展开代码 defparse_model(d, ch):# model_dict, input_channels(3)logger.info('\n%3s%18s%3s%10s %-40s%-30s'% ('','from','n','params','module','arguments')) anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] na = (len(anchors[0]...
9. 接着需要在yolov5的读取模型配置文件的代码(models/yolo.py的parse_model函数)进行修改,使得能够调用到上面的模块,只需修改下面这部分代码。 n = max(round(n * gd), 1) if n > 1 else n # depth gain if m in [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, B...
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist self.names = [str(i) for i in range(self.yaml['nc'])] # default names # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) # Build strides, anchors m = self.model[-...
opt=parser.parse_args() opt.img_size*= 2iflen(opt.img_size) == 1else1#expandprint(opt) set_logging() t=time.time()#Load PyTorch modeldevice =select_device(opt.device) model= attempt_load(opt.weights, map_location=device)#load FP32 modellabels =model.names#Checksgs = int(max(model...
将上面的模块封装好后,就可以在yolo.py的parse_model函数中增加模块的参数配置逻辑了,代码如下: if m in [nn.Conv2d, Conv, RobustConv, RobustConv2, E_ELAN, E_ELAN_H, DWConv, GhostConv, RepConv, RepConv_OREPA, DownC, SPP, SPPF, SPPCSPC, GhostSPPCSPC, MixConv2d, Focus, Stem, GhostSte...
2.网络的架构基于NAS实现的MnasNet(效果比MobileNetV2好),由NAS搜索获取参数 3.引入MobileNetV1的深度可分离卷积 4.引入MobileNetV2的具有线性瓶颈的倒残差结构 5.引入基于squeeze and excitation结构的轻量级注意力模型(SE) 6.使用了一种新的激活函数h-swish(x) ...
将上面的模块封装好后,就可以在yolo.py的parse_model函数中增加模块的参数配置逻辑了,代码如下: 代码语言:javascript 复制 if m in [nn.Conv2d, Conv, RobustConv, RobustConv2, E_ELAN, E_ELAN_H, DWConv, GhostConv, RepConv, RepConv_OREPA, ...
opt = parser.parse_args() opt.img_size *=2iflen(opt.img_size) ==1else1# expandopt.dynamic = opt.dynamicandnotopt.end2end opt.dynamic =Falseifopt.dynamic_batchelseopt.dynamic print(opt) set_logging() t = time.time()# Load PyTorch modeldevice = select_device(opt.device) ...
parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch') parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used') opt = parser.parse_args() # Set DDP variables opt...
parser.add_argument('--simplify',action='store_true',default=True,help='simplify onnx model')#在导出onnx模型的时候,是否做模型剪枝操作,建议加上,如果不加,opencv去调用onnx模型可能会出错parser.add_argument('--include-nms',action='store_true',help='export end2end onnx')opt=parser.parse_args...