AIlib2/DMPR.py

from DMPRUtils.DMPR_process import DMPR_process
import tensorrt as trt
import sys,os
#from DMPRUtils.model.detector import DirectionalPointDetector
from DMPRUtils.yolo_net import Model
import torch

class DMPRModel(object):
    def __init__(self, weights=None,
                par={'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.3, 'dmprimg_size':640}
                ):
    
        self.par = par
        self.device = 'cuda:0'   
        self.half =True   
        
        if weights.endswith('.engine'):
            self.infer_type ='trt'
        elif  weights.endswith('.pth') or weights.endswith('.pt') :
            self.infer_type ='pth'
        else:
            print('#########ERROR:',weights,': no registered inference type, exit')
            sys.exit(0)
            
        if self.infer_type=='trt':
            logger = trt.Logger(trt.Logger.ERROR)
            with open(weights, "rb") as f, trt.Runtime(logger) as runtime:
                self.model=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
            print('############load seg model trt success: ',weights)
        elif self.infer_type=='pth':
            #self.model = DirectionalPointDetector(3, self.par['depth_factor'], self.par['NUM_FEATURE_MAP_CHANNEL']).to(self.device)
            confUrl = os.path.join( os.path.dirname(__file__),'DMPRUtils','config','yolov5s.yaml' )
            self.model = Model(confUrl, ch=3).to(self.device)
            self.model.load_state_dict(torch.load(weights))
            print('#######load pt model:%s success '%(weights))
        self.par['modelType']=self.infer_type
   
    def eval(self,image):	
        det,timeInfos = DMPR_process(image, self.model, self.device, self.par)
        det = det.cpu().detach().numpy()
        return det,timeInfos
        
    def get_ms(self,t1,t0):
        return (t1-t0)*1000.0