Conv是yolov5自定義的類，里邊包含了卷積層、BN層和激活函數

class Conv(nn.Module):# Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)default_act = nn.SiLU()  # default activationdef __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):"""Initializes a standard convolution layer with optional batch normalization and activation."""super().__init__()self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)self.bn = nn.BatchNorm2d(c2)self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()

假設輸入是[3, 32, 6, 2, 2]（就是yolov5s第一層），即輸入通道=3，輸出通道=32，kernel_size=3，padding=2，stride=2

于是有：
參數量=conv2d參數+BN2d參數=3*6*6*32+32*2 = 3520