摘要：本案例是CenterNet-Hourglass论文复现的体验案例，此模型是对Objects as Points 中提出的CenterNet进行结果复现。
本文分享自华为云社区《CenterNet-Hourglass (物体检测/Pytorch)》，作者：HWCloudAI。
目标检测常采用Anchor的方法来获取物体可能存在的位置，再对该位置进行分类，这样的做法耗时、低效，同时需要后处理（比如NMS）。CenterNet将目标看成一个点，即目标bounding box的中心点，整个问题转变成了关键点估计问题，其他目标属性，比如尺寸、3D位置、方向和姿态等都以估计的中心点为基准进行参数回归。
本案例是CenterNet-Hourglass论文复现的体验案例，此模型是对Objects as Points 中提出的CenterNet进行结果复现（原论文Table 2 最后一行）。本模型是以Hourglass网络架构作为backbone，以ExtremNet 作为预训练模型，在COCO数据集上进行50epochs的训练后得到的。本项目是基于原论文的官方代码进行针对ModelArts平台的修改来实现ModelArts上的训练与部署。
具体算法介绍：https://marketplace.huaweicloud.com/markets/aihub/modelhub/detail/?id=380f95a6-1552-4128-ac96-36066258853f
注意事项：
1.本案例使用框架:PyTorch1.4.0
2.本案例使用硬件:GPU: 1*NVIDIA-V100NV32(32GB) | CPU: 8 核 64GB
3.运行代码方法: 点击本页面顶部菜单栏的三角形运行按钮或按Ctrl+Enter键 运行每个方块中的代码
4.JupyterLab的详细用法: 请参考《ModelAtrs JupyterLab使用指导》
5.碰到问题的解决办法: 请参考《ModelAtrs JupyterLab常见问题解决办法》
1.下载数据和代码
运行下面代码，进行数据和代码的下载和解压
本案例使用COCO数据集。
import os
#数据代码下载
!wget https://obs-aigallery-zc.obs.cn-north-4.myhuaweicloud.com/algorithm/CenterNet.zip
# 解压缩
os.system('unzip CenterNet.zip -d ./')
--2021-06-25 17:50:11-- https://obs-aigallery-zc.obs.cn-north-4.myhuaweicloud.com/algorithm/CenterNet.zip
Resolving proxy-notebook.modelarts.com (proxy-notebook.modelarts.com)... 192.168.6.62
Connecting to proxy-notebook.modelarts.com (proxy-notebook.modelarts.com)|192.168.6.62|:8083... connected.
Proxy request sent, awaiting response... 200 OK
Length: 1529663572 (1.4G) [application/zip]
Saving to: ‘CenterNet.zip’
CenterNet.zip 100%[===================>] 1.42G 279MB/s in 5.6s
2021-06-25 17:50:16 (261 MB/s) - ‘CenterNet.zip’ saved [1529663572/1529663572]
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2.训练
2.1依赖库加载和安装
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
root_path = './CenterNet/'
os.chdir(root_path)
os.system('pip install pycocotools')
import _init_paths
import torch
import torch.utils.data
from opts import opts
from models.model import create_model, load_model, save_model
from models.data_parallel import DataParallel
from logger import Logger
from datasets.dataset_factory import get_dataset
from trains.train_factory import train_factory
from evaluation import test, prefetch_test, image_infer
USE_MODELARTS = True
INFO:root:Using MoXing-v2.0.0.rc0-19e4d3ab
INFO:root:Using OBS-Python-SDK-3.20.9.1
NMS not imported! If you need it, do
cd $CenterNet_ROOT/src/lib/external
make
2.2训练函数
def main(opt):
torch.manual_seed(opt.seed)
torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test
Dataset = get_dataset(opt.dataset, opt.task)
opt = opts().update_dataset_info_and_set_heads(opt, Dataset)
logger = Logger(opt)
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
opt.device = torch.device('cuda' if opt.gpus[0] >= 0 else 'cpu')
print('Creating model...')
model = create_model(opt.arch, opt.heads, opt.head_conv)
optimizer = torch.optim.Adam(model.parameters(), opt.lr)
start_epoch = 0
if opt.load_model != '':
model, optimizer, start_epoch = load_model(
model, opt.load_model, optimizer, opt.resume, opt.lr, opt.lr_step)
Trainer = train_factory[opt.task]
trainer = Trainer(opt, model, optimizer)
trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
print('Setting up data...')
train_loader = torch.utils.data.DataLoader(
Dataset(opt, 'train'),
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True,
drop_last=True
)
print('Starting training...')
best = 1e10
for epoch in range(start_epoch + 1, opt.num_epochs + 1):
mark = epoch if opt.save_all else 'last'
log_dict_train, _ = trainer.train(epoch, train_loader)
logger.write('epoch: {} |'.format(epoch))
for k, v in log_dict_train.items():
logger.scalar_summary('train_{}'.format(k), v, epoch)
logger.write('{} {:8f} | '.format(k, v))
save_model(os.path.join(opt.save_dir, 'model_last.pth'),
epoch, model)
logger.write('\n')
if epoch in opt.lr_step:
save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)),
epoch, model, optimizer)
lr = opt.lr * (0.1 ** (opt.lr_step.index(epoch) + 1))
print('Drop LR to', lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
logger.close()
2.3开始训练
训练需要一点时间，请耐心等待
if __name__ == '__main__':
opt = opts().parse()
if USE_MODELARTS:
pwd = os.getcwd()
print('Copying dataset to work space...')
print('Listing directory: ')
print(os.listdir())
if not os.path.exists(opt.save_dir):
os.makedirs(opt.save_dir)
main(opt)
if USE_MODELARTS:
print("Processing model checkpoints & service config for deployment...")
if not opt.eval:
infer_dir = os.path.join(opt.save_dir, 'model')
os.makedirs(infer_dir)
os.system(f'mv ./trained_model/* {infer_dir}')
pretrained_pth = os.path.join(infer_dir, '*.pth')
ckpt_dir = os.path.join(opt.save_dir, 'checkpoints')
os.makedirs(ckpt_dir)
os.system(f'mv {pretrained_pth} {ckpt_dir}')
pth_files = os.path.join(opt.save_dir, '*.pth')
infer_pth = os.path.join(ckpt_dir, f'{opt.model_deploy}.pth')
os.system(f'mv {pth_files} {ckpt_dir}')
os.system(f'mv {infer_pth} {infer_dir}')
print(os.listdir(opt.save_dir))
print("ModelArts post-training work is done!")
Fix size testing.
training chunk_sizes: [8]
The output will be saved to ./output/exp/ctdet/default
Copying dataset to work space...
Listing directory:
['pre-trained_weights', '.ipynb_checkpoints', 'coco_eval.py', 'train.py', 'coco', 'output', 'training_logs', 'trained_model', '_init_paths.py', '__pycache__', 'coco_classes.py', 'lib', 'evaluation.py']
heads {'hm': 80, 'wh': 2, 'reg': 2}
Creating model...
loaded ./trained_model/epoch_50_mAP_42.7.pth, epoch 50
Setting up data...
==> initializing coco 2017 train data.
loading annotations into memory...
Done (t=0.54s)
creating index...
index created!
Loaded train 5000 samples
Starting training...
/home/ma-user/anaconda3/envs/Pytorch-1.4.0/lib/python3.6/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
warnings.warn(warning.format(ret))
ctdet/default| train: [1][0/625] |loss 1.7568 |hm_loss 1.3771 |wh_loss 1.9394 |off_loss 0.1857 |Data 0.384s (0.384s) |Net 5.019s (5.019s)
ctdet/default| train: [1][200/625] |loss 1.9275 |hm_loss 1.4429 |wh_loss 2.7269 |off_loss 0.2119 |Data 0.001s (0.003s) |Net 0.759s (0.779s)
ctdet/default| train: [1][400/625] |loss 1.9290 |hm_loss 1.4430 |wh_loss 2.7423 |off_loss 0.2118 |Data 0.001s (0.002s) |Net 0.760s (0.770s)
ctdet/default| train: [1][600/625] |loss 1.9276 |hm_loss 1.4397 |wh_loss 2.7623 |off_loss 0.2117 |Data 0.001s (0.002s) |Net 0.765s (0.767s)
Processing model checkpoints & service config for deployment...
['model', 'logs_2021-06-25-17-51', 'opt.txt', 'checkpoints']
ModelArts post-training work is done!
3.模型测试
3.1推理函数
# -*- coding: utf-8 -*-
# TODO 添加模型运行需要导入的模块
import os
import torch
import numpy as np
from PIL import Image
from io import BytesIO
from collections import OrderedDict
import cv2
import sys
sys.path.insert(0, './lib')
from opts import opts
from coco_classes import coco_class_map
from detectors.detector_factory import detector_factory
class ModelClass():
def __init__(self, model_path):
self.model_path = model_path # 本行代码必须保留，且无需修改
self.opt = opts().parse()
self.opt.num_classes = 80
self.opt.resume = True
self.opt.keep_res = True
self.opt.fix_res = False
self.opt.heads = {'hm': 80, 'wh': 2, 'reg': 2}
self.opt.load_model = model_path
self.opt.mean = np.array([0.40789654, 0.44719302, 0.47026115],
dtype=np.float32).reshape(1, 1, 3)
self.opt.std = np.array([0.28863828, 0.27408164, 0.27809835],
dtype=np.float32).reshape(1, 1, 3)
self.opt.batch_infer = False
# configurable varibales:
if 'BATCH_INFER' in os.environ:
print('Batch inference mode!')
self.opt.batch_infer = True
if 'FLIP_TEST' in os.environ:
print('Flip test!')
self.opt.flip_test = True
if 'MULTI_SCALE' in os.environ:
print('Multi scale!')
self.opt.test_scales = [0.5,0.75,1,1.25,1.5]
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if not torch.cuda.is_available():
self.opt.gpus = [-1]
self.class_map = coco_class_map()
torch.set_grad_enabled(False)
Detector = detector_factory[self.opt.task]
self.detector = Detector(self.opt)
print('load model success')
def predict(self, file_name):
image = Image.open(file_name).convert('RGB')
img = np.array(image)
img = img[:, :, ::-1]
results = self.detector.run(img)['results']
image = cv2.cvtColor(np.asarray(image),cv2.COLOR_RGB2BGR)
if not self.opt.batch_infer:
for c_id, dets in results.items():
for det in dets:
if det[4] > self.opt.vis_thresh:
scores = str(round(float(det[4]), 4))
classes = self.class_map[c_id]
image = cv2.rectangle(image,(int(det[0]),int(det[1])),(int(det[2]),int(det[3])),(0,255,0),2)
image = cv2.putText(image,classes+':'+scores,(int(det[0]),int(det[1])),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,0,255),2)
else:
for c_id, dets in results.items():
for det in dets:
scores = str(round(float(det[4]), 4))
classes = self.class_map[c_id]
image = cv2.rectangle(image,(int(det[0]),int(det[1])),(int(det[2]),int(det[3])),(0,255,0),2)
image = cv2.putText(image,classes+':'+scores,(int(det[0]),int(det[1])),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,255),2)
return image
3.2开始推理
可以自行修改预测的图像路径
if __name__ == '__main__':
import matplotlib.pyplot as plt
img_path = './coco/train/000000021903.jpg'
model_path = './output/exp/ctdet/default/model/model_last.pth' #模型的保存路径，你可以自己找一下
# 以下代码无需修改
my_model = ModelClass(model_path)
result = my_model.predict(img_path)
result = Image.fromarray(cv2.cvtColor(result,cv2.COLOR_BGR2RGB))
plt.figure(figsize=(10,10)) #设置窗口大小
plt.imshow(result)
plt.show()
Fix size testing.
training chunk_sizes: [8]
The output will be saved to ./output/exp/ctdet/default
Creating model...
loaded ./output/exp/ctdet/default/model/model_last.pth, epoch 1
load model success
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