unik3d/models/backbones/swinv2.py

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_

from unik3d.utils.misc import get_params, load_checkpoint_swin


class Mlp(nn.Module):
    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        drop=0.0,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(
        B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C
    )
    windows = (
        x.permute(0, 1, 3, 2, 4, 5)
        .contiguous()
        .view(-1, window_size[0], window_size[1], C)
    )
    return windows


def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image

    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))
    x = windows.view(
        B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -1
    )
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class WindowAttention(nn.Module):
    r"""Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
    """

    def __init__(
        self,
        dim,
        window_size,
        num_heads,
        qkv_bias=True,
        attn_drop=0.0,
        proj_drop=0.0,
        pretrained_window_size=[0, 0],
    ):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.pretrained_window_size = pretrained_window_size
        self.num_heads = num_heads

        self.logit_scale = nn.Parameter(
            torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True
        )

        # mlp to generate continuous relative position bias
        self.rpe_mlp = nn.Sequential(
            nn.Linear(2, 512, bias=True),
            nn.ReLU(inplace=True),
            nn.Linear(512, num_heads, bias=False),
        )

        # get relative_coords_table
        relative_coords_h = torch.arange(
            -(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32
        )
        relative_coords_w = torch.arange(
            -(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32
        )
        relative_coords_table = (
            torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w]))
            .permute(1, 2, 0)
            .contiguous()
            .unsqueeze(0)
        )  # 1, 2*Wh-1, 2*Ww-1, 2
        if pretrained_window_size[0] > 0:
            relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1
            relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1
        else:
            relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1
            relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1
        relative_coords_table *= 8  # normalize to -8, 8
        relative_coords_table = (
            torch.sign(relative_coords_table)
            * torch.log2(torch.abs(relative_coords_table) + 1.0)
            / np.log2(8)
        )

        self.register_buffer("relative_coords_table", relative_coords_table)

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = (
            coords_flatten[:, :, None] - coords_flatten[:, None, :]
        )  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(
            1, 2, 0
        ).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=False)
        if qkv_bias:
            self.q_bias = nn.Parameter(torch.zeros(dim))
            self.v_bias = nn.Parameter(torch.zeros(dim))
        else:
            self.q_bias = None
            self.v_bias = None
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv_bias = None
        if self.q_bias is not None:
            qkv_bias = torch.cat(
                (
                    self.q_bias,
                    torch.zeros_like(self.v_bias, requires_grad=False),
                    self.v_bias,
                )
            )
        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        q, k, v = (
            qkv[0],
            qkv[1],
            qkv[2],
        )  # make torchscript happy (cannot use tensor as tuple)

        # cosine attention
        attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)
        logit_scale = torch.clamp(
            self.logit_scale,
            max=torch.log(torch.tensor(1.0 / 0.01, device=self.logit_scale.device)),
        ).exp()
        attn = attn * logit_scale

        relative_position_bias_table = self.rpe_mlp(self.relative_coords_table).view(
            -1, self.num_heads
        )
        relative_position_bias = relative_position_bias_table[
            self.relative_position_index.view(-1)
        ].view(
            self.window_size[0] * self.window_size[1],
            self.window_size[0] * self.window_size[1],
            -1,
        )  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(
            2, 0, 1
        ).contiguous()  # nH, Wh*Ww, Wh*Ww
        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
                1
            ).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)

        attn = self.softmax(attn)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def extra_repr(self) -> str:
        return (
            f"dim={self.dim}, window_size={self.window_size}, "
            f"pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}"
        )

    def flops(self, N):
        # calculate flops for 1 window with token length of N
        flops = 0
        # qkv = self.qkv(x)
        flops += N * self.dim * 3 * self.dim
        # attn = (q @ k.transpose(-2, -1))
        flops += self.num_heads * N * (self.dim // self.num_heads) * N
        #  x = (attn @ v)
        flops += self.num_heads * N * N * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += N * self.dim * self.dim
        return flops


class SwinTransformerBlock(nn.Module):
    r"""Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
        pretrained_window_size (int): Window size in pre-training.
    """

    def __init__(
        self,
        dim,
        input_resolution,
        num_heads,
        window_size=7,
        shift_size=0,
        mlp_ratio=4.0,
        qkv_bias=True,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        pretrained_window_size=0,
    ):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio

        if input_resolution[0] <= self.window_size[0]:
            self.shift_size[0] = 0
            self.window_size[0] = input_resolution[0]
        if input_resolution[1] <= self.window_size[1]:
            self.shift_size[1] = 0
            self.window_size[1] = input_resolution[1]

        assert (
            0 <= self.shift_size[1] < self.window_size[1]
        ), "shift_size must in 0-window_size"
        assert (
            0 <= self.shift_size[0] < self.window_size[0]
        ), "shift_size must in 0-window_size"
        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim,
            window_size=self.window_size,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            attn_drop=attn_drop,
            proj_drop=drop,
            pretrained_window_size=pretrained_window_size,
        )

        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )

        # if self.shift_size > 0:
        #     # calculate attention mask for SW-MSA
        #     H, W = self.input_resolution
        #     img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
        #     h_slices = (slice(0, -self.window_size),
        #                 slice(-self.window_size, -self.shift_size),
        #                 slice(-self.shift_size, None))
        #     w_slices = (slice(0, -self.window_size),
        #                 slice(-self.window_size, -self.shift_size),
        #                 slice(-self.shift_size, None))
        #     cnt = 0
        #     for h in h_slices:
        #         for w in w_slices:
        #             img_mask[:, h, w, :] = cnt
        #             cnt += 1

        #     mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
        #     mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
        #     attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
        #     attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        # else:
        #     attn_mask = None

        # self.register_buffer("attn_mask", attn_mask)

    def forward(self, x, mask_matrix):
        H, W = self.H, self.W
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        shortcut = x
        x = x.view(B, H, W, C)

        # pad feature maps to multiples of window size
        pad_l = pad_t = 0
        pad_r = (self.window_size[1] - W % self.window_size[1]) % self.window_size[1]
        pad_b = (self.window_size[0] - H % self.window_size[0]) % self.window_size[0]
        if pad_r > 0 or pad_b > 0:
            x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
        _, Hp, Wp, _ = x.shape

        # cyclic shift
        if self.shift_size[0] > 0 or self.shift_size[1] > 0:
            shifted_x = torch.roll(
                x, shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(1, 2)
            )
            attn_mask = mask_matrix
        else:
            shifted_x = x
            attn_mask = None

        # partition windows
        x_windows = window_partition(
            shifted_x, self.window_size
        )  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(
            -1, self.window_size[0] * self.window_size[1], C
        )  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(
            x_windows, mask=attn_mask
        )  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = attn_windows.view(
            -1, self.window_size[0], self.window_size[1], C
        )
        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C

        # reverse cyclic shift
        if self.shift_size[0] > 0 or self.shift_size[1] > 0:
            x = torch.roll(
                shifted_x, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2)
            )
        else:
            x = shifted_x

        if pad_r > 0 or pad_b > 0:
            x = x[:, :H, :W, :].contiguous()

        x = x.view(B, H * W, C)

        x = shortcut + self.drop_path(self.norm1(x))

        # FFN
        x = x + self.drop_path(self.norm2(self.mlp(x)))

        return x

    def extra_repr(self) -> str:
        return (
            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
            f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
        )

    def flops(self):
        flops = 0
        H, W = self.input_resolution
        # norm1
        flops += self.dim * H * W
        # W-MSA/SW-MSA
        nW = H * W / self.window_size / self.window_size
        flops += nW * self.attn.flops(self.window_size * self.window_size)
        # mlp
        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
        # norm2
        flops += self.dim * H * W
        return flops


class PatchMerging(nn.Module):
    r"""Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(2 * dim)

    def forward(self, x, H, W):
        """
        x: B, H*W, C
        """
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = x.view(B, H, W, C)

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.reduction(x)
        x = self.norm(x)

        return x

    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"

    def flops(self):
        H, W = self.input_resolution
        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        flops += H * W * self.dim // 2
        return flops


class BasicLayer(nn.Module):
    """A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
        pretrained_window_size (int): Local window size in pre-training.
    """

    def __init__(
        self,
        dim,
        input_resolution,
        depth,
        num_heads,
        window_size,
        use_shift=True,
        mlp_ratio=4.0,
        qkv_bias=True,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        norm_layer=nn.LayerNorm,
        downsample=None,
        use_checkpoint=False,
        pretrained_window_size=0,
    ):

        super().__init__()
        self.dim = dim
        self.depth = depth
        self.use_checkpoint = use_checkpoint
        self.window_size = list(to_2tuple(window_size))
        pretrained_window_size = list(to_2tuple(pretrained_window_size))
        self.shift_size = (
            [x // 2 for x in window_size]
            if isinstance(window_size, (tuple, list))
            else window_size // 2
        )
        self.shift_size = list(to_2tuple(self.shift_size))

        # build blocks
        self.blocks = nn.ModuleList(
            [
                SwinTransformerBlock(
                    dim=dim,
                    input_resolution=input_resolution,
                    num_heads=num_heads,
                    window_size=self.window_size,
                    shift_size=self.shift_size if (i % 2 and use_shift) else [0, 0],
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    drop=drop,
                    attn_drop=attn_drop,
                    drop_path=(
                        drop_path[i] if isinstance(drop_path, list) else drop_path
                    ),
                    norm_layer=norm_layer,
                    pretrained_window_size=pretrained_window_size,
                )
                for i in range(depth)
            ]
        )

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def forward(self, x, H, W):
        # calculate attention mask for SW-MSA
        Hp = int(np.ceil(H / self.window_size[0])) * self.window_size[0]
        Wp = int(np.ceil(W / self.window_size[1])) * self.window_size[1]
        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
        h_slices = (
            slice(0, -self.window_size[0]),
            slice(-self.window_size[0], -self.shift_size[0]),
            slice(-self.shift_size[0], None),
        )
        w_slices = (
            slice(0, -self.window_size[1]),
            slice(-self.window_size[1], -self.shift_size[1]),
            slice(-self.shift_size[1], None),
        )
        cnt = 0
        for h in h_slices:
            for w in w_slices:
                img_mask[:, h, w, :] = cnt
                cnt += 1

        mask_windows = window_partition(
            img_mask, self.window_size
        )  # nW, window_size, window_size, 1
        mask_windows = mask_windows.view(-1, self.window_size[0] * self.window_size[1])
        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
            attn_mask == 0, float(0.0)
        )

        x_outs, cls_tokens = [], []
        for blk in self.blocks:
            blk.H, blk.W = H, W
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x, attn_mask)
            else:
                x = blk(x, attn_mask)
            x_outs.append(x)
        if self.downsample is not None:
            x_down = self.downsample(x, H, W)
            Wh, Ww = (H + 1) // 2, (W + 1) // 2
            return x_outs, H, W, x_down, Wh, Ww
        else:
            return x_outs, H, W, x, H, W

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

    def flops(self):
        flops = 0
        for blk in self.blocks:
            flops += blk.flops()
        if self.downsample is not None:
            flops += self.downsample.flops()
        return flops

    def _init_respostnorm(self):
        for blk in self.blocks:
            nn.init.constant_(blk.norm1.bias, 0)
            nn.init.constant_(blk.norm1.weight, 0)
            nn.init.constant_(blk.norm2.bias, 0)
            nn.init.constant_(blk.norm2.weight, 0)


class PatchEmbed(nn.Module):
    r"""Image to Patch Embedding

    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(
        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
    ):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [
            img_size[0] // patch_size[0],
            img_size[1] // patch_size[1],
        ]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(
            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
        )
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        """Forward function."""
        # padding
        _, _, H, W = x.size()
        if W % self.patch_size[1] != 0:
            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
        if H % self.patch_size[0] != 0:
            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))

        x = self.proj(x)  # B C Wh Ww
        if self.norm is not None:
            Wh, Ww = x.size(2), x.size(3)
            x = self.norm(x.flatten(2).transpose(1, 2))
            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)

        return x

    def flops(self):
        Ho, Wo = self.patches_resolution
        flops = (
            Ho
            * Wo
            * self.embed_dim
            * self.in_chans
            * (self.patch_size[0] * self.patch_size[1])
        )
        if self.norm is not None:
            flops += Ho * Wo * self.embed_dim
        return flops


class SwinTransformerV2(nn.Module):
    r"""Swin Transformer
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030

    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
        pretrained_window_sizes (tuple(int)): Pretrained window sizes of each layer.
    """

    def __init__(
        self,
        img_size=224,
        patch_size=4,
        in_chans=3,
        embed_dim=96,
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24],
        window_size=7,
        mlp_ratio=4.0,
        qkv_bias=True,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.1,
        norm_layer=nn.LayerNorm,
        ape=False,
        patch_norm=True,
        use_checkpoint=False,
        use_shift=True,
        pretrained_window_sizes=[0, 0, 0, 0],
        pretrained=None,
        frozen_stages=-1,
        output_idx=[2, 4, 22, 24],
        **kwargs,
    ):
        super().__init__()
        self.num_layers = len(depths)
        self.depths = output_idx
        self.embed_dim = embed_dim
        dims = [embed_dim * 2**i for i in range(len(depths))]
        self.embed_dims = [
            int(dim) for i, dim in enumerate(dims) for _ in range(depths[i])
        ]

        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
        self.mlp_ratio = mlp_ratio
        self.frozen_stages = frozen_stages
        if isinstance(window_size, int):
            window_size = [window_size] * self.num_layers
        if isinstance(use_shift, bool):
            use_shift = [use_shift] * self.num_layers

        # self.mask_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
        # trunc_normal_(self.mask_token, mean=0., std=.02)
        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None,
        )
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution

        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(
                torch.zeros(1, num_patches, embed_dim)
            )
            trunc_normal_(self.absolute_pos_embed, std=0.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [
            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
        ]  # stochastic depth decay rule

        # build layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = BasicLayer(
                dim=int(embed_dim * 2**i_layer),
                input_resolution=[
                    img_size[0] // (2 ** (2 + i_layer)),
                    img_size[1] // (2 ** (2 + i_layer)),
                ],
                depth=depths[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size[i_layer],
                use_shift=use_shift[i_layer],
                mlp_ratio=self.mlp_ratio,
                qkv_bias=qkv_bias,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
                norm_layer=norm_layer,
                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                use_checkpoint=use_checkpoint,
                pretrained_window_size=pretrained_window_sizes[i_layer],
            )
            self.layers.append(layer)

        self.apply(self._init_weights)
        for bly in self.layers:
            bly._init_respostnorm()

        if pretrained is not None:
            pretrained_state = torch.load(pretrained, map_location="cpu")["model"]
            pretrained_state_filtered = load_checkpoint_swin(self, pretrained_state)
            msg = self.load_state_dict(pretrained_state_filtered, strict=False)

        self._freeze_stages()

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            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)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {"absolute_pos_embed"}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {"rpe_mlp", "logit_scale", "relative_position_bias_table", "mask_token"}

    def forward(self, x, mask=None):
        """Forward function."""
        # Add requires_grad_() to all input to support freezing with gradient checkpointing!
        x = self.patch_embed(x.requires_grad_())
        B, Wh, Ww = x.size(0), x.size(2), x.size(3)
        if self.ape:
            # interpolate the position embedding to the corresponding size
            absolute_pos_embed = F.interpolate(
                self.absolute_pos_embed,
                size=(Wh, Ww),
                mode="bicubic",
                align_corners=True,
            )
            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
        else:
            x = x.flatten(2).transpose(1, 2)
        x = self.pos_drop(x)

        # B, L, _ = x.shape
        # if mask is not None:
        #     mask_tokens = self.mask_token.expand(B, L, -1)
        #     mask = mask.flatten(1).unsqueeze(-1).type_as(mask_tokens)
        # else:
        #     mask = torch.zeros_like(x)
        #     mask_tokens = torch.zeros_like(self.mask_token).expand(B, L, -1)
        # x = x * (1. - mask) + mask_tokens * mask

        outs, cls_tokens = [], []
        for i in range(self.num_layers):
            layer = self.layers[i]
            x_outs, H, W, x, Wh, Ww = layer(x.requires_grad_(), Wh, Ww)
            out = [
                x_out.view(-1, H, W, self.num_features[i]).contiguous()
                for x_out in x_outs
            ]
            outs.extend(out)
            cls_token_ = [x.mean(dim=(1, 2)).unsqueeze(1).contiguous() for x in out]
            cls_tokens.extend(cls_token_)
        return outs, cls_tokens

    def train(self, mode=True):
        super().train(mode)
        self._freeze_stages()

    def freeze(self) -> None:
        for module in self.modules():
            module.eval()
        for parameters in self.parameters():
            parameters.requires_grad = False

    def _freeze_stages(self):
        if self.frozen_stages >= 0:
            self.patch_embed.eval()
            for param in self.patch_embed.parameters():
                param.requires_grad = False
            if self.ape:
                self.absolute_pos_embed.requires_grad = False
            self.pos_drop.eval()

        for i in range(1, self.frozen_stages + 1):
            m = self.layers[i - 1]
            m.eval()
            for param in m.parameters():
                param.requires_grad = False

    def flops(self):
        flops = 0
        flops += self.patch_embed.flops()
        for i, layer in enumerate(self.layers):
            flops += layer.flops()
        flops += (
            self.num_features
            * self.patches_resolution[0]
            * self.patches_resolution[1]
            // (2**self.num_layers)
        )
        return flops

    def get_params(self, lr, wd, *args, **kwargs):
        encoder_p, encoder_lr = get_params(self, lr, wd)
        return encoder_p, encoder_lr

    @classmethod
    def build(cls, config):
        obj = globals()[config["name"]](config)
        return obj