解释每一句super(CustomCSPPAN, self).__init__() out_channels = [max(round(c * width_mult), 1) for c in out_channels] block_num = max(round(block_num * depth_mult), 1) act = get_act_fn( act, trt=trt) if act is None or isinstance(act, (str, dict)) else act self.num_blocks = len(in_channels) self.data_format = data_format self._out_channels = out_channels in_channels = in_channels[::-1] fpn_stages = [] fpn_routes = [] for i, (ch_in, ch_out) in enumerate(zip(in_channels, out_channels)): if i > 0: ch_in += ch_pre // 2 stage = nn.Sequential() for j in range(stage_num): stage.add_sublayer( str(j), eval(stage_fn)(block_fn, ch_in if j == 0 else ch_out, ch_out, block_num, act=act, spp=(spp and i == 0))) if drop_block: stage.add_sublayer('drop', DropBlock(block_size, keep_prob))

这段代码是一个类的初始化方法。它首先调用了父类的初始化方法。然后，根据给定的参数计算了一些输出通道数和块的数量，以及激活函数。接下来，它计算了输入通道和输出通道的反向列表，然后创建了一个空的列表fpn_stages和fpn_routes。接下来，它通过循环遍历输入通道和输出通道的zip对象，计算了每个阶段的输入通道和输出通道，并创建了一个Sequential对象作为该阶段的模型。在每个阶段中，它使用eval()函数来动态地创建一个新的block_fn对象，并将其添加到Sequential对象中。如果drop_block参数为True，则它还会添加一个DropBlock层到每个阶段的末尾。

相关问题

解释一下这段代码def __init__(self, channels, use_conv, dims=2, out_channels=None): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims if use_conv: self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)

这段代码是一个类的初始化函数，它有四个参数：channels、use_conv、dims和out_channels。其中，channels表示输入数据的通道数，out_channels表示输出数据的通道数，如果没有指定out_channels，则默认与channels相同。use_conv表示是否使用卷积操作，dims表示数据的维度，可以是1、2或3。如果use_conv为True，则会创建一个卷积层，使用conv_nd函数创建，卷积核大小为3，padding为1。

class SelfAttention(nn.Module): def __init__(self,in_c,out_c,fm_sz,pos_bias = False): super(SelfAttention,self).__init__() self.w_q = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_k = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_v = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.pos_code = self.__getPosCode(fm_sz,out_c) self.softmax = nn.Softmax(dim = 2) self.pos_bias = pos_bias 改写为twensorflow形式

可以将这段PyTorch代码改写为如下的TensorFlow代码：

import tensorflow as tf
from tensorflow import keras

class SelfAttention(keras.layers.Layer):
    def __init__(self, in_c, out_c, fm_sz, pos_bias=False):
        super(SelfAttention, self).__init__()
        self.w_q = keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=(None, None, in_c))
        self.w_k = keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=(None, None, in_c))
        self.w_v = keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=(None, None, in_c))
        self.pos_code = self.__getPosCode(fm_sz, out_c)
        self.softmax = keras.layers.Softmax(axis=2)
        self.pos_bias = pos_bias
        
    def __getPosCode(self, fm_sz, out_c):
        x = []
        for i in range(fm_sz):
            x.append([np.sin, np.cos][i % 2](1 / (10000 ** (i // 2 / fm_sz))))
        x = tf.convert_to_tensor([x], dtype=tf.float32)
        return tf.concat([(x + tf.transpose(x)).unsqueeze(0) for i in range(out_c)], axis=0)
    
    def call(self, x):
        q, k, v = self.w_q(x), self.w_k(x), self.w_v(x)
        pos_code = tf.concat([self.pos_code.unsqueeze(0) for i in range(x.shape[0])], axis=0)
        if self.pos_bias:
            att_map = tf.matmul(q, tf.transpose(k, perm=[0, 1, 3, 2])) + pos_code
        else:
            att_map = tf.matmul(q, tf.transpose(k, perm=[0, 1, 3, 2])) + tf.matmul(q, tf.transpose(pos_code, perm=[0, 1, 3, 2]))
        am_shape = att_map.shape
        att_map = self.softmax(tf.reshape(att_map, [am_shape[0], am_shape[1], am_shape[2] * am_shape[3]]))
        att_map = tf.reshape(att_map, am_shape)
        return att_map * v

需要注意的是，这里的代码只是一种可能的TensorFlow实现方式，具体还需要根据实际情况进行微调。