AIlib2/segutils/segmodel_trt.py

import torch
import argparse
import sys,os
sys.path.extend(['segutils'])
from core.models.bisenet import BiSeNet
from model_stages import BiSeNet_STDC
from torchvision import transforms
import cv2,glob
import numpy as np
import matplotlib.pyplot as plt           
import time
from pathlib import Path
from trtUtils import TRTModule,segTrtForward,segtrtEval,segPreProcess_image,get_ms
from concurrent.futures import ThreadPoolExecutor
import tensorrt as trt
from copy import deepcopy
import onnx
import numpy as np
import onnxruntime as ort
import cv2
#import pycuda.driver as cuda
class SegModel_BiSeNet(object):
    def __init__(self, nclass=2,weights=None,modelsize=512,device='cuda:0'):
        #self.args = args       
        self.model = BiSeNet(nclass)
        checkpoint = torch.load(weights)
        if isinstance(modelsize,list) or isinstance(modelsize,tuple):
            self.modelsize = modelsize
        else:    self.modelsize = (modelsize,modelsize)
        self.model.load_state_dict(checkpoint['model'])
        self.device = device
        self.model= self.model.to(self.device)
        '''self.composed_transforms = transforms.Compose([
            
            transforms.Normalize(mean=(0.335, 0.358, 0.332), std=(0.141, 0.138, 0.143)),
            transforms.ToTensor()])   '''
        self.mean = (0.335, 0.358, 0.332)
        self.std =   (0.141, 0.138, 0.143)  
    def eval(self,image):	
        time0 = time.time()
        imageH,imageW,imageC = image.shape        
        image = self.preprocess_image(image)
        time1 = time.time()
        self.model.eval()
        image  = image.to(self.device)
        with torch.no_grad():
            output = self.model(image)

        time2 = time.time()    
        pred = output.data.cpu().numpy()
        pred = np.argmax(pred, axis=1)[0]#得到每行
        time3 = time.time()        
        pred = cv2.resize(pred.astype(np.uint8),(imageW,imageH))
        time4 = time.time()
        outstr= 'pre-precess:%.1f ,infer:%.1f ,post-precess:%.1f ,post-resize:%.1f, total:%.1f \n '%( self.get_ms(time1,time0),self.get_ms(time2,time1),self.get_ms(time3,time2),self.get_ms(time4,time3),self.get_ms(time4,time0) )

        #print('pre-precess:%.1f ,infer:%.1f ,post-precess:%.1f ,post-resize:%.1f, total:%.1f '%( self.get_ms(time1,time0),self.get_ms(time2,time1),self.get_ms(time3,time2),self.get_ms(time4,time3),self.get_ms(time4,time0) ))
        return pred,outstr 
    def get_ms(self,t1,t0):
        return (t1-t0)*1000.0            
    def preprocess_image(self,image):
        
        time0 = time.time()
        image = cv2.resize(image,self.modelsize)
        time0 = time.time()
        image = image.astype(np.float32)        
        image /= 255.0

        image[:,:,0] -=self.mean[0]
        image[:,:,1] -=self.mean[1]
        image[:,:,2] -=self.mean[2]

        image[:,:,0] /= self.std[0]
        image[:,:,1] /= self.std[1]
        image[:,:,2] /= self.std[2]
        image = cv2.cvtColor( image,cv2.COLOR_RGB2BGR)
        #image -= self.mean
        #image /= self.std
        image = np.transpose(image, ( 2, 0, 1))
        
        image = torch.from_numpy(image).float()
        image = image.unsqueeze(0)
        
        
        return image
class SegModel_STDC(object):
    def __init__(self, nclass=2,weights=None,modelsize=512,device='cuda:0',modelSize=(360,640)):
        #self.args = args       
        self.model = BiSeNet_STDC(backbone='STDCNet813', n_classes=nclass,
                                use_boundary_2=False, use_boundary_4=False,
                                use_boundary_8=True, use_boundary_16=False,
                                use_conv_last=False,modelSize=modelSize)
        self.device = device      
        self.model.load_state_dict(torch.load(weights, map_location=torch.device(self.device)  ))
        self.model= self.model.to(self.device)
        self.mean = (0.485, 0.456, 0.406)
        self.std = (0.229, 0.224, 0.225)
        self.modelSize = modelSize
        
    def eval(self,image):	
        time0 = time.time()
        imageH, imageW, _ = image.shape
  
        image = self.RB_convert(image)

        img = self.preprocess_image(image)
        
        if self.device != 'cpu':
            imgs = img.to(self.device)
        else:imgs=img    
        time1 = time.time()
        self.model.eval()
    
        with torch.no_grad():
            output = self.model(imgs)
            
        time2 = time.time()  
        pred = output.data.cpu().numpy()
        pred = np.argmax(pred, axis=1)[0]#得到每行
        time3 = time.time() 
        pred = cv2.resize(pred.astype(np.uint8),(imageW,imageH))
        time4 = time.time()
        outstr= 'pre-precess:%.1f ,infer:%.1f ,post-cpu-argmax:%.1f ,post-resize:%.1f, total:%.1f \n '%( self.get_ms(time1,time0),self.get_ms(time2,time1),self.get_ms(time3,time2),self.get_ms(time4,time3),self.get_ms(time4,time0) )
        
        return pred,outstr 
    def get_ms(self,t1,t0):
        return (t1-t0)*1000.0            
    def preprocess_image(self,image):
        
        image = cv2.resize(image, (self.modelSize[1],self.modelSize[0] ), interpolation=cv2.INTER_LINEAR)
        image = image.astype(np.float32)
        image /= 255.0

        image[:, :, 0] -= self.mean[0]
        image[:, :, 1] -= self.mean[1]
        image[:, :, 2] -= self.mean[2]

        image[:, :, 0] /= self.std[0]
        image[:, :, 1] /= self.std[1]
        image[:, :, 2] /= self.std[2]

        image = np.transpose(image, (2, 0, 1))
        image = torch.from_numpy(image).float()
        image = image.unsqueeze(0)

        return image
    def RB_convert(self,image):
        image_c = image.copy()
        image_c[:,:,0] = image[:,:,2]
        image_c[:,:,2] = image[:,:,0]
        return image_c    

 
def get_largest_contours(contours):
    areas = [cv2.contourArea(x) for x in contours]
    max_area = max(areas)
    max_id = areas.index(max_area)

    return max_id
    
def infer_usage(par):   
    #par={'modelSize':(inputShape[3],inputShape[2]),'mean':(0.485, 0.456, 0.406),'std':(0.229, 0.224, 0.225),'RGB_convert_first':True,
    #      'weights':trtFile,'device':device,'max_threads':1,
    #      'image_dir':'../../AIdemo2/images/trafficAccident/','out_dir' :'results'}
          

    segmodel = par['segmodel']

    image_urls=glob.glob('%s/*'%(par['image_dir']))
    out_dir =par['out_dir']
    os.makedirs(out_dir,exist_ok=True)
    for im,image_url in enumerate(image_urls[0:1]):
        #image_url = '/home/thsw2/WJ/data/THexit/val/images/54(199).JPG'
        image_array0 = cv2.imread(image_url)
        H,W,C = image_array0.shape
        time_1=time.time()        
        pred,outstr = segmodel.eval(image_array0 )

  
        binary0 = pred.copy()

        
        time0 = time.time()
        contours, hierarchy = cv2.findContours(binary0,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
        max_id = -1
        time1 = time.time()
        time2 = time.time()
        
        cv2.drawContours(image_array0,contours,max_id,(0,255,255),3)
        time3 = time.time()
        out_url='%s/%s'%(out_dir,os.path.basename(image_url))
        ret = cv2.imwrite(out_url,image_array0)
        cv2.imwrite(out_url.replace('.','_mask.'),(pred*50).astype(np.uint8))
        time4 = time.time()
        
        print('image:%d,%s ,%d*%d,eval:%.1f ms, %s,findcontours:%.1f ms,draw:%.1f total:%.1f'%(im,os.path.basename(image_url),H,W,get_ms(time0,time_1),outstr,get_ms(time1,time0), get_ms(time3,time2),get_ms(time3,time_1)) )
        
def colorstr(*input):
    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
    *args, string = input if len(input) > 1 else ('blue', 'bold', input[0])  # color arguments, string
    colors = {'black': '\033[30m',  # basic colors
              'red': '\033[31m',
              'green': '\033[32m',
              'yellow': '\033[33m',
              'blue': '\033[34m',
              'magenta': '\033[35m',
              'cyan': '\033[36m',
              'white': '\033[37m',
              'bright_black': '\033[90m',  # bright colors
              'bright_red': '\033[91m',
              'bright_green': '\033[92m',
              'bright_yellow': '\033[93m',
              'bright_blue': '\033[94m',
              'bright_magenta': '\033[95m',
              'bright_cyan': '\033[96m',
              'bright_white': '\033[97m',
              'end': '\033[0m',  # misc
              'bold': '\033[1m',
              'underline': '\033[4m'}
    return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
def file_size(path):
    # Return file/dir size (MB)
    path = Path(path)
    if path.is_file():
        return path.stat().st_size / 1E6
    elif path.is_dir():
        return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / 1E6
    else:
        return 0.0

        
def toONNX(seg_model,onnxFile,inputShape=(1,3,360,640),device=torch.device('cuda:0'),dynamic=False ):
    
    import onnx

    im = torch.rand(inputShape).to(device)
    seg_model.eval()
    out=seg_model(im)
    print('###test model infer example over ####')
    train=False
    dynamic = False
    opset=11
    print('####begin to export to onnx')
    
    torch.onnx.export(seg_model, im,onnxFile, opset_version=opset,
                      training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
                      do_constant_folding=not train,
                      input_names=['images'],
                      output_names=['output'],
                      #dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'},  # shape(1,3,640,640)
                      #              'output': {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
                      #              } if dynamic else None
                      dynamic_axes={
                          'images': {0: 'batch_size', 2: 'in_width', 3: 'int_height'},
                          'output': {0: 'batch_size', 2: 'out_width', 3: 'out_height'}} if dynamic else None

                                    )
    '''                                
    input_name='images'  
    output_name='output'    
    torch.onnx.export(seg_model, 
                      im, 
                      onnxFile,
                      opset_version=11,
                      input_names=[input_name],
                      output_names=[output_name],
                      dynamic_axes={
                          input_name: {0: 'batch_size', 2: 'in_width', 3: 'int_height'},
                          output_name: {0: 'batch_size', 2: 'out_width', 3: 'out_height'}}
                      )

    '''
                                
                                    
    print('output onnx file:',onnxFile)                                
def ONNXtoTrt(onnxFile,trtFile):
    import tensorrt as trt
    #onnx = Path('../weights/BiSeNet/checkpoint.onnx')
    #onnxFile = Path('../weights/STDC/model_maxmIOU75_1720_0.946_360640.onnx')
    time0=time.time()
    half=True;verbose=True;workspace=4;prefix=colorstr('TensorRT:')
    #f = onnx.with_suffix('.engine')  # TensorRT engine file
    f=trtFile
    logger = trt.Logger(trt.Logger.INFO)
    if verbose:
        logger.min_severity = trt.Logger.Severity.VERBOSE

    builder = trt.Builder(logger)
    config = builder.create_builder_config()
    config.max_workspace_size = workspace * 1 << 30

    flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
    network = builder.create_network(flag)
    parser = trt.OnnxParser(network, logger)
    if not parser.parse_from_file(str(onnxFile)):
        raise RuntimeError(f'failed to load ONNX file: {onnx}')

    inputs = [network.get_input(i) for i in range(network.num_inputs)]
    outputs = [network.get_output(i) for i in range(network.num_outputs)]
    print(f'{prefix} Network Description:')
    for inp in inputs:
        print(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
    for out in outputs:
        print(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')

    half &= builder.platform_has_fast_fp16
    print(f'{prefix} building FP{16 if half else 32} engine in {f}')
    if half:
        config.set_flag(trt.BuilderFlag.FP16)
    with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
        t.write(engine.serialize())
    print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
    time1=time.time()
    print('output trtfile from ONNX, time:%.4f s ,'%(time1-time0),trtFile)
def ONNX_eval(par):

      
    model_path = par['weights'];
    modelSize=par['modelSize']
    mean = par['mean']
    std = par['std']
    image_urls=glob.glob('%s/*'%(par['image_dir'] ))
    out_dir = par['out_dir']
     
    # 验证模型合法性
    onnx_model = onnx.load(model_path)
    onnx.checker.check_model(onnx_model)

    # 设置模型session以及输入信息
    sess = ort.InferenceSession(str(model_path),providers= ort.get_available_providers())
    print('len():',len( sess.get_inputs() ))
    input_name1 = sess.get_inputs()[0].name

    
    half = False;device = 'cuda:0'
    os.makedirs(out_dir,exist_ok=True)

    for im,image_url in enumerate(image_urls[0:1]):       
        image_array0 = cv2.imread(image_url)   
        #img=segPreProcess_image(image_array0).to(device)
        img=segPreProcess_image(image_array0,modelSize=modelSize,mean=mean,std=std,numpy=True,RGB_convert_first=par['RGB_convert_first']) 
        #img = cv2.resize(img,(512,512)).transpose(2,0,1)
        img = np.array(img)[np.newaxis, :, :, :].astype(np.float32)   
        H,W,C = image_array0.shape
        time_1=time.time()        
        #pred,outstr = segmodel.eval(image_array0 )
        print('###line343:',img.shape, os.path.basename(image_url))
        print('###line343:img[0,0,10:12,10:12]  ',img[0,0,10:12,10:12])
        output = sess.run(None, {input_name1: img})
        pred =output[0]
        
        #pred = pred.data.cpu().numpy()
        pred = np.argmax(pred, axis=1)[0]#得到每行
        pred = cv2.resize(pred.astype(np.uint8),(W,H))
        print('###line362:',np.max(pred))
        outstr='###---###'
        
        binary0 = pred.copy()

        
        time0 = time.time()
        contours, hierarchy = cv2.findContours(binary0,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
        max_id = -1

        time1 = time.time()
        
  
        time2 = time.time()
        
        #cv2.drawContours(image_array0,contours,max_id,(0,255,255),3)
        cv2.drawContours(image_array0,contours,-1,(0,255,255),3)
        time3 = time.time()
        out_url='%s/%s'%(out_dir,os.path.basename(image_url))
        ret = cv2.imwrite(out_url,image_array0)
        ret = cv2.imwrite(out_url.replace('.jpg','_mask.jpg').replace('.png','_mask.png' ),(pred*50).astype(np.uint8))
        time4 = time.time()
        
        print('image:%d,%s ,%d*%d,eval:%.1f ms, %s,findcontours:%.1f ms,draw:%.1f total:%.1f'%(im,os.path.basename(image_url),H,W,get_ms(time0,time_1),outstr,get_ms(time1,time0), get_ms(time3,time2),get_ms(time3,time_1)) )
        print('outimage:',out_url)
        
        
    #print(output)
 
class SegModel_STDC_trt(object):
    def __init__(self,weights=None,modelsize=512,std=(0.229, 0.224, 0.225),mean=(0.485, 0.456, 0.406),device='cuda:0'):

        logger = trt.Logger(trt.Logger.INFO)
        with open(weights, "rb") as f, trt.Runtime(logger) as runtime:
            engine=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
        self.model = TRTModule(engine, ["images"], ["output"])
        self.mean = mean
        self.std = std
        self.device = device
        self.modelsize = modelsize
    
        
    def eval(self,image): 
        time0=time.time()
        H,W,C=image.shape
        img_input=self.segPreProcess_image(image)
        time1=time.time()
        pred=self.model(img_input)
        time2=time.time()
        pred=torch.argmax(pred,dim=1).cpu().numpy()[0]
        #pred = np.argmax(pred.cpu().numpy(), axis=1)[0]#得到每行
        time3 = time.time() 
        pred = cv2.resize(pred.astype(np.uint8),(W,H))
        time4 = time.time()        
        outstr= 'pre-precess:%.1f ,infer:%.1f ,post-cpu-argmax:%.1f ,post-resize:%.1f, total:%.1f \n '%( self.get_ms(time1,time0),self.get_ms(time2,time1),self.get_ms(time3,time2),self.get_ms(time4,time3),self.get_ms(time4,time0) )

        return pred,outstr
    def segPreProcess_image(self,image):

        image = cv2.resize(image,self.modelsize)
        image = cv2.cvtColor( image,cv2.COLOR_RGB2BGR)

        image = image.astype(np.float32)        
        image /= 255.0
        image[:,:,0] -=self.mean[0]
        image[:,:,1] -=self.mean[1]
        image[:,:,2] -=self.mean[2]

        image[:,:,0] /= self.std[0]
        image[:,:,1] /= self.std[1]
        image[:,:,2] /= self.std[2]
        image = np.transpose(image, ( 2, 0, 1))
        
        image = torch.from_numpy(image).float()
        image = image.unsqueeze(0)
        return image.to(self.device)
    def get_ms(self,t1,t0):
        return (t1-t0)*1000.0     
        

    
def EngineInfer_onePic_thread(pars_thread):
 
    engine,image_array0,out_dir,image_url,im ,par= pars_thread[0:6]
    out_url='%s/%s'%(out_dir,os.path.basename(image_url))

    H,W,C = image_array0.shape
    time0=time.time()   
   
    time1=time.time() 
    # 运行模型
    
    
    #pred,segInfoStr=segtrtEval(engine,image_array0,par={'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'numpy':False, 'RGB_convert_first':True})
    pred,segInfoStr=segtrtEval(engine,image_array0,par=par)
    cv2.imwrite(out_url.replace('.','_mask.'),(pred*50).astype(np.uint8))
    
    pred = 1 - pred
    time2=time.time() 

    outstr='###---###'
    binary0 = pred.copy()  
    time3 = time.time()
    
    contours, hierarchy = cv2.findContours(binary0,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    max_id = -1
    #if len(contours)>0:
    #    max_id = get_largest_contours(contours)
    #    binary0[:,:] = 0
    #    cv2.fillPoly(binary0, [contours[max_id][:,0,:]], 1) 
    time4 = time.time()
    
    cv2.drawContours(image_array0,contours,max_id,(0,255,255),3)
    time5 = time.time()
    
    ret = cv2.imwrite(out_url,image_array0)
    time6 = time.time()

    print('image:%d,%s ,%d*%d, %s,,findcontours:%.1f ms,draw:%.1f total:%.1f'%(im,os.path.basename(image_url),H,W,segInfoStr, get_ms(time4,time3),get_ms(time5,time4),get_ms(time5,time0) ))
        
        
    return 'success'
    
    
def EngineInfer(par):
    
    modelSize=par['modelSize'];mean = par['mean'] ;std = par['std'] ;RGB_convert_first=par['RGB_convert_first'];device=par['device']
    weights=par['weights']; image_dir=par['image_dir']
    max_threads=par['max_threads'];par['numpy']=False
    image_urls=glob.glob('%s/*'%(image_dir))
    out_dir =par['out_dir']
    
    os.makedirs(out_dir,exist_ok=True)
       
    #trt_model = SegModel_STDC_trt(weights=weights,modelsize=modelSize,std=std,mean=mean,device=device)
    logger = trt.Logger(trt.Logger.ERROR)
    with open(weights, "rb") as f, trt.Runtime(logger) as runtime:
        engine=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
        print('#####load TRT file:',weights,'success #####')

    pars_thread=[]
    pars_threads=[]
    for im,image_url in enumerate(image_urls[0:]): 
        image_array0 = cv2.imread(image_url)   
        pars_thread=[engine,image_array0,out_dir,image_url,im,par]
        pars_threads.append(pars_thread)
        #EngineInfer_onePic_thread(pars_thread)
    t1=time.time()   
    if  max_threads==1:
        for  i in range(len(pars_threads[0:])):
            EngineInfer_onePic_thread(pars_threads[i])
           
            '''
            pred,segInfoStr=segtrtEval(pars_threads[i][0],pars_threads[i][1],par)
            bname=os.path.basename( pars_threads[i][3] )
            outurl= os.path.join( out_dir , bname.replace( '.png','_mask.png').replace('.jpg','._mask.jpg') )
            ret=cv2.imwrite( outurl,(pred*50).astype(np.uint8))            
            print(ret,outurl)'''
            
    else:    
        with ThreadPoolExecutor(max_workers=max_threads) as t:
                for result in t.map(EngineInfer_onePic_thread, pars_threads):
                    tt=result
                
    t2=time.time()
    print('All %d images time:%.1f ms, each:%.1f ms , with %d threads'%(len(image_urls),(t2-t1)*1000, (t2-t1)*1000.0/len(image_urls), max_threads) )   
        
     
                                    
if __name__=='__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', type=str, default='stdc_360X640.pth', help='model path(s)')
    parser.add_argument('--nclass', type=int, default=2, help='segmodel nclass')
    parser.add_argument('--mWidth', type=int, default=640, help='segmodel mWdith')
    parser.add_argument('--mHeight', type=int, default=360, help='segmodel mHeight')
    opt = parser.parse_args()
    print( opt.weights  )
    #pthFile = Path('../../../yolov5TRT/weights/river/stdc_360X640.pth')
    pthFile = Path(opt.weights)
    onnxFile = str(pthFile.with_suffix('.onnx')).replace('360X640', '%dX%d'%( opt.mWidth,opt.mHeight ))
    trtFile =  onnxFile.replace('.onnx','.engine' )

    nclass = opt.nclass; device=torch.device('cuda:0');
    
    '''###BiSeNet
    weights = '../weights/BiSeNet/checkpoint.pth';;inputShape =(1, 3, 512,512)
    segmodel = SegModel_BiSeNet(nclass=nclass,weights=weights)
    seg_model=segmodel.model    
    '''
    
    ##STDC net    
    weights = pthFile
    inputShape =(1, 3, opt.mHeight,opt.mWidth)#(bs,channels,height,width)
    #inputShape =(1, 3, 360,640)#(bs,channels,height,width)
    segmodel = SegModel_STDC(nclass=nclass,weights=weights,modelSize=(inputShape[2],inputShape[3]));
    
   
    seg_model=segmodel.model

    par={'modelSize':(inputShape[3],inputShape[2]),'mean':(0.485, 0.456, 0.406),'std':(0.229, 0.224, 0.225),'RGB_convert_first':True,
          'weights':trtFile,'device':device,'max_threads':1,'predResize':True,
          'image_dir':'../../AIdemo2/images/trafficAccident/','out_dir' :'results'}
          
    par_onnx =deepcopy( par)
    par_onnx['weights']=onnxFile
    par_pth =deepcopy( par);par_pth['segmodel']=segmodel;
    #infer_usage(par_pth)

    #toONNX(seg_model,onnxFile,inputShape=inputShape,device=device,dynamic=True)  
    ONNXtoTrt(onnxFile,trtFile)

    #EngineInfer(par)
    
    
    #ONNX_eval(par_onnx)