def info(self, verbose=False, img_size=640): # print model information model_info(self, verbose, img_size) def _apply(self, fn): # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers self = super()._apply(fn) m = self.model[-1]...
def model_info(model, verbose=False): # Plots a line-by-line description of a PyTorch model n_p = sum(x.numel() for x in model.parameters()) # number parameters n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients if verbose: print('%5s %...
time() # 获取当前时间 use_time = t2 - t1 # 计算预测所用的时间 print("推理时间: %.2f" % use_time) # 打印预测所用的时间 det = pred[0] # 获取预测结果 # 如果有检测信息则进入 if det is not None and len(det): det_info = model.postprocess(pred) # 对预测结果进行后处理 for info...
= self.yaml['nc']: LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") self.yaml['nc'] = nc # override yaml value if anchors: # anchors 先验框的配置 LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}') self.yaml['anchors']...
defmy_export_onnx(model,im,file,opset,train,dynamic,simplify,prefix=colorstr('ONNX:')):print(...
img=model.predict(pre_img)# 进行目标检测t2=time.time()# 记录结束时间print("推理时间:%.2f"%(t2-t1))# 打印推理时间det=pred[0]# 获取检测结果ifdetisnotNoneandlen(det):det_info=model.postprocess(pred)# 后处理forinfoindet_info:# 遍历检测到的每个对象name,bbox,conf,cls_id=info['class_...
model_infofrom thop import profilemodel = load_model()model_parms(model)# model_info(model, verbose=True)result = sparsity(model)print("prune before:{}".format(result))prune(model)result = sparsity(model)print("prune after:{}".format(result))model_parms(model)# model_info(model, verbose=...
left,top,right,bottom=float(info[4]),float(info[5]),float(info[6]),float(info[7])#四个坐标 #将四个坐标转换为x_center,y_center,width,height的格式 width=right-left height=bottom-top x_center=left+width/2 y_center=top+height/2 ...
pred,superimposed_img=model.predict(pre_img)# 使用模型进行预测 t2=time.time()# 获取当前时间 use_time=t2-t1 # 计算预测所用的时间print("推理时间: %.2f"%use_time)# 打印预测所用的时间 det=pred[0]# 获取预测结果 # 如果有检测信息则进入ifdet is not None andlen(det):det_info=model.post...
# 如果有检测信息则进入ifdet is not None andlen(det):det_info=model.postprocess(pred)# 对预测结果进行后处理forinfoindet_info:# 遍历检测信息 name,bbox,conf,cls_id=info['class_name'],info['bbox'],info['score'],info['class_id']# 获取类名、边界框、置信度和类别IDlabel='%s%.0f%%'...