Python RetainNet Examples

Example #1

# 训练数据集,VOC格式数据集, 训练数据取自 ImageSets/Main/train.txt'
train_dataset = vocdataset(cfg,
                           is_train=True,
                           transform=transfrom(cfg, is_train=True),
                           target_transform=targettransform(cfg))

# 测试数据集,VOC格式数据集, 测试数据取自 ImageSets/Main/eval.txt'
test_dataset = vocdataset(cfg=cfg,
                          is_train=False,
                          transform=transfrom(cfg=cfg, is_train=False),
                          target_transform=targettransform(cfg))

if __name__ == '__main__':
    """
    使用时,请先打开visdom
    
    命令行 输入  pip install visdom          进行安装 
    输入        python -m visdom.server'    启动
    """

    # 首次调用会下载resnet预训练模型

    # 实例化模型. 模型的具体各种参数在Config文件中进行配置
    net = RetainNet(cfg)
    # 将模型移动到gpu上,cfg.DEVICE.MAINDEVICE定义了模型所使用的主GPU
    net.to(cfg.DEVICE.MAINDEVICE)
    # 初始化训练器,训练器参数通过cfg进行配置;也可传入参数进行配置,但不建议
    trainer = Trainer(cfg)
    # 训练器开始在 数据集上训练模型
    trainer(net, train_dataset)

Example #2

File: Demo_detect_video.py Project: shuowoshishui/re-tinanet

# -*- coding: utf-8 -*-
# @Author  : LG
from Model import RetainNet
from Configs import _C as cfg

# 实例化模型
net = RetainNet(cfg)
# 使用cpu或gpu
net.to('cuda')
# 模型从权重文件中加载权重
net.load_pretrained_weight(cfg.FILE.MODEL_SAVE_ROOT + '/' + 'model_8000.pkl')

video_path = '/home/chenshuo/Retinanet-Pytorch/test.mp4'

# 进行检测,
# if save_video_path不为None,则不保存视频,如需保存视频save_video_path=XXX.mp4 ,
# show=True,实时显示检测结果
net.Detect_video(video_path=video_path,
                 score_threshold=0.02,
                 save_video_path='/home/chenshuo/Retinanet-Pytorch/test1.mp4',
                 show=False)

Example #3

# -*- coding: utf-8 -*-
# @Author  : LG
from Model import RetainNet
from Configs import _C as cfg

# 实例化模型
net = RetainNet(cfg)
# 使用cpu或gpu
net.to('cuda')
# 模型从权重文件中加载权重
net.load_pretrained_weight('XXX.pkl')

video_path = 'XXX.mp4'

# 进行检测,
# if save_video_path不为None,则不保存视频,如需保存视频save_video_path=XXX.mp4 ,
# show=True,实时显示检测结果
net.Detect_video(video_path=video_path,
                 score_threshold=0.02,
                 save_video_path=None,
                 show=True)

Example #4

File: Demo_detect_one_image.py Project: shuowoshishui/re-tinanet

# -*- coding: utf-8 -*-
# @Author  : LG
from Model import RetainNet
from Configs import _C as cfg
from PIL import Image
import matplotlib.pyplot as plt
# 实例化模型
net = RetainNet(cfg)
# 使用cpu或gpu
net.to('cuda')
# 模型从权重文件中加载权重
net.load_pretrained_weight('/home/chenshuo/Retinanet-Pytorch/Weights/trained1/model_20000.pkl')
# 打开图片
image = Image.open("/home/chenshuo/RTTS/JPEGImages/BD_Baidu_145.png")
# 进行检测, 分别返回 绘制了检测框的图片数据/回归框/标签/分数.
drawn_image, boxes, labels, scores = net.Detect_single_img(image=image,score_threshold=0.5)

plt.imsave('XXX_det.png',drawn_image)
plt.imshow(drawn_image)
plt.show()

Example #5

from Model import RetainNet, Evaler
from Data import vocdataset
from Configs import _C as cfg
from Data import transfrom, targettransform

# 训练数据集,VOC格式数据集, 训练数据取自 ImageSets/Main/train.txt'
train_dataset = vocdataset(cfg,
                           is_train=True,
                           transform=transfrom(cfg, is_train=True),
                           target_transform=targettransform(cfg))

# 测试数据集,VOC格式数据集, 测试数据取自 ImageSets/Main/eval.txt'
test_dataset = vocdataset(cfg=cfg,
                          is_train=False,
                          transform=transfrom(cfg=cfg, is_train=False),
                          target_transform=targettransform(cfg))

if __name__ == '__main__':
    # 模型测试只支持GPU单卡或多卡,不支持cpu
    net = RetainNet(cfg)
    # 将模型移动到gpu上,cfg.DEVICE.MAINDEVICE定义了模型所使用的主GPU
    net.to(cfg.DEVICE.MAINDEVICE)
    # 模型从权重文件中加载权重
    net.load_pretrained_weight('XXX.pkl')
    # 初始化验证器,验证器参数通过cfg进行配置;也可传入参数进行配置,但不建议
    evaler = Evaler(cfg, eval_devices=None)
    # 验证器开始在数据集上验证模型
    ap, map = evaler(model=net, test_dataset=test_dataset)
    print(ap)
    print(map)

Example #6

from Model import RetainNet, Evaler
from Data import vocdataset
from Configs import _C as cfg
from Data import transfrom, targettransform

# 训练数据集,VOC格式数据集, 训练数据取自 ImageSets/Main/train.txt'
train_dataset = vocdataset(cfg,
                           is_train=True,
                           transform=transfrom(cfg, is_train=True),
                           target_transform=targettransform(cfg))

# 测试数据集,VOC格式数据集, 测试数据取自 ImageSets/Main/eval.txt'
test_dataset = vocdataset(cfg=cfg,
                          is_train=False,
                          transform=transfrom(cfg=cfg, is_train=False),
                          target_transform=targettransform(cfg))

if __name__ == '__main__':
    # 模型测试只支持GPU单卡或多卡,不支持cpu
    net = RetainNet(cfg)
    # 将模型移动到gpu上,cfg.DEVICE.MAINDEVICE定义了模型所使用的主GPU
    net.to(cfg.DEVICE.MAINDEVICE)
    # 模型从权重文件中加载权重
    net.load_pretrained_weight(cfg.FILE.MODEL_SAVE_ROOT + '/' +
                               'model_20000.pkl')
    # 初始化验证器,验证器参数通过cfg进行配置;也可传入参数进行配置,但不建议
    evaler = Evaler(cfg, eval_devices=None)
    # 验证器开始在数据集上验证模型
    ap, map = evaler(model=net, test_dataset=test_dataset)
    print('ap: ', ap)
    print('map: ', map)

Example #7

File: Demo_detect_one_image.py Project: yizhex/Retinanet-Pytorch

# -*- coding: utf-8 -*-
# @Author  : LG
from Model import RetainNet
from Configs import _C as cfg
from PIL import Image
import matplotlib.pyplot as plt
# 实例化模型
net = RetainNet(cfg)
# 使用cpu或gpu
net.to('cuda')
# 模型从权重文件中加载权重
net.load_pretrained_weight(
    '/home/super/PycharmProjects/Retinanet-Pytorch/Weights/trained/model_35.pkl'
)
# 打开图片
image = Image.open(
    "/home/super/VOC_det/VOCdevkit/VOC2007/JPEGImages/000009.jpg")
# 进行检测, 分别返回 绘制了检测框的图片数据/回归框/标签/分数.
drawn_image, boxes, labels, scores = net.Detect_single_img(image=image,
                                                           score_threshold=0.5)

plt.imsave('XXX_det.jpg', drawn_image)
plt.imshow(drawn_image)
plt.show()