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]) //2)...
参数配置和配置文件 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, Foc...
LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}') self.yaml['anchors'] = round(anchors) # override yaml value # 解析模型, self.model是解析后的模型 self.save是每一层与之相连的层 self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist ...
logger.info('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc)) self.yaml['nc'] = nc # override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist self.names = [str(i) for i in range(self.yaml['nc'])] # de...
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...
help="remove detections with lower confidence")returnparser.parse_args() 将上述红色标注的配置文件换成自己的就行了,最后运行会出现如下: ... 158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF 159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF 1...
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, ...
if __name__ == '__main__':parser = argparse.ArgumentParser()parser.add_argument('--weights', type=str, default='yolov7.pt', help='initial weights path')parser.add_argument('--cfg', type=str, default='cfg/training/yolov7.yaml', help='model.yaml path')parser.add_argument('--dat...
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...
parse(in_file) root = tree.getroot() # 遍历文件的所有标签 for obj in root.iter('object'): name = obj.find('name').text if (name in dict.keys()): dict[name] += 1 # 如果标签不是第一次出现,则+1 else: dict[name] = 1 # 如果标签是第一次出现,则将该标签名对应的value初始化...