Deprecation warning message is misleading

[brandonfosso]

monai/networks/nets/swin_unetr:__init__ has a note that the img_size argument is no longer required but img_size is used within the first few lines of the function and would definitely be required. Am I missing something here? Code snippet below:

    @deprecated_arg(
        name="img_size",
        since="1.3",
        removed="1.5",
        msg_suffix="The img_size argument is not required anymore and "
        "checks on the input size are run during forward().",
    )
    def __init__(
        self,
        img_size: Sequence[int] | int,
        in_channels: int,
        out_channels: int,
        depths: Sequence[int] = (2, 2, 2, 2),
        num_heads: Sequence[int] = (3, 6, 12, 24),
        feature_size: int = 24,
        norm_name: tuple | str = "instance",
        drop_rate: float = 0.0,
        attn_drop_rate: float = 0.0,
        dropout_path_rate: float = 0.0,
        normalize: bool = True,
        use_checkpoint: bool = False,
        spatial_dims: int = 3,
        downsample="merging",
        use_v2=False,
    ) -> None:
        """
        Args:
            img_size: spatial dimension of input image.
                This argument is only used for checking that the input image size is divisible by the patch size.
                The tensor passed to forward() can have a dynamic shape as long as its spatial dimensions are divisible by 2**5.
                It will be removed in an upcoming version.
            in_channels: dimension of input channels.
            out_channels: dimension of output channels.
            feature_size: dimension of network feature size.
            depths: number of layers in each stage.
            num_heads: number of attention heads.
            norm_name: feature normalization type and arguments.
            drop_rate: dropout rate.
            attn_drop_rate: attention dropout rate.
            dropout_path_rate: drop path rate.
            normalize: normalize output intermediate features in each stage.
            use_checkpoint: use gradient checkpointing for reduced memory usage.
            spatial_dims: number of spatial dims.
            downsample: module used for downsampling, available options are `"mergingv2"`, `"merging"` and a
                user-specified `nn.Module` following the API defined in :py:class:`monai.networks.nets.PatchMerging`.
                The default is currently `"merging"` (the original version defined in v0.9.0).
            use_v2: using swinunetr_v2, which adds a residual convolution block at the beggining of each swin stage.

        Examples::

            # for 3D single channel input with size (96,96,96), 4-channel output and feature size of 48.
            >>> net = SwinUNETR(img_size=(96,96,96), in_channels=1, out_channels=4, feature_size=48)

            # for 3D 4-channel input with size (128,128,128), 3-channel output and (2,4,2,2) layers in each stage.
            >>> net = SwinUNETR(img_size=(128,128,128), in_channels=4, out_channels=3, depths=(2,4,2,2))

            # for 2D single channel input with size (96,96), 2-channel output and gradient checkpointing.
            >>> net = SwinUNETR(img_size=(96,96), in_channels=3, out_channels=2, use_checkpoint=True, spatial_dims=2)

        """

        super().__init__()

        img_size = ensure_tuple_rep(img_size, spatial_dims)
        patch_sizes = ensure_tuple_rep(self.patch_size, spatial_dims)
        window_size = ensure_tuple_rep(7, spatial_dims)

        if spatial_dims not in (2, 3):
            raise ValueError("spatial dimension should be 2 or 3.")

        self._check_input_size(img_size)
        ...

[KumoLiu]

Hi @brandonfosso, thanks for reporting.
You may get some information from the PR: #7093 (comment)
Yes, for now you still need to specify this argument and may be a little wired.

[brandonfosso]

Thanks for explaining