YOLOv7的Darknet53是哪几行代码

YOLOv7中的Darknet53是一个神经网络模型，包含多个层次的卷积和池化操作。以下是Darknet53的代码实现：

def conv_bn_leaky(x, num_filters, filter_size, stride=1, padding='same'):
    x = tf.keras.layers.Conv2D(num_filters, filter_size, strides=stride, padding=padding, use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)
    return x

def residual_block(x, num_filters):
    shortcut = x
    x = conv_bn_leaky(x, num_filters, 1)
    x = conv_bn_leaky(x, num_filters*2, 3)
    x = tf.keras.layers.Add()([shortcut, x])
    return x

def darknet53(x):
    x = conv_bn_leaky(x, 32, 3)
    x = conv_bn_leaky(x, 64, 3, stride=2)
    x = residual_block(x, 32)
    x = conv_bn_leaky(x, 128, 3, stride=2)
    for i in range(2):
        x = residual_block(x, 64)
    x = conv_bn_leaky(x, 256, 3, stride=2)
    for i in range(8):
        x = residual_block(x, 128)
    route_1 = x
    x = conv_bn_leaky(x, 512, 3, stride=2)
    for i in range(8):
        x = residual_block(x, 256)
    route_2 = x
    x = conv_bn_leaky(x, 1024, 3, stride=2)
    for i in range(4):
        x = residual_block(x, 512)
    return route_1, route_2, x

其中，`conv_bn_leaky` 函数实现了卷积层、批量归一化和 LeakyReLU(斜率为 0.1 的修正线性单元)激活函数，`residual_block` 函数实现了残差块，`darknet53` 函数则是将多个卷积层和残差块串联在一起，实现了整个Darknet53网络的前向传播。