what is the meaning of the following code in''MPViT-main\semantic_segmentation\configs\_base_\models''

[zhouqunbing]

这段代码具体对应于文中的哪个模块呀？为什么会牵扯到相对位置编码呢？
class ConvRelPosEnc(nn.Module):
"""Convolutional relative position encoding."""
def init(self, Ch, h, window):
"""Initialization.

    Ch: Channels per head.
    h: Number of heads.
    window: Window size(s) in convolutional relative positional encoding.
            It can have two forms:
            1. An integer of window size, which assigns all attention heads
               with the same window size in ConvRelPosEnc.
            2. A dict mapping window size to #attention head splits
               (e.g. {window size 1: #attention head split 1, window size
                                  2: #attention head split 2})
               It will apply different window size to
               the attention head splits.
    """
    super().__init__()

    if isinstance(window, int):
        # Set the same window size for all attention heads.
        window = {window: h}
        self.window = window
    elif isinstance(window, dict):
        self.window = window
    else:
        raise ValueError()

    self.conv_list = nn.ModuleList()
    self.head_splits = []
    for cur_window, cur_head_split in window.items():
        dilation = 1  # Use dilation=1 at default.
        padding_size = (cur_window + (cur_window - 1) *
                        (dilation - 1)) // 2
        cur_conv = nn.Conv2d(
            cur_head_split * Ch,
            cur_head_split * Ch,
            kernel_size=(cur_window, cur_window),
            padding=(padding_size, padding_size),
            dilation=(dilation, dilation),
            groups=cur_head_split * Ch,
            )
        self.conv_list.append(cur_conv)
        self.head_splits.append(cur_head_split)
    self.channel_splits = [x * Ch for x in self.head_splits]

def forward(self, q, v, size):
    """foward function"""
    B, h, N, Ch = q.shape
    H, W = size

    # We don't use CLS_TOKEN
    q_img = q
    v_img = v

    # Shape: [B, h, H*W, Ch] -> [B, h*Ch, H, W].
    v_img = rearrange(v_img, "B h (H W) Ch -> B (h Ch) H W", H=H, W=W)
    # Split according to channels.
    v_img_list = torch.split(v_img, self.channel_splits, dim=1)
    conv_v_img_list = [
        conv(x) for conv, x in zip(self.conv_list, v_img_list)
    ]
    conv_v_img = torch.cat(conv_v_img_list, dim=1)
    # Shape: [B, h*Ch, H, W] -> [B, h, H*W, Ch].
    conv_v_img = rearrange(conv_v_img, "B (h Ch) H W -> B h (H W) Ch", h=h)

    EV_hat_img = q_img * conv_v_img
    EV_hat = EV_hat_img
    return EV_hat

[1787648106]

Transformer中的分解自注意力的计算那块。
另外有没有人使用多卡环境跑的时候，每个两个验证报torch.distributed.elastic.multiprocessing.api:failed（exitcode:-9）然后程序终止的呀？