档，快速查阅读使用教程和API文档说明。
PaddleX代码GitHub链接：https://github.com/PaddlePaddle/PaddleX/tree/develop
PaddleX文档链接：https://paddlex.readthedocs.io/zh_CN/latest/index.html
PaddleX官网链接：https://www.paddlepaddle.org.cn/paddle/paddlex
YOLOv3简介
YOLOv3是在YOLOv1和YOLOv2的基础上，在保持速度优势的前提下，提升了预测精度，尤其是加强了对小物体的识别能力。本示例在一个小数据集上展示了如何通过PaddleX进行训练/
1.安装PaddleX
!pip install paddlex -i https://mirror.baidu.com/pypi/simpleLooking in indexes: https://mirror.baidu.com/pypi/simple
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Building wheels for collected packages: pycocotools
Building wheel for pycocotools (setup.py) ... done
Created wheel for pycocotools: filename=pycocotools-2.0.0-cp37-cp37m-linux_x86_64.whl size=286722 sha256=56a7160274535c2c706cf34e5346d916f4cf13a81b1b5c9ed8db7c71b1fc157e
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Installing collected packages: colorama, paddlehub, pycocotools, paddleslim, paddlex
Found existing installation: paddlehub 1.5.0
Uninstalling paddlehub-1.5.0:
Successfully uninstalled paddlehub-1.5.0
Successfully installed colorama-0.4.3 paddlehub-1.6.2 paddleslim-1.0.1 paddlex-0.1.5 pycocotools-2.0.02.准备昆虫目标检测数据集
进行下载并解压数据集
#在paddlex的数据集datasets目录中下载昆虫数据集即insect_det.tar.gz
!wget https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz
#对下载的昆虫数据集进行解压
!tar xzf insect_det.tar.gz--2020-05-08 15:37:18-- https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz
Resolving bj.bcebos.com (bj.bcebos.com)... 182.61.200.229, 182.61.200.195
Connecting to bj.bcebos.com (bj.bcebos.com)|182.61.200.229|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 17304082 (17M) [application/octet-stream]
Saving to: ‘insect_det.tar.gz.1’
insect_det.tar.gz.1 100%[===================>] 16.50M 5.83MB/s in 2.8s
2020-05-08 15:37:21 (5.83 MB/s) - ‘insect_det.tar.gz.1’ saved [17304082/17304082]文件中多了insect_det.tar.gz及解压后的insect_det
labels.txt是标签的类别：6类
test_list.txt是测试集的路径
train_list.txt是训练集的路径
同样 val_list.txt是验证集的数据路径
接下来就是配置模型，进行训练及测试
3.模型训练
3.1 配置GPU
#设置使用0号GPU卡（如无GPU,执行此代码后仍然会使用GPU训练模型）
import matplotlib
matplotlib.use('Agg')
import os
os.enciron['CUDA_VISIBLE_DEVICES'] = '0'
import paddlex as pdx2020-05-08 15:42:38,051-INFO: font search path ['/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/afm', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/pdfcorefonts']
2020-05-08 15:42:38,452-INFO: generated new fontManager3.2 定义图像处理流程transforms
定义数据处理流程，其中训练和测试需要分别定义，训练过程包括了部分测试过程中不需要的数据增强操作，如在本示例中，训练过程使用了MixupImage、RandomDistort、RandomExpand、RandomCrop和RandomHorizontalFlip共5种数据增强方式。
from paddlex.det import transforms
train_transforms = transforms.Compose([
transforms.MixupImage(mixup_epoch=250),
transforms.RandomDistort(),
transforms.RandomExpand(),
transforms.RandomCrop(),
transforms.Resize(target_size=608,interp='RANDOM'),
transforms.RandomHorizontalFlip(),
transforms.Normalize(),
])
eval_transforms = transforms.Compose([
transforms.Resize(target_size=608,interp='CUBIC'),
transforms.Normalize(),
])3.3 定义数据集Dataset
目标检测可使用VOCDetection格式和COCODetection两种数据集，此处由于数据集为VOC格式，因此采用pdx.datasets.VOCDetection来加载数据集。
train_dataset = pdx.datasets.VOCDetection(
data_dir='insect_det',
file_list='insect_det/train_list.txt',
label_list='insect_det/labels.txt',
transforms=train_transforms,
shuffle=True)
eval_dataset = pdx.datasets.VOCDetection(
data_dir='insect_det',
file_list='insect_set/val_list.txt',
label_list='insect_det/labels.txt',
transforms=eval_transforms)2020-05-08 15:59:38 [INFO] Starting to read file list from dataset...
2020-05-08 15:59:38 [INFO] 169 samples in file insect_det/train_list.txt
creating index...
index created!
2020-05-08 15:59:39 [INFO] Starting to read file list from dataset...
2020-05-08 15:59:39 [INFO] 24 samples in file insect_det/val_list.txt
creating index...
index created!3.4 模型开始训练
使用本数据集在P40上训练，如有GPU,模型训练过程预估为40分钟左右；若无GPU，则预估为14小时左右。模型训练过程每间隔save_interval_epoochs轮会保存一次模型在save_dir目录下，同时在保存的过程中也会在验证集上计算相关指标。
num_classes = len(train_dataset.labels)
model = pdx.det.YOLOv3(num_classes=num_classes,backbone='DarkNet53')
model.train(
num_epochs=270,
train_dataset=train_dataset,
train_batch_size=8,
eval_dataset=eval_dataset,
learning_rate=0.000125,
lr_decay_epochs=[210, 240],
save_interval_epochs=20,
save_dir='output/yolov3_darknet53')4.模型预测
使用模型进行预测，同时使用pdx.det.visualize将结果进行可视化，可视化结果保存到./output/yolov3_mobilenetv1下，其中threshold代表Box的置信度阈值，将Box置信度低于该阈值的框过滤不进行可视化。
image_name = 'JPEGImages/0217.jpg'
result = model.predict(image_name)
pdx.det.visualize(image_name,rusult,threshold=0.5,save_dir='./output/yolov3_mobilenetv1')可视化结果如下图所示
