swin2_mose/model.py

#
# Source code: https://github.com/IMPLabUniPr/swin2-mose
#
# ----------------------------------------------------------------------------
# https://arxiv.org/abs/2404.18924
# ----------------------------------------------------------------------------

import math
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 swin2_mose.libs import window_reverse, Mlp, window_partition
from swin2_mose.moe import MoE


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., proj_drop=0.,
                 pretrained_window_size=[0, 0],
                 use_lepe=False,
                 use_cpb_bias=True,
                 use_rpe_bias=False):

        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)

        self.use_cpb_bias = use_cpb_bias

        if self.use_cpb_bias:
            print('positional encoder: CPB')
            # mlp to generate continuous relative position bias
            self.cpb_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.use_rpe_bias = use_rpe_bias
        if self.use_rpe_bias:
            print('positional encoder: RPE')
            # define a parameter table of relative position bias
            self.relative_position_bias_table = nn.Parameter(
                torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH

            # 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
            rpe_relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
            self.register_buffer("rpe_relative_position_index", rpe_relative_position_index)

            trunc_normal_(self.relative_position_bias_table, std=.02)

        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)

        self.use_lepe = use_lepe
        if self.use_lepe:
            print('positional encoder: LEPE')
            self.get_v = nn.Conv2d(
                dim, dim, kernel_size=3, stride=1, padding=1, groups=dim)

    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)

        if self.use_lepe:
            lepe = self.lepe_pos(v)

        # 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. / 0.01)).to(self.logit_scale.device)).exp()
        attn = attn * logit_scale

        if self.use_cpb_bias:
            relative_position_bias_table = self.cpb_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 self.use_rpe_bias:
            relative_position_bias = self.relative_position_bias_table[self.rpe_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
            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)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v)

        if self.use_lepe:
            x = x + lepe

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

    def lepe_pos(self, v):
        B, NH, HW, NW = v.shape
        C = NH * NW
        H = W = int(math.sqrt(HW))
        v = v.transpose(-2, -1).contiguous().view(B, C, H, W)
        lepe = self.get_v(v)
        lepe = lepe.reshape(-1, self.num_heads, NW, HW)
        lepe = lepe.permute(0, 1, 3, 2).contiguous()
        return lepe

    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., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0,
                 use_lepe=False,
                 use_cpb_bias=True,
                 MoE_config=None,
                 use_rpe_bias=False):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
            pretrained_window_size=to_2tuple(pretrained_window_size),
            use_lepe=use_lepe,
            use_cpb_bias=use_cpb_bias,
            use_rpe_bias=use_rpe_bias)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)

        if MoE_config is None:
            print('-->>> MLP')
            self.mlp = Mlp(
                in_features=dim, hidden_features=mlp_hidden_dim,
                act_layer=act_layer, drop=drop)
        else:
            print('-->>> MOE')
            print(MoE_config)
            self.mlp = MoE(
                input_size=dim, output_size=dim, hidden_size=mlp_hidden_dim,
                **MoE_config)

        if self.shift_size > 0:
            attn_mask = self.calculate_mask(self.input_resolution)
        else:
            attn_mask = None

        self.register_buffer("attn_mask", attn_mask)

    def calculate_mask(self, x_size):
        # calculate attention mask for SW-MSA
        H, W = x_size
        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))

        return attn_mask

    def forward(self, x, x_size):
        H, W = x_size
        B, L, C = x.shape

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

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

        # 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 * self.window_size, C)  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
        if self.input_resolution == x_size:
            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
        else:
            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))

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

        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
        else:
            x = shifted_x
        x = x.view(B, H * W, C)
        x = shortcut + self.drop_path(self.norm1(x))

        # FFN

        loss_moe = None
        res = self.mlp(x)
        if not torch.is_tensor(res):
            res, loss_moe = res

        x = x + self.drop_path(self.norm2(res))

        return x, loss_moe

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

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        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,
                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
                 pretrained_window_size=0,
                 use_lepe=False,
                 use_cpb_bias=True,
                 MoE_config=None,
                 use_rpe_bias=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
                                 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,
                                 use_lepe=use_lepe,
                                 use_cpb_bias=use_cpb_bias,
                                 MoE_config=MoE_config,
                                 use_rpe_bias=use_rpe_bias)
            for i in range(depth)])

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

    def forward(self, x, x_size):
        loss_moe_all = 0
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x, x_size)
            else:
                x = blk(x, x_size)

            if not torch.is_tensor(x):
                x, loss_moe = x
                loss_moe_all += loss_moe or 0

        if self.downsample is not None:
            x = self.downsample(x)
        return x, loss_moe_all

    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):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        # assert H == self.img_size[0] and W == self.img_size[1],
        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
        if self.norm is not None:
            x = self.norm(x)
        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 RSTB(nn.Module):
    """Residual Swin Transformer Block (RSTB).

    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.
        img_size: Input image size.
        patch_size: Patch size.
        resi_connection: The convolutional block before residual connection.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
                 img_size=224, patch_size=4, resi_connection='1conv',
                 use_lepe=False,
                 use_cpb_bias=True,
                 MoE_config=None,
                 use_rpe_bias=False):
        super(RSTB, self).__init__()

        self.dim = dim
        self.input_resolution = input_resolution

        self.residual_group = BasicLayer(dim=dim,
                                         input_resolution=input_resolution,
                                         depth=depth,
                                         num_heads=num_heads,
                                         window_size=window_size,
                                         mlp_ratio=mlp_ratio,
                                         qkv_bias=qkv_bias,
                                         drop=drop, attn_drop=attn_drop,
                                         drop_path=drop_path,
                                         norm_layer=norm_layer,
                                         downsample=downsample,
                                         use_checkpoint=use_checkpoint,
                                         use_lepe=use_lepe,
                                         use_cpb_bias=use_cpb_bias,
                                         MoE_config=MoE_config,
                                         use_rpe_bias=use_rpe_bias
                                         )

        if resi_connection == '1conv':
            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
        elif resi_connection == '3conv':
            # to save parameters and memory
            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))

        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
            norm_layer=None)

        self.patch_unembed = PatchUnEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
            norm_layer=None)

    def forward(self, x, x_size):
        loss_moe = None
        res = self.residual_group(x, x_size)

        if not torch.is_tensor(res):
            res, loss_moe = res

        res = self.patch_embed(self.conv(self.patch_unembed(res, x_size)))
        return res + x, loss_moe

    def flops(self):
        flops = 0
        flops += self.residual_group.flops()
        H, W = self.input_resolution
        flops += H * W * self.dim * self.dim * 9
        flops += self.patch_embed.flops()
        flops += self.patch_unembed.flops()

        return flops


class PatchUnEmbed(nn.Module):
    r""" Image to Patch Unembedding

    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

    def forward(self, x, x_size):
        B, HW, C = x.shape
        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
        return x

    def flops(self):
        flops = 0
        return flops


class Upsample(nn.Sequential):
    """Upsample module.

    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.
    """

    def __init__(self, scale, num_feat):
        m = []
        if (scale & (scale - 1)) == 0:  # scale = 2^n
            for _ in range(int(math.log(scale, 2))):
                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
                m.append(nn.PixelShuffle(2))
        elif scale == 3:
            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
            m.append(nn.PixelShuffle(3))
        else:
            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
        super(Upsample, self).__init__(*m)

class Upsample_hf(nn.Sequential):
    """Upsample module.

    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.
    """

    def __init__(self, scale, num_feat):
        m = []
        if (scale & (scale - 1)) == 0:  # scale = 2^n
            for _ in range(int(math.log(scale, 2))):
                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
                m.append(nn.PixelShuffle(2))
        elif scale == 3:
            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
            m.append(nn.PixelShuffle(3))
        else:
            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
        super(Upsample_hf, self).__init__(*m)


class UpsampleOneStep(nn.Sequential):
    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
       Used in lightweight SR to save parameters.

    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.

    """

    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
        self.num_feat = num_feat
        self.input_resolution = input_resolution
        m = []
        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
        m.append(nn.PixelShuffle(scale))
        super(UpsampleOneStep, self).__init__(*m)

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.num_feat * 3 * 9
        return flops



class Swin2MoSE(nn.Module):
    r""" Swin2-MoSE

    Args:
        img_size (int | tuple(int)): Input image size. Default 64
        patch_size (int | tuple(int)): Patch size. Default: 1
        in_chans (int): Number of input image channels. Default: 3
        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
        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
        img_range: Image range. 1. or 255.
        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
    """

    def __init__(self, img_size=64, patch_size=1, in_chans=3,
                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
                 window_size=7, mlp_ratio=4., qkv_bias=True,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
                 use_lepe=False,
                 use_cpb_bias=True,
                 MoE_config=None,
                 use_rpe_bias=False,
                 **kwargs):
        super(Swin2MoSE, self).__init__()
        num_in_ch = in_chans
        num_out_ch = in_chans
        num_feat = 64
        self.img_range = img_range
        if in_chans == 3:
            rgb_mean = (0.4488, 0.4371, 0.4040)
            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
        else:
            self.mean = torch.zeros(1, 1, 1, 1)
        self.upscale = upscale
        self.upsampler = upsampler
        self.window_size = window_size

        #####################################################################################################
        ################################### 1, shallow feature extraction ###################################
        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)

        #####################################################################################################
        ################################### 2, deep feature extraction ######################################
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = embed_dim
        self.mlp_ratio = mlp_ratio

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, 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

        # merge non-overlapping patches into image
        self.patch_unembed = PatchUnEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)

        # 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=.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 Residual Swin Transformer blocks (RSTB)
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = RSTB(dim=embed_dim,
                         input_resolution=(patches_resolution[0],
                                           patches_resolution[1]),
                         depth=depths[i_layer],
                         num_heads=num_heads[i_layer],
                         window_size=window_size,
                         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])],  # no impact on SR results
                         norm_layer=norm_layer,
                         downsample=None,
                         use_checkpoint=use_checkpoint,
                         img_size=img_size,
                         patch_size=patch_size,
                         resi_connection=resi_connection,
                         use_lepe=use_lepe,
                         use_cpb_bias=use_cpb_bias,
                         MoE_config=MoE_config,
                         use_rpe_bias=use_rpe_bias,
                         )
            self.layers.append(layer)

        if self.upsampler == 'pixelshuffle_hf':
            self.layers_hf = nn.ModuleList()
            for i_layer in range(self.num_layers):
                layer = RSTB(dim=embed_dim,
                             input_resolution=(patches_resolution[0],
                                               patches_resolution[1]),
                             depth=depths[i_layer],
                             num_heads=num_heads[i_layer],
                             window_size=window_size,
                             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])],  # no impact on SR results
                             norm_layer=norm_layer,
                             downsample=None,
                             use_checkpoint=use_checkpoint,
                             img_size=img_size,
                             patch_size=patch_size,
                             resi_connection=resi_connection,
                             use_lepe=use_lepe,
                             use_cpb_bias=use_cpb_bias,
                             MoE_config=MoE_config,
                             use_rpe_bias=use_rpe_bias
                             )
                self.layers_hf.append(layer)

        self.norm = norm_layer(self.num_features)

        # build the last conv layer in deep feature extraction
        if resi_connection == '1conv':
            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
        elif resi_connection == '3conv':
            # to save parameters and memory
            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))

        #####################################################################################################
        ################################ 3, high quality image reconstruction ################################
        if self.upsampler == 'pixelshuffle':
            # for classical SR
            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
                                                      nn.LeakyReLU(inplace=True))
            self.upsample = Upsample(upscale, num_feat)
            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
        elif self.upsampler == 'pixelshuffle_aux':
            self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
            self.conv_before_upsample = nn.Sequential(
                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
                nn.LeakyReLU(inplace=True))
            self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
            self.conv_after_aux = nn.Sequential(
                nn.Conv2d(3, num_feat, 3, 1, 1),
                nn.LeakyReLU(inplace=True))
            self.upsample = Upsample(upscale, num_feat)
            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

        elif self.upsampler == 'pixelshuffle_hf':
            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
                                                      nn.LeakyReLU(inplace=True))
            self.upsample = Upsample(upscale, num_feat)
            self.upsample_hf = Upsample_hf(upscale, num_feat)
            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
            self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
                                                      nn.LeakyReLU(inplace=True))
            self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
            self.conv_before_upsample_hf = nn.Sequential(
                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
                nn.LeakyReLU(inplace=True))
            self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

        elif self.upsampler == 'pixelshuffledirect':
            # for lightweight SR (to save parameters)
            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
                                            (patches_resolution[0], patches_resolution[1]))
        elif self.upsampler == 'nearest+conv':
            # for real-world SR (less artifacts)
            assert self.upscale == 4, 'only support x4 now.'
            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
                                                      nn.LeakyReLU(inplace=True))
            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
        else:
            # for image denoising and JPEG compression artifact reduction
            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        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)

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

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {'relative_position_bias_table'}

    def check_image_size(self, x):
        _, _, h, w = x.size()
        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
        return x

    def forward_features(self, x):
        x_size = (x.shape[2], x.shape[3])
        x = self.patch_embed(x)
        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)

        loss_moe_all = 0
        for layer in self.layers:
            x = layer(x, x_size)

            if not torch.is_tensor(x):
                x, loss_moe = x
                loss_moe_all += loss_moe or 0

        x = self.norm(x)  # B L C
        x = self.patch_unembed(x, x_size)

        return x, loss_moe_all

    def forward_features_hf(self, x):
        x_size = (x.shape[2], x.shape[3])
        x = self.patch_embed(x)
        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)

        loss_moe_all = 0
        for layer in self.layers_hf:
            x = layer(x, x_size)

            if not torch.is_tensor(x):
                x, loss_moe = x
                loss_moe_all += loss_moe or 0

        x = self.norm(x)  # B L C
        x = self.patch_unembed(x, x_size)

        return x, loss_moe_all

    def forward_backbone(self, x):
        H, W = x.shape[2:]
        x = self.check_image_size(x)

        self.mean = self.mean.type_as(x)
        x = (x - self.mean) * self.img_range

        if self.upsampler == 'pixelshuffledirect':
            # for lightweight SR
            x = self.conv_first(x)

            res = self.forward_features(x)
            if not torch.is_tensor(res):
                res, loss_moe = res

            x = self.conv_after_body(res) + x
        else:
            raise Exception('not implemented yet')

        x = x / self.img_range + self.mean
        return x

    def forward(self, x):
        H, W = x.shape[2:]
        x = self.check_image_size(x)

        self.mean = self.mean.type_as(x)
        x = (x - self.mean) * self.img_range

        loss_moe = 0
        if self.upsampler == 'pixelshuffle':
            # for classical SR
            x = self.conv_first(x)

            res = self.forward_features(x)
            if not torch.is_tensor(res):
                res, loss_moe = res

            x = self.conv_after_body(res) + x
            x = self.conv_before_upsample(x)
            x = self.conv_last(self.upsample(x))
        elif self.upsampler == 'pixelshuffle_aux':
            bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
            bicubic = self.conv_bicubic(bicubic)
            x = self.conv_first(x)

            res = self.forward_features(x)
            if not torch.is_tensor(res):
                res, loss_moe = res

            x = self.conv_after_body(res) + x
            x = self.conv_before_upsample(x)
            aux = self.conv_aux(x) # b, 3, LR_H, LR_W
            x = self.conv_after_aux(aux)
            x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
            x = self.conv_last(x)
            aux = aux / self.img_range + self.mean
        elif self.upsampler == 'pixelshuffle_hf':
            # for classical SR with HF
            x = self.conv_first(x)

            res = self.forward_features(x)
            if not torch.is_tensor(res):
                res, loss_moe = res

            x = self.conv_after_body(res) + x
            x_before = self.conv_before_upsample(x)
            x_out = self.conv_last(self.upsample(x_before))

            x_hf = self.conv_first_hf(x_before)

            res_hf = self.forward_features_hf(x_hf)
            if not torch.is_tensor(res_hf):
                res_hf, loss_moe_hf = res_hf
                loss_moe += loss_moe_hf

            x_hf = self.conv_after_body_hf(res_hf) + x_hf
            x_hf = self.conv_before_upsample_hf(x_hf)
            x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
            x = x_out + x_hf
            x_hf = x_hf / self.img_range + self.mean

        elif self.upsampler == 'pixelshuffledirect':
            # for lightweight SR
            x = self.conv_first(x)

            res = self.forward_features(x)
            if not torch.is_tensor(res):
                res, loss_moe = res

            x = self.conv_after_body(res) + x
            x = self.upsample(x)
        elif self.upsampler == 'nearest+conv':
            # for real-world SR
            x = self.conv_first(x)

            res = self.forward_features(x)
            if not torch.is_tensor(res):
                res, loss_moe = res

            x = self.conv_after_body(res) + x
            x = self.conv_before_upsample(x)
            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
            x = self.conv_last(self.lrelu(self.conv_hr(x)))
        else:
            # for image denoising and JPEG compression artifact reduction
            x_first = self.conv_first(x)

            res = self.forward_features(x_first)
            if not torch.is_tensor(res):
                res, loss_moe = res

            res = self.conv_after_body(res) + x_first
            x = x + self.conv_last(res)

        x = x / self.img_range + self.mean
        if self.upsampler == "pixelshuffle_aux":
            return x[:, :, :H*self.upscale, :W*self.upscale], aux, loss_moe

        elif self.upsampler == "pixelshuffle_hf":
            x_out = x_out / self.img_range + self.mean
            return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale], loss_moe

        else:
            return x[:, :, :H*self.upscale, :W*self.upscale], loss_moe

    def flops(self):
        flops = 0
        H, W = self.patches_resolution
        flops += H * W * 3 * self.embed_dim * 9
        flops += self.patch_embed.flops()
        for i, layer in enumerate(self.layers):
            flops += layer.flops()
        flops += H * W * 3 * self.embed_dim * self.embed_dim
        flops += self.upsample.flops()
        return flops