the Question about code "vit_up_head.py"

[songfy825]

class VisionTransformerUpHead(BaseDecodeHead):
    """ Vision Transformer with support for patch or hybrid CNN input stage
    """

    def __init__(self, img_size=768, embed_dim=1024,
                 norm_layer=partial(nn.LayerNorm, eps=1e-6), norm_cfg=None,
                 num_conv=1, upsampling_method='bilinear', num_upsampe_layer=1, conv3x3_conv1x1=True, **kwargs):
        super(VisionTransformerUpHead, self).__init__(**kwargs)
        self.img_size = img_size
        self.norm_cfg = norm_cfg
        self.num_conv = num_conv
        self.norm = norm_layer(embed_dim)
        self.upsampling_method = upsampling_method
        self.num_upsampe_layer = num_upsampe_layer
        self.conv3x3_conv1x1 = conv3x3_conv1x1

        out_channel = self.num_classes

        if self.num_conv == 2:
            if self.conv3x3_conv1x1:
                self.conv_0 = nn.Conv2d(
                    embed_dim, 256, kernel_size=3, stride=1, padding=1)
            else:
                self.conv_0 = nn.Conv2d(embed_dim, 256, 1, 1)
            self.conv_1 = nn.Conv2d(256, out_channel, 1, 1)
            _, self.syncbn_fc_0 = build_norm_layer(self.norm_cfg, 256)

        elif self.num_conv == 4:
            self.conv_0 = nn.Conv2d(
                embed_dim, 256, kernel_size=3, stride=1, padding=1)
            self.conv_1 = nn.Conv2d(
                256, 256, kernel_size=3, stride=1, padding=1)
            self.conv_2 = nn.Conv2d(
                256, 256, kernel_size=3, stride=1, padding=1)
            self.conv_3 = nn.Conv2d(
                256, 256, kernel_size=3, stride=1, padding=1)
            self.conv_4 = nn.Conv2d(256, out_channel, kernel_size=1, stride=1)

            _, self.syncbn_fc_0 = build_norm_layer(self.norm_cfg, 256)
            _, self.syncbn_fc_1 = build_norm_layer(self.norm_cfg, 256)
            _, self.syncbn_fc_2 = build_norm_layer(self.norm_cfg, 256)
            _, self.syncbn_fc_3 = build_norm_layer(self.norm_cfg, 256)

        # Segmentation head

    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Linear):
                trunc_normal_(m.weight, std=.02)
                if isinstance(m, nn.Linear) and m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.LayerNorm):
                nn.init.constant_(m.bias, 0)
                nn.init.constant_(m.weight, 1.0)

    def forward(self, x):
        x = self._transform_inputs(x)
        if x.dim() == 3:
            if x.shape[1] % 48 != 0:
                x = x[:, 1:]
            x = self.norm(x)

        if self.upsampling_method == 'bilinear':
            if x.dim() == 3:
                n, hw, c = x.shape
                h = w = int(math.sqrt(hw))
                x = x.transpose(1, 2).reshape(n, c, h, w)

            if self.num_conv == 2:
                if self.num_upsampe_layer == 2:
                    x = self.conv_0(x)
                    x = self.syncbn_fc_0(x)
                    x = F.relu(x, inplace=True)
                    x = F.interpolate(
                        x, size=x.shape[-1]*4, mode='bilinear', align_corners=self.align_corners)
                    x = self.conv_1(x)
                    x = F.interpolate(
                        x, size=self.img_size, mode='bilinear', align_corners=self.align_corners)
                elif self.num_upsampe_layer == 1:
                    x = self.conv_0(x)
                    x = self.syncbn_fc_0(x)
                    x = F.relu(x, inplace=True)
                    x = self.conv_1(x)
                    x = F.interpolate(
                        x, size=self.img_size, mode='bilinear', align_corners=self.align_corners)
            elif self.num_conv == 4:
                if self.num_upsampe_layer == 4:
                    x = self.conv_0(x)
                    x = self.syncbn_fc_0(x)
                    x = F.relu(x, inplace=True)
                    x = F.interpolate(
                        x, size=x.shape[-1]*2, mode='bilinear', align_corners=self.align_corners)
                    x = self.conv_1(x)
                    x = self.syncbn_fc_1(x)
                    x = F.relu(x, inplace=True)
                    x = F.interpolate(
                        x, size=x.shape[-1]*2, mode='bilinear', align_corners=self.align_corners)
                    x = self.conv_2(x)
                    x = self.syncbn_fc_2(x)
                    x = F.relu(x, inplace=True)
                    x = F.interpolate(
                        x, size=x.shape[-1]*2, mode='bilinear', align_corners=self.align_corners)
                    x = self.conv_3(x)
                    x = self.syncbn_fc_3(x)
                    x = F.relu(x, inplace=True)
                    x = self.conv_4(x)
                    x = F.interpolate(
                        x, size=x.shape[-1]*2, mode='bilinear', align_corners=self.align_corners)

        return x

When "self.num_conv == 4:" F.interpolate only do on the last dimension,WHY the final shape would not be (B,num_classes,H/16,W)?