实践案例丨 CenterNet-Hourglass 论文复现

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 5.6s2021-06-25 17:50:16 (261 MB/s) - ‘CenterNet.zip’ saved [1529663572/1529663572]0

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_function
root_path = './CenterNet/'os.chdir(root_path)os.system('pip install pycocotools')
import _init_pathsimport torchimport torch.utils.datafrom opts import optsfrom models.model import create_model, load_model, save_modelfrom models.data_parallel import DataParallelfrom logger import Loggerfrom datasets.dataset_factory import get_datasetfrom trains.train_factory import train_factoryfrom 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-3.20.9.1NMS not imported! If you need it, do 
 cd $CenterNet_ROOT/src/lib/external 
 make

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()

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!

# -*- coding: utf-8 -*-# TODO 添加模型运行需要导入的模块import osimport torchimport numpy as npfrom PIL import Imagefrom io import BytesIOfrom collections import OrderedDictimport cv2import syssys.path.insert(0, './lib')
from opts import optsfrom coco_classes import coco_class_mapfrom 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

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