YOLOv8打印模型结构配置信息并查看网络模型详细参数:参数量、计算量(GFLOPS)

简介: YOLOv8打印模型结构配置信息并查看网络模型详细参数:参数量、计算量(GFLOPS)

前言

本文主要介绍如何打印并且查看YOLOv8网络模型的网络结构配置信息、每一层结构详细信息、以及参数量、计算量等模型相关信息。 该方法同样适用于改进后的模型网络结构信息及相关参数查看。可用于不同模型进行参数量、计算量等对比使用。

查看配置文件结构信息

在每次进行YOLOv8模型训练前,都会打印相应的模型结构信息,如上图。但是如何自己能够直接打印出上述网络结构配置信息呢?,博主通过查看源码发现,信息是在源码DetectionModel类中,打印出来的。因此我们直接使用该类,传入我们自己的模型配置文件,运行该类即可,代码如下:

class DetectionModel(BaseModel):
    """YOLOv8 detection model."""
    def __init__(self, cfg="yolov8n.yaml", ch=3, nc=None, verbose=True):  # model, input channels, number of classes
        """Initialize the YOLOv8 detection model with the given config and parameters."""
        super().__init__()
        self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg)  # cfg dict
        # Define model
        ch = self.yaml["ch"] = self.yaml.get("ch", ch)  # input channels
        if nc and nc != self.yaml["nc"]:
            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
            self.yaml["nc"] = nc  # override YAML value
        self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose)  # model, savelist
        self.names = {i: f"{i}" for i in range(self.yaml["nc"])}  # default names dict
        self.inplace = self.yaml.get("inplace", True)
        # Build strides
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect):  # includes all Detect subclasses like Segment, Pose, OBB, WorldDetect
            s = 256  # 2x min stride
            m.inplace = self.inplace
            forward = lambda x: self.forward(x)[0] if isinstance(m, (Segment, Pose, OBB)) else self.forward(x)
            m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forward
            self.stride = m.stride
            m.bias_init()  # only run once
        else:
            self.stride = torch.Tensor([32])  # default stride for i.e. RTDETR
        # Init weights, biases
        initialize_weights(self)
        if verbose:
            self.info()
            LOGGER.info("")
# 模型网络结构配置文件路径
yaml_path = 'ultralytics/cfg/models/v8/yolov8n.yaml'
# 改进的模型结构路径
# yaml_path = 'ultralytics/cfg/models/v8/yolov8n-CBAM.yaml'  
# 传入模型网络结构配置文件cfg, nc为模型检测类别数
DetectionModel(cfg=yaml_path,nc=5)

运行代码后,打印结果如下:

打印结果说明:

可以看到模型配置文件一共有23行,params为每一层的参数量大小,module为每一层的结构名称,arguments为每一层结构需要传入的参数。最后一行summary为总的信息参数,模型一共有225层,参参数量(parameters)为:3157200,计算量GFLOPs为:8.9.

查看详细的网络结构

上面只是打印出了网络配置文件每一层相关的信息,如果我们想看更加细致的每一步信息,可以直接使用model.info()来进行查看,代码如下:

加载训练好的模型或者网络结构配置文件

from ultralytics import YOLO
# 加载训练好的模型或者网络结构配置文件
model = YOLO('best.pt')
# model = YOLO('ultralytics/cfg/models/v8/yolov8n.yaml')

打印模型参数信息:

# 打印模型参数信息
print(model.info())

结果如下:

打印模型每一层结构信息:

在上面代码中加入detailed参数即可。

print(model.info(detailed=True))

打印信息如下:

layer                                     name  gradient   parameters                shape         mu      sigma
    0                      model.0.conv.weight     False          432        [16, 3, 3, 3]   -0.00404      0.153 torch.float32
    1                        model.0.bn.weight     False           16                 [16]       2.97       1.87 torch.float32
    2                          model.0.bn.bias     False           16                 [16]      0.244       4.17 torch.float32
    3                      model.1.conv.weight     False         4608       [32, 16, 3, 3]  -3.74e-05     0.0646 torch.float32
    4                        model.1.bn.weight     False           32                 [32]       5.01       1.12 torch.float32
    5                          model.1.bn.bias     False           32                 [32]      0.936       1.51 torch.float32
    6                  model.2.cv1.conv.weight     False         1024       [32, 32, 1, 1]    -0.0103     0.0918 torch.float32
    7                    model.2.cv1.bn.weight     False           32                 [32]       2.21       1.39 torch.float32
    8                      model.2.cv1.bn.bias     False           32                 [32]      0.803       1.39 torch.float32
    9                  model.2.cv2.conv.weight     False         1536       [32, 48, 1, 1]   -0.00279     0.0831 torch.float32
   10                    model.2.cv2.bn.weight     False           32                 [32]       1.21      0.576 torch.float32
   11                      model.2.cv2.bn.bias     False           32                 [32]      0.542       1.13 torch.float32
   12              model.2.m.0.cv1.conv.weight     False         2304       [16, 16, 3, 3]  -0.000871     0.0574 torch.float32
   13                model.2.m.0.cv1.bn.weight     False           16                 [16]       2.36      0.713 torch.float32
   14                  model.2.m.0.cv1.bn.bias     False           16                 [16]       1.02       1.71 torch.float32
   15              model.2.m.0.cv2.conv.weight     False         2304       [16, 16, 3, 3]   -0.00071       0.05 torch.float32
   16                model.2.m.0.cv2.bn.weight     False           16                 [16]       2.11      0.519 torch.float32
   17                  model.2.m.0.cv2.bn.bias     False           16                 [16]       0.92        1.9 torch.float32
   18                      model.3.conv.weight     False        18432       [64, 32, 3, 3]   -0.00137     0.0347 torch.float32
   19                        model.3.bn.weight     False           64                 [64]      0.818      0.206 torch.float32
   20                          model.3.bn.bias     False           64                 [64]      0.249      0.936 torch.float32
   21                  model.4.cv1.conv.weight     False         4096       [64, 64, 1, 1]   -0.00235     0.0553 torch.float32
   22                    model.4.cv1.bn.weight     False           64                 [64]      0.824      0.359 torch.float32
   23                      model.4.cv1.bn.bias     False           64                 [64]       0.26      0.779 torch.float32
   24                  model.4.cv2.conv.weight     False         8192      [64, 128, 1, 1]    -0.0019     0.0474 torch.float32
   25                    model.4.cv2.bn.weight     False           64                 [64]      0.718       0.21 torch.float32
   26                      model.4.cv2.bn.bias     False           64                 [64]      -0.05      0.754 torch.float32
   27              model.4.m.0.cv1.conv.weight     False         9216       [32, 32, 3, 3]   -0.00178     0.0368 torch.float32
   28                model.4.m.0.cv1.bn.weight     False           32                 [32]      0.751      0.154 torch.float32
   29                  model.4.m.0.cv1.bn.bias     False           32                 [32]     -0.263      0.629 torch.float32
   30              model.4.m.0.cv2.conv.weight     False         9216       [32, 32, 3, 3]   -0.00156     0.0346 torch.float32
   31                model.4.m.0.cv2.bn.weight     False           32                 [32]      0.769      0.203 torch.float32
   32                  model.4.m.0.cv2.bn.bias     False           32                 [32]      0.183      0.694 torch.float32
   33              model.4.m.1.cv1.conv.weight     False         9216       [32, 32, 3, 3]   -0.00209     0.0339 torch.float32
   34                model.4.m.1.cv1.bn.weight     False           32                 [32]      0.686      0.106 torch.float32
   35                  model.4.m.1.cv1.bn.bias     False           32                 [32]      -0.82      0.469 torch.float32
   36              model.4.m.1.cv2.conv.weight     False         9216       [32, 32, 3, 3]   -0.00253     0.0309 torch.float32
   37                model.4.m.1.cv2.bn.weight     False           32                 [32]       1.05      0.242 torch.float32
   38                  model.4.m.1.cv2.bn.bias     False           32                 [32]      0.523      0.868 torch.float32
   39                      model.5.conv.weight     False        73728      [128, 64, 3, 3]  -0.000612     0.0224 torch.float32
   40                        model.5.bn.weight     False          128                [128]      0.821      0.231 torch.float32
   41                          model.5.bn.bias     False          128                [128]     -0.235      0.678 torch.float32
   42                  model.6.cv1.conv.weight     False        16384     [128, 128, 1, 1]   -0.00307     0.0347 torch.float32
   43                    model.6.cv1.bn.weight     False          128                [128]      0.895      0.398 torch.float32
   44                      model.6.cv1.bn.bias     False          128                [128]     -0.134      0.746 torch.float32
   45                  model.6.cv2.conv.weight     False        32768     [128, 256, 1, 1]   -0.00204     0.0303 torch.float32
   46                    model.6.cv2.bn.weight     False          128                [128]      0.773      0.205 torch.float32
   47                      model.6.cv2.bn.bias     False          128                [128]     -0.584      0.755 torch.float32
   48              model.6.m.0.cv1.conv.weight     False        36864       [64, 64, 3, 3]   -0.00186     0.0232 torch.float32
   49                model.6.m.0.cv1.bn.weight     False           64                 [64]       1.06      0.176 torch.float32
   50                  model.6.m.0.cv1.bn.bias     False           64                 [64]     -0.915      0.598 torch.float32
   51              model.6.m.0.cv2.conv.weight     False        36864       [64, 64, 3, 3]   -0.00196     0.0221 torch.float32
   52                model.6.m.0.cv2.bn.weight     False           64                 [64]      0.833       0.25 torch.float32
   53                  model.6.m.0.cv2.bn.bias     False           64                 [64]    -0.0806      0.526 torch.float32
   54              model.6.m.1.cv1.conv.weight     False        36864       [64, 64, 3, 3]   -0.00194     0.0225 torch.float32
   55                model.6.m.1.cv1.bn.weight     False           64                 [64]      0.916      0.178 torch.float32
   56                  model.6.m.1.cv1.bn.bias     False           64                 [64]      -1.13      0.732 torch.float32
   57              model.6.m.1.cv2.conv.weight     False        36864       [64, 64, 3, 3]   -0.00147     0.0213 torch.float32
   58                model.6.m.1.cv2.bn.weight     False           64                 [64]       1.18      0.278 torch.float32
   59                  model.6.m.1.cv2.bn.bias     False           64                 [64]      0.206      0.767 torch.float32
   60                      model.7.conv.weight     False       294912     [256, 128, 3, 3]  -0.000707     0.0142 torch.float32
   61                        model.7.bn.weight     False          256                [256]      0.961      0.197 torch.float32
   62                          model.7.bn.bias     False          256                [256]     -0.718      0.451 torch.float32
   63                  model.8.cv1.conv.weight     False        65536     [256, 256, 1, 1]   -0.00323     0.0224 torch.float32
   64                    model.8.cv1.bn.weight     False          256                [256]       1.14       0.33 torch.float32
   65                      model.8.cv1.bn.bias     False          256                [256]     -0.734      0.573 torch.float32
   66                  model.8.cv2.conv.weight     False        98304     [256, 384, 1, 1]   -0.00194     0.0198 torch.float32
   67                    model.8.cv2.bn.weight     False          256                [256]       1.19      0.223 torch.float32
   68                      model.8.cv2.bn.bias     False          256                [256]     -0.689      0.486 torch.float32
   69              model.8.m.0.cv1.conv.weight     False       147456     [128, 128, 3, 3]    -0.0011     0.0152 torch.float32
   70                model.8.m.0.cv1.bn.weight     False          128                [128]       1.14      0.205 torch.float32
   71                  model.8.m.0.cv1.bn.bias     False          128                [128]     -0.821       0.72 torch.float32
   72              model.8.m.0.cv2.conv.weight     False       147456     [128, 128, 3, 3]   -0.00112     0.0151 torch.float32
   73                model.8.m.0.cv2.bn.weight     False          128                [128]       1.65      0.369 torch.float32
   74                  model.8.m.0.cv2.bn.bias     False          128                [128]       -0.2      0.606 torch.float32
   75                  model.9.cv1.conv.weight     False        32768     [128, 256, 1, 1]   -0.00452     0.0257 torch.float32
   76                    model.9.cv1.bn.weight     False          128                [128]      0.926      0.251 torch.float32
   77                      model.9.cv1.bn.bias     False          128                [128]       1.43      0.654 torch.float32
   78                  model.9.cv2.conv.weight     False       131072     [256, 512, 1, 1]  -0.000201      0.018 torch.float32
   79                    model.9.cv2.bn.weight     False          256                [256]      0.936      0.257 torch.float32
   80                      model.9.cv2.bn.bias     False          256                [256]      -1.27      0.828 torch.float32
   81                 model.12.cv1.conv.weight     False        49152     [128, 384, 1, 1]   -0.00243     0.0255 torch.float32
   82                   model.12.cv1.bn.weight     False          128                [128]       0.87      0.224 torch.float32
   83                     model.12.cv1.bn.bias     False          128                [128]     -0.373      0.821 torch.float32
   84                 model.12.cv2.conv.weight     False        24576     [128, 192, 1, 1]   -0.00462     0.0284 torch.float32
   85                   model.12.cv2.bn.weight     False          128                [128]       0.73      0.201 torch.float32
   86                     model.12.cv2.bn.bias     False          128                [128]     -0.281      0.661 torch.float32
   87             model.12.m.0.cv1.conv.weight     False        36864       [64, 64, 3, 3]    -0.0022     0.0224 torch.float32
   88               model.12.m.0.cv1.bn.weight     False           64                 [64]      0.832      0.134 torch.float32
   89                 model.12.m.0.cv1.bn.bias     False           64                 [64]     -0.841      0.611 torch.float32
   90             model.12.m.0.cv2.conv.weight     False        36864       [64, 64, 3, 3]  -0.000787     0.0213 torch.float32
   91               model.12.m.0.cv2.bn.weight     False           64                 [64]      0.824      0.182 torch.float32
   92                 model.12.m.0.cv2.bn.bias     False           64                 [64]     -0.107      0.636 torch.float32
   93                 model.15.cv1.conv.weight     False        12288      [64, 192, 1, 1]   -0.00189     0.0321 torch.float32
   94                   model.15.cv1.bn.weight     False           64                 [64]      0.536      0.215 torch.float32
   95                     model.15.cv1.bn.bias     False           64                 [64]       0.16      0.967 torch.float32
   96                 model.15.cv2.conv.weight     False         6144       [64, 96, 1, 1]   -0.00124     0.0364 torch.float32
   97                   model.15.cv2.bn.weight     False           64                 [64]      0.561      0.275 torch.float32
   98                     model.15.cv2.bn.bias     False           64                 [64]      0.127      0.942 torch.float32
   99             model.15.m.0.cv1.conv.weight     False         9216       [32, 32, 3, 3]   -0.00236     0.0306 torch.float32
  100               model.15.m.0.cv1.bn.weight     False           32                 [32]      0.663       0.14 torch.float32
  101                 model.15.m.0.cv1.bn.bias     False           32                 [32]     -0.567      0.583 torch.float32
  102             model.15.m.0.cv2.conv.weight     False         9216       [32, 32, 3, 3]   -0.00152     0.0289 torch.float32
  103               model.15.m.0.cv2.bn.weight     False           32                 [32]      0.741      0.161 torch.float32
  104                 model.15.m.0.cv2.bn.bias     False           32                 [32]       0.21      0.786 torch.float32
  105                     model.16.conv.weight     False        36864       [64, 64, 3, 3]  -0.000977     0.0176 torch.float32
  106                       model.16.bn.weight     False           64                 [64]      0.842      0.216 torch.float32
  107                         model.16.bn.bias     False           64                 [64]     -0.389      0.592 torch.float32
  108                 model.18.cv1.conv.weight     False        24576     [128, 192, 1, 1]   -0.00217     0.0241 torch.float32
  109                   model.18.cv1.bn.weight     False          128                [128]      0.876      0.208 torch.float32
  110                     model.18.cv1.bn.bias     False          128                [128]     -0.307      0.611 torch.float32
  111                 model.18.cv2.conv.weight     False        24576     [128, 192, 1, 1]   -0.00254     0.0237 torch.float32
  112                   model.18.cv2.bn.weight     False          128                [128]      0.726      0.297 torch.float32
  113                     model.18.cv2.bn.bias     False          128                [128]     -0.434      0.761 torch.float32
  114             model.18.m.0.cv1.conv.weight     False        36864       [64, 64, 3, 3]   -0.00257     0.0203 torch.float32
  115               model.18.m.0.cv1.bn.weight     False           64                 [64]      0.798      0.175 torch.float32
  116                 model.18.m.0.cv1.bn.bias     False           64                 [64]     -0.773      0.493 torch.float32
  117             model.18.m.0.cv2.conv.weight     False        36864       [64, 64, 3, 3]    -0.0014     0.0195 torch.float32
  118               model.18.m.0.cv2.bn.weight     False           64                 [64]       1.19      0.285 torch.float32
  119                 model.18.m.0.cv2.bn.bias     False           64                 [64]      -0.07      0.668 torch.float32
  120                     model.19.conv.weight     False       147456     [128, 128, 3, 3]  -0.000819     0.0118 torch.float32
  121                       model.19.bn.weight     False          128                [128]      0.876       0.21 torch.float32
  122                         model.19.bn.bias     False          128                [128]     -0.508      0.365 torch.float32
  123                 model.21.cv1.conv.weight     False        98304     [256, 384, 1, 1]   -0.00166     0.0159 torch.float32
  124                   model.21.cv1.bn.weight     False          256                [256]       1.06      0.225 torch.float32
  125                     model.21.cv1.bn.bias     False          256                [256]     -0.592      0.579 torch.float32
  126                 model.21.cv2.conv.weight     False        98304     [256, 384, 1, 1]   -0.00257     0.0148 torch.float32
  127                   model.21.cv2.bn.weight     False          256                [256]       1.03      0.317 torch.float32
  128                     model.21.cv2.bn.bias     False          256                [256]     -0.893      0.491 torch.float32
  129             model.21.m.0.cv1.conv.weight     False       147456     [128, 128, 3, 3]   -0.00142     0.0129 torch.float32
  130               model.21.m.0.cv1.bn.weight     False          128                [128]       1.03      0.223 torch.float32
  131                 model.21.m.0.cv1.bn.bias     False          128                [128]      -0.96      0.618 torch.float32
  132             model.21.m.0.cv2.conv.weight     False       147456     [128, 128, 3, 3]   -0.00124     0.0128 torch.float32
  133               model.21.m.0.cv2.bn.weight     False          128                [128]       1.35      0.253 torch.float32
  134                 model.21.m.0.cv2.bn.bias     False          128                [128]     -0.553      0.516 torch.float32
  135             model.22.cv2.0.0.conv.weight     False        36864       [64, 64, 3, 3]   -0.00193     0.0181 torch.float32
  136               model.22.cv2.0.0.bn.weight     False           64                 [64]       0.88      0.351 torch.float32
  137                 model.22.cv2.0.0.bn.bias     False           64                 [64]     -0.492      0.707 torch.float32
  138             model.22.cv2.0.1.conv.weight     False        36864       [64, 64, 3, 3]   -0.00163     0.0178 torch.float32
  139               model.22.cv2.0.1.bn.weight     False           64                 [64]       2.41       1.04 torch.float32
  140                 model.22.cv2.0.1.bn.bias     False           64                 [64]      0.922      0.757 torch.float32
  141                  model.22.cv2.0.2.weight     False         4096       [64, 64, 1, 1]   -0.00542     0.0553 torch.float32
  142                    model.22.cv2.0.2.bias     False           64                 [64]      0.997       1.39 torch.float32
  143             model.22.cv2.1.0.conv.weight     False        73728      [64, 128, 3, 3]    -0.0017      0.014 torch.float32
  144               model.22.cv2.1.0.bn.weight     False           64                 [64]       1.28      0.485 torch.float32
  145                 model.22.cv2.1.0.bn.bias     False           64                 [64]     -0.389       0.68 torch.float32
  146             model.22.cv2.1.1.conv.weight     False        36864       [64, 64, 3, 3]   -0.00128     0.0167 torch.float32
  147               model.22.cv2.1.1.bn.weight     False           64                 [64]       2.56          1 torch.float32
  148                 model.22.cv2.1.1.bn.bias     False           64                 [64]      0.855      0.564 torch.float32
  149                  model.22.cv2.1.2.weight     False         4096       [64, 64, 1, 1]   -0.00756     0.0594 torch.float32
  150                    model.22.cv2.1.2.bias     False           64                 [64]      0.992       1.32 torch.float32
  151             model.22.cv2.2.0.conv.weight     False       147456      [64, 256, 3, 3]  -0.000553     0.0115 torch.float32
  152               model.22.cv2.2.0.bn.weight     False           64                 [64]       1.55      0.413 torch.float32
  153                 model.22.cv2.2.0.bn.bias     False           64                 [64]     -0.259       0.62 torch.float32
  154             model.22.cv2.2.1.conv.weight     False        36864       [64, 64, 3, 3]   -0.00065     0.0149 torch.float32
  155               model.22.cv2.2.1.bn.weight     False           64                 [64]       2.95      0.852 torch.float32
  156                 model.22.cv2.2.1.bn.bias     False           64                 [64]      0.822      0.556 torch.float32
  157                  model.22.cv2.2.2.weight     False         4096       [64, 64, 1, 1]   -0.00674     0.0609 torch.float32
  158                    model.22.cv2.2.2.bias     False           64                 [64]       0.99       1.33 torch.float32
  159             model.22.cv3.0.0.conv.weight     False        36864       [64, 64, 3, 3]   -0.00138     0.0275 torch.float32
  160               model.22.cv3.0.0.bn.weight     False           64                 [64]          1    0.00984 torch.float32
  161                 model.22.cv3.0.0.bn.bias     False           64                 [64]    0.00709     0.0156 torch.float32
  162             model.22.cv3.0.1.conv.weight     False        36864       [64, 64, 3, 3]   -0.00549     0.0281 torch.float32
  163               model.22.cv3.0.1.bn.weight     False           64                 [64]       1.01     0.0248 torch.float32
  164                 model.22.cv3.0.1.bn.bias     False           64                 [64]     0.0356     0.0581 torch.float32
  165                  model.22.cv3.0.2.weight     False           64        [1, 64, 1, 1]    -0.0297     0.0872 torch.float32
  166                    model.22.cv3.0.2.bias     False            1                  [1]       -7.2        nan torch.float32
  167             model.22.cv3.1.0.conv.weight     False        73728      [64, 128, 3, 3]   -0.00178     0.0217 torch.float32
  168               model.22.cv3.1.0.bn.weight     False           64                 [64]          1     0.0165 torch.float32
  169                 model.22.cv3.1.0.bn.bias     False           64                 [64]    0.00244     0.0185 torch.float32
  170             model.22.cv3.1.1.conv.weight     False        36864       [64, 64, 3, 3]   -0.00481     0.0278 torch.float32
  171               model.22.cv3.1.1.bn.weight     False           64                 [64]       1.02     0.0239 torch.float32
  172                 model.22.cv3.1.1.bn.bias     False           64                 [64]     0.0411     0.0545 torch.float32
  173                  model.22.cv3.1.2.weight     False           64        [1, 64, 1, 1]    -0.0327      0.104 torch.float32
  174                    model.22.cv3.1.2.bias     False            1                  [1]       -5.8        nan torch.float32
  175             model.22.cv3.2.0.conv.weight     False       147456      [64, 256, 3, 3]  -0.000784     0.0158 torch.float32
  176               model.22.cv3.2.0.bn.weight     False           64                 [64]          1    0.00871 torch.float32
  177                 model.22.cv3.2.0.bn.bias     False           64                 [64]    0.00025     0.0116 torch.float32
  178             model.22.cv3.2.1.conv.weight     False        36864       [64, 64, 3, 3]   -0.00326     0.0272 torch.float32
  179               model.22.cv3.2.1.bn.weight     False           64                 [64]       1.06     0.0776 torch.float32
  180                 model.22.cv3.2.1.bn.bias     False           64                 [64]      0.048     0.0988 torch.float32
  181                  model.22.cv3.2.2.weight     False           64        [1, 64, 1, 1]    -0.0661      0.101 torch.float32
  182                    model.22.cv3.2.2.bias     False            1                  [1]      -4.48        nan torch.float32
  183                 model.22.dfl.conv.weight     False           16        [1, 16, 1, 1]        7.5       4.76 torch.float32
Model summary: 225 layers, 3011043 parameters, 0 gradients, 8.2 GFLOPs
(225, 3011043, 0, 8.1941504)

可以看到,打印出了模型每一层网络结构的名字、参数量以及该层的结构形状。

本文方法同样适用于ultralytics框架的其他模型结构,使用方法相同,可用于不同模型进行参数量、计算量等对比使用。

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