diff --git "a/SIFR_models/flare7kpp/model.py" "b/SIFR_models/flare7kpp/model.py"
new file mode 100755--- /dev/null
+++ "b/SIFR_models/flare7kpp/model.py"
@@ -0,0 +1,3066 @@
+"""
+## Uformer: A General U-Shaped Transformer for Image Restoration
+## Zhendong Wang, Xiaodong Cun, Jianmin Bao, Jianzhuang Liu
+## https://arxiv.org/abs/2106.03106
+"""
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+import math
+import numpy as np
+import time
+from torch import einsum
+
+# from basicsr.utils.registry import ARCH_REGISTRY
+
+
+#########################################
+class ConvBlock(nn.Module):
+    def __init__(self, in_channel, out_channel, strides=1):
+        super(ConvBlock, self).__init__()
+        self.strides = strides
+        self.in_channel = in_channel
+        self.out_channel = out_channel
+        self.block = nn.Sequential(
+            nn.Conv2d(
+                in_channel, out_channel, kernel_size=3, stride=strides, padding=1
+            ),
+            nn.LeakyReLU(inplace=True),
+            nn.Conv2d(
+                out_channel, out_channel, kernel_size=3, stride=strides, padding=1
+            ),
+            nn.LeakyReLU(inplace=True),
+        )
+        self.conv11 = nn.Conv2d(
+            in_channel, out_channel, kernel_size=1, stride=strides, padding=0
+        )
+
+    def forward(self, x):
+        out1 = self.block(x)
+        out2 = self.conv11(x)
+        out = out1 + out2
+        return out
+
+    def flops(self, H, W):
+        flops = (
+            H * W * self.in_channel * self.out_channel * (3 * 3 + 1)
+            + H * W * self.out_channel * self.out_channel * 3 * 3
+        )
+        return flops
+
+
+class UNet(nn.Module):
+    def __init__(self, block=ConvBlock, dim=32):
+        super(UNet, self).__init__()
+
+        self.dim = dim
+        self.ConvBlock1 = ConvBlock(3, dim, strides=1)
+        self.pool1 = nn.Conv2d(dim, dim, kernel_size=4, stride=2, padding=1)
+
+        self.ConvBlock2 = block(dim, dim * 2, strides=1)
+        self.pool2 = nn.Conv2d(dim * 2, dim * 2, kernel_size=4, stride=2, padding=1)
+
+        self.ConvBlock3 = block(dim * 2, dim * 4, strides=1)
+        self.pool3 = nn.Conv2d(dim * 4, dim * 4, kernel_size=4, stride=2, padding=1)
+
+        self.ConvBlock4 = block(dim * 4, dim * 8, strides=1)
+        self.pool4 = nn.Conv2d(dim * 8, dim * 8, kernel_size=4, stride=2, padding=1)
+
+        self.ConvBlock5 = block(dim * 8, dim * 16, strides=1)
+
+        self.upv6 = nn.ConvTranspose2d(dim * 16, dim * 8, 2, stride=2)
+        self.ConvBlock6 = block(dim * 16, dim * 8, strides=1)
+
+        self.upv7 = nn.ConvTranspose2d(dim * 8, dim * 4, 2, stride=2)
+        self.ConvBlock7 = block(dim * 8, dim * 4, strides=1)
+
+        self.upv8 = nn.ConvTranspose2d(dim * 4, dim * 2, 2, stride=2)
+        self.ConvBlock8 = block(dim * 4, dim * 2, strides=1)
+
+        self.upv9 = nn.ConvTranspose2d(dim * 2, dim, 2, stride=2)
+        self.ConvBlock9 = block(dim * 2, dim, strides=1)
+
+        self.conv10 = nn.Conv2d(dim, 3, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        conv1 = self.ConvBlock1(x)
+        pool1 = self.pool1(conv1)
+
+        conv2 = self.ConvBlock2(pool1)
+        pool2 = self.pool2(conv2)
+
+        conv3 = self.ConvBlock3(pool2)
+        pool3 = self.pool3(conv3)
+
+        conv4 = self.ConvBlock4(pool3)
+        pool4 = self.pool4(conv4)
+
+        conv5 = self.ConvBlock5(pool4)
+
+        up6 = self.upv6(conv5)
+        up6 = torch.cat([up6, conv4], 1)
+        conv6 = self.ConvBlock6(up6)
+
+        up7 = self.upv7(conv6)
+        up7 = torch.cat([up7, conv3], 1)
+        conv7 = self.ConvBlock7(up7)
+
+        up8 = self.upv8(conv7)
+        up8 = torch.cat([up8, conv2], 1)
+        conv8 = self.ConvBlock8(up8)
+
+        up9 = self.upv9(conv8)
+        up9 = torch.cat([up9, conv1], 1)
+        conv9 = self.ConvBlock9(up9)
+
+        conv10 = self.conv10(conv9)
+        out = x + conv10
+
+        return out
+
+    def flops(self, H, W):
+        flops = 0
+        flops += self.ConvBlock1.flops(H, W)
+        flops += H / 2 * W / 2 * self.dim * self.dim * 4 * 4
+        flops += self.ConvBlock2.flops(H / 2, W / 2)
+        flops += H / 4 * W / 4 * self.dim * 2 * self.dim * 2 * 4 * 4
+        flops += self.ConvBlock3.flops(H / 4, W / 4)
+        flops += H / 8 * W / 8 * self.dim * 4 * self.dim * 4 * 4 * 4
+        flops += self.ConvBlock4.flops(H / 8, W / 8)
+        flops += H / 16 * W / 16 * self.dim * 8 * self.dim * 8 * 4 * 4
+
+        flops += self.ConvBlock5.flops(H / 16, W / 16)
+
+        flops += H / 8 * W / 8 * self.dim * 16 * self.dim * 8 * 2 * 2
+        flops += self.ConvBlock6.flops(H / 8, W / 8)
+        flops += H / 4 * W / 4 * self.dim * 8 * self.dim * 4 * 2 * 2
+        flops += self.ConvBlock7.flops(H / 4, W / 4)
+        flops += H / 2 * W / 2 * self.dim * 4 * self.dim * 2 * 2 * 2
+        flops += self.ConvBlock8.flops(H / 2, W / 2)
+        flops += H * W * self.dim * 2 * self.dim * 2 * 2
+        flops += self.ConvBlock9.flops(H, W)
+
+        flops += H * W * self.dim * 3 * 3 * 3
+        return flops
+
+
+#########################################
+class PosCNN(nn.Module):
+    def __init__(self, in_chans, embed_dim=768, s=1):
+        super(PosCNN, self).__init__()
+        self.proj = nn.Sequential(
+            nn.Conv2d(in_chans, embed_dim, 3, s, 1, bias=True, groups=embed_dim)
+        )
+        self.s = s
+
+    def forward(self, x, H=None, W=None):
+        B, N, C = x.shape
+        H = H or int(math.sqrt(N))
+        W = W or int(math.sqrt(N))
+        feat_token = x
+        cnn_feat = feat_token.transpose(1, 2).view(B, C, H, W)
+        if self.s == 1:
+            x = self.proj(cnn_feat) + cnn_feat
+        else:
+            x = self.proj(cnn_feat)
+        x = x.flatten(2).transpose(1, 2)
+        return x
+
+    def no_weight_decay(self):
+        return ["proj.%d.weight" % i for i in range(4)]
+
+
+class SELayer(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(SELayer, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool1d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction, bias=False),
+            nn.ReLU(inplace=True),
+            nn.Linear(channel // reduction, channel, bias=False),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, x):  # x: [B, N, C]
+        x = torch.transpose(x, 1, 2)  # [B, C, N]
+        b, c, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1)
+        x = x * y.expand_as(x)
+        x = torch.transpose(x, 1, 2)  # [B, N, C]
+        return x
+
+
+class SepConv2d(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        act_layer=nn.ReLU,
+    ):
+        super(SepConv2d, self).__init__()
+        self.depthwise = torch.nn.Conv2d(
+            in_channels,
+            in_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=in_channels,
+        )
+        self.pointwise = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1)
+        self.act_layer = act_layer() if act_layer is not None else nn.Identity()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.stride = stride
+
+    def forward(self, x):
+        x = self.depthwise(x)
+        x = self.act_layer(x)
+        x = self.pointwise(x)
+        return x
+
+    def flops(self, H, W):
+        flops = 0
+        flops += H * W * self.in_channels * self.kernel_size**2 / self.stride**2
+        flops += H * W * self.in_channels * self.out_channels
+        return flops
+
+
+#########################################
+######## Embedding for q,k,v ########
+class ConvProjection(nn.Module):
+    def __init__(
+        self,
+        dim,
+        heads=8,
+        dim_head=64,
+        kernel_size=3,
+        q_stride=1,
+        k_stride=1,
+        v_stride=1,
+        dropout=0.0,
+        last_stage=False,
+        bias=True,
+    ):
+
+        super().__init__()
+
+        inner_dim = dim_head * heads
+        self.heads = heads
+        pad = (kernel_size - q_stride) // 2
+        self.to_q = SepConv2d(dim, inner_dim, kernel_size, q_stride, pad, bias)
+        self.to_k = SepConv2d(dim, inner_dim, kernel_size, k_stride, pad, bias)
+        self.to_v = SepConv2d(dim, inner_dim, kernel_size, v_stride, pad, bias)
+
+    def forward(self, x, attn_kv=None):
+        b, n, c, h = *x.shape, self.heads
+        l = int(math.sqrt(n))
+        w = int(math.sqrt(n))
+
+        attn_kv = x if attn_kv is None else attn_kv
+        x = rearrange(x, "b (l w) c -> b c l w", l=l, w=w)
+        attn_kv = rearrange(attn_kv, "b (l w) c -> b c l w", l=l, w=w)
+        # print(attn_kv)
+        q = self.to_q(x)
+        q = rearrange(q, "b (h d) l w -> b h (l w) d", h=h)
+
+        k = self.to_k(attn_kv)
+        v = self.to_v(attn_kv)
+        k = rearrange(k, "b (h d) l w -> b h (l w) d", h=h)
+        v = rearrange(v, "b (h d) l w -> b h (l w) d", h=h)
+        return q, k, v
+
+    def flops(self, H, W):
+        flops = 0
+        flops += self.to_q.flops(H, W)
+        flops += self.to_k.flops(H, W)
+        flops += self.to_v.flops(H, W)
+        return flops
+
+
+class LinearProjection(nn.Module):
+    def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, bias=True):
+        super().__init__()
+        inner_dim = dim_head * heads
+        self.heads = heads
+        self.to_q = nn.Linear(dim, inner_dim, bias=bias)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=bias)
+        self.dim = dim
+        self.inner_dim = inner_dim
+
+    def forward(self, x, attn_kv=None):
+        B_, N, C = x.shape
+        attn_kv = x if attn_kv is None else attn_kv
+        q = (
+            self.to_q(x)
+            .reshape(B_, N, 1, self.heads, C // self.heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+        kv = (
+            self.to_kv(attn_kv)
+            .reshape(B_, N, 2, self.heads, C // self.heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q = q[0]
+        k, v = kv[0], kv[1]
+        return q, k, v
+
+    def flops(self, H, W):
+        flops = H * W * self.dim * self.inner_dim * 3
+        return flops
+
+
+class LinearProjection_Concat_kv(nn.Module):
+    def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, bias=True):
+        super().__init__()
+        inner_dim = dim_head * heads
+        self.heads = heads
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=bias)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=bias)
+        self.dim = dim
+        self.inner_dim = inner_dim
+
+    def forward(self, x, attn_kv=None):
+        B_, N, C = x.shape
+        attn_kv = x if attn_kv is None else attn_kv
+        qkv_dec = (
+            self.to_qkv(x)
+            .reshape(B_, N, 3, self.heads, C // self.heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+        kv_enc = (
+            self.to_kv(attn_kv)
+            .reshape(B_, N, 2, self.heads, C // self.heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k_d, v_d = (
+            qkv_dec[0],
+            qkv_dec[1],
+            qkv_dec[2],
+        )  # make torchscript happy (cannot use tensor as tuple)
+        k_e, v_e = kv_enc[0], kv_enc[1]
+        k = torch.cat((k_d, k_e), dim=2)
+        v = torch.cat((v_d, v_e), dim=2)
+        return q, k, v
+
+    def flops(self, H, W):
+        flops = H * W * self.dim * self.inner_dim * 5
+        return flops
+
+
+#########################################
+########### window-based self-attention #############
+class WindowAttention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        win_size,
+        num_heads,
+        token_projection="linear",
+        qkv_bias=True,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+        se_layer=False,
+    ):
+
+        super().__init__()
+        self.dim = dim
+        self.win_size = win_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * win_size[0] - 1) * (2 * win_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.win_size[0])  # [0,...,Wh-1]
+        coords_w = torch.arange(self.win_size[1])  # [0,...,Ww-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.win_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.win_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.win_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=qkv_bias)
+        if token_projection == "conv":
+            self.qkv = ConvProjection(dim, num_heads, dim // num_heads, bias=qkv_bias)
+        elif token_projection == "linear_concat":
+            self.qkv = LinearProjection_Concat_kv(
+                dim, num_heads, dim // num_heads, bias=qkv_bias
+            )
+        else:
+            self.qkv = LinearProjection(dim, num_heads, dim // num_heads, bias=qkv_bias)
+
+        self.token_projection = token_projection
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.se_layer = SELayer(dim) if se_layer else nn.Identity()
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=0.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, attn_kv=None, mask=None):
+        B_, N, C = x.shape
+        q, k, v = self.qkv(x, attn_kv)
+        q = q * self.scale
+        attn = q @ k.transpose(-2, -1)
+
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)
+        ].view(
+            self.win_size[0] * self.win_size[1], self.win_size[0] * self.win_size[1], -1
+        )  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(
+            2, 0, 1
+        ).contiguous()  # nH, Wh*Ww, Wh*Ww
+        ratio = attn.size(-1) // relative_position_bias.size(-1)
+        relative_position_bias = repeat(
+            relative_position_bias, "nH l c -> nH l (c d)", d=ratio
+        )
+
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            mask = repeat(mask, "nW m n -> nW m (n d)", d=ratio)
+            attn = attn.view(
+                B_ // nW, nW, self.num_heads, N, N * ratio
+            ) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N * ratio)
+            attn = self.softmax(attn)
+        else:
+            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.se_layer(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, win_size={self.win_size}, num_heads={self.num_heads}"
+
+    def flops(self, H, W):
+        # calculate flops for 1 window with token length of N
+        # print(N, self.dim)
+        flops = 0
+        N = self.win_size[0] * self.win_size[1]
+        nW = H * W / N
+        # qkv = self.qkv(x)
+        # flops += N * self.dim * 3 * self.dim
+        flops += self.qkv.flops(H, W)
+        # attn = (q @ k.transpose(-2, -1))
+        if self.token_projection != "linear_concat":
+            flops += nW * self.num_heads * N * (self.dim // self.num_heads) * N
+            #  x = (attn @ v)
+            flops += nW * self.num_heads * N * N * (self.dim // self.num_heads)
+        else:
+            flops += nW * self.num_heads * N * (self.dim // self.num_heads) * N * 2
+            #  x = (attn @ v)
+            flops += nW * self.num_heads * N * N * 2 * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += nW * N * self.dim * self.dim
+        print("W-MSA:{%.2f}" % (flops / 1e9))
+        return flops
+
+
+#########################################
+########### feed-forward network #############
+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)
+        self.in_features = in_features
+        self.hidden_features = hidden_features
+        self.out_features = out_features
+
+    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 flops(self, H, W):
+        flops = 0
+        # fc1
+        flops += H * W * self.in_features * self.hidden_features
+        # fc2
+        flops += H * W * self.hidden_features * self.out_features
+        print("MLP:{%.2f}" % (flops / 1e9))
+        return flops
+
+
+class LeFF(nn.Module):
+    def __init__(self, dim=32, hidden_dim=128, act_layer=nn.GELU, drop=0.0):
+        super().__init__()
+        self.linear1 = nn.Sequential(nn.Linear(dim, hidden_dim), act_layer())
+        self.dwconv = nn.Sequential(
+            nn.Conv2d(
+                hidden_dim,
+                hidden_dim,
+                groups=hidden_dim,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+            ),
+            act_layer(),
+        )
+        self.linear2 = nn.Sequential(nn.Linear(hidden_dim, dim))
+        self.dim = dim
+        self.hidden_dim = hidden_dim
+
+    def forward(self, x):
+        # bs x hw x c
+        bs, hw, c = x.size()
+        hh = int(math.sqrt(hw))
+
+        x = self.linear1(x)
+
+        # spatial restore
+        x = rearrange(x, " b (h w) (c) -> b c h w ", h=hh, w=hh)
+        # bs,hidden_dim,32x32
+
+        x = self.dwconv(x)
+
+        # flaten
+        x = rearrange(x, " b c h w -> b (h w) c", h=hh, w=hh)
+
+        x = self.linear2(x)
+
+        return x
+
+    def flops(self, H, W):
+        flops = 0
+        # fc1
+        flops += H * W * self.dim * self.hidden_dim
+        # dwconv
+        flops += H * W * self.hidden_dim * 3 * 3
+        # fc2
+        flops += H * W * self.hidden_dim * self.dim
+        print("LeFF:{%.2f}" % (flops / 1e9))
+        return flops
+
+
+#########################################
+########### window operation#############
+def window_partition(x, win_size, dilation_rate=1):
+    B, H, W, C = x.shape
+    if dilation_rate != 1:
+        x = x.permute(0, 3, 1, 2)  # B, C, H, W
+        assert type(dilation_rate) is int, "dilation_rate should be a int"
+        x = F.unfold(
+            x,
+            kernel_size=win_size,
+            dilation=dilation_rate,
+            padding=4 * (dilation_rate - 1),
+            stride=win_size,
+        )  # B, C*Wh*Ww, H/Wh*W/Ww
+        windows = (
+            x.permute(0, 2, 1).contiguous().view(-1, C, win_size, win_size)
+        )  # B' ,C ,Wh ,Ww
+        windows = windows.permute(0, 2, 3, 1).contiguous()  # B' ,Wh ,Ww ,C
+    else:
+        x = x.view(B, H // win_size, win_size, W // win_size, win_size, C)
+        windows = (
+            x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, win_size, win_size, C)
+        )  # B' ,Wh ,Ww ,C
+    return windows
+
+
+def window_reverse(windows, win_size, H, W, dilation_rate=1):
+    # B' ,Wh ,Ww ,C
+    B = int(windows.shape[0] / (H * W / win_size / win_size))
+    x = windows.view(B, H // win_size, W // win_size, win_size, win_size, -1)
+    if dilation_rate != 1:
+        x = windows.permute(0, 5, 3, 4, 1, 2).contiguous()  # B, C*Wh*Ww, H/Wh*W/Ww
+        x = F.fold(
+            x,
+            (H, W),
+            kernel_size=win_size,
+            dilation=dilation_rate,
+            padding=4 * (dilation_rate - 1),
+            stride=win_size,
+        )
+    else:
+        x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+#########################################
+# Downsample Block
+class Downsample(nn.Module):
+    def __init__(self, in_channel, out_channel):
+        super(Downsample, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channel, out_channel, kernel_size=4, stride=2, padding=1),
+        )
+        self.in_channel = in_channel
+        self.out_channel = out_channel
+
+    def forward(self, x):
+        B, L, C = x.shape
+        # import pdb;pdb.set_trace()
+        H = int(math.sqrt(L))
+        W = int(math.sqrt(L))
+        x = x.transpose(1, 2).contiguous().view(B, C, H, W)
+        out = self.conv(x).flatten(2).transpose(1, 2).contiguous()  # B H*W C
+        return out
+
+    def flops(self, H, W):
+        flops = 0
+        # conv
+        flops += H / 2 * W / 2 * self.in_channel * self.out_channel * 4 * 4
+        print("Downsample:{%.2f}" % (flops / 1e9))
+        return flops
+
+
+# Upsample Block
+class Upsample(nn.Module):
+    def __init__(self, in_channel, out_channel):
+        super(Upsample, self).__init__()
+        self.deconv = nn.Sequential(
+            nn.ConvTranspose2d(in_channel, out_channel, kernel_size=2, stride=2),
+        )
+        self.in_channel = in_channel
+        self.out_channel = out_channel
+
+    def forward(self, x):
+        B, L, C = x.shape
+        H = int(math.sqrt(L))
+        W = int(math.sqrt(L))
+        x = x.transpose(1, 2).contiguous().view(B, C, H, W)
+        out = self.deconv(x).flatten(2).transpose(1, 2).contiguous()  # B H*W C
+        return out
+
+    def flops(self, H, W):
+        flops = 0
+        # conv
+        flops += H * 2 * W * 2 * self.in_channel * self.out_channel * 2 * 2
+        print("Upsample:{%.2f}" % (flops / 1e9))
+        return flops
+
+
+# Input Projection
+class InputProj(nn.Module):
+    def __init__(
+        self,
+        in_channel=3,
+        out_channel=64,
+        kernel_size=3,
+        stride=1,
+        norm_layer=None,
+        act_layer=nn.LeakyReLU,
+    ):
+        super().__init__()
+        self.proj = nn.Sequential(
+            nn.Conv2d(
+                in_channel,
+                out_channel,
+                kernel_size=3,
+                stride=stride,
+                padding=kernel_size // 2,
+            ),
+            act_layer(inplace=True),
+        )
+        if norm_layer is not None:
+            self.norm = norm_layer(out_channel)
+        else:
+            self.norm = None
+        self.in_channel = in_channel
+        self.out_channel = out_channel
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        x = self.proj(x).flatten(2).transpose(1, 2).contiguous()  # B H*W C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self, H, W):
+        flops = 0
+        # conv
+        flops += H * W * self.in_channel * self.out_channel * 3 * 3
+
+        if self.norm is not None:
+            flops += H * W * self.out_channel
+        print("Input_proj:{%.2f}" % (flops / 1e9))
+        return flops
+
+
+# Output Projection
+class OutputProj(nn.Module):
+    def __init__(
+        self,
+        in_channel=64,
+        out_channel=3,
+        kernel_size=3,
+        stride=1,
+        norm_layer=None,
+        act_layer=None,
+    ):
+        super().__init__()
+        self.proj = nn.Sequential(
+            nn.Conv2d(
+                in_channel,
+                out_channel,
+                kernel_size=3,
+                stride=stride,
+                padding=kernel_size // 2,
+            ),
+        )
+        if act_layer is not None:
+            self.proj.add_module(act_layer(inplace=True))
+        if norm_layer is not None:
+            self.norm = norm_layer(out_channel)
+        else:
+            self.norm = None
+        self.in_channel = in_channel
+        self.out_channel = out_channel
+
+    def forward(self, x):
+        B, L, C = x.shape
+        H = int(math.sqrt(L))
+        W = int(math.sqrt(L))
+        x = x.transpose(1, 2).view(B, C, H, W)
+        x = self.proj(x)
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self, H, W):
+        flops = 0
+        # conv
+        flops += H * W * self.in_channel * self.out_channel * 3 * 3
+
+        if self.norm is not None:
+            flops += H * W * self.out_channel
+        print("Output_proj:{%.2f}" % (flops / 1e9))
+        return flops
+
+
+#########################################
+########### LeWinTransformer #############
+class LeWinTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim,
+        input_resolution,
+        num_heads,
+        win_size=8,
+        shift_size=0,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        act_layer=nn.GELU,
+        norm_layer=nn.LayerNorm,
+        token_projection="linear",
+        token_mlp="leff",
+        se_layer=False,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.win_size = win_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        self.token_mlp = token_mlp
+        if min(self.input_resolution) <= self.win_size:
+            self.shift_size = 0
+            self.win_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.win_size, "shift_size must in 0-win_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim,
+            win_size=to_2tuple(self.win_size),
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+            token_projection=token_projection,
+            se_layer=se_layer,
+        )
+
+        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 token_mlp == "ffn"
+            else LeFF(dim, mlp_hidden_dim, act_layer=act_layer, drop=drop)
+        )
+
+    def extra_repr(self) -> str:
+        return (
+            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
+            f"win_size={self.win_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+        )
+
+    def forward(self, x, mask=None):
+        B, L, C = x.shape
+        H = int(math.sqrt(L))
+        W = int(math.sqrt(L))
+
+        ## input mask
+        if mask != None:
+            input_mask = F.interpolate(mask, size=(H, W)).permute(0, 2, 3, 1)
+            input_mask_windows = window_partition(
+                input_mask, self.win_size
+            )  # nW, win_size, win_size, 1
+            attn_mask = input_mask_windows.view(
+                -1, self.win_size * self.win_size
+            )  # nW, win_size*win_size
+            attn_mask = attn_mask.unsqueeze(2) * attn_mask.unsqueeze(
+                1
+            )  # nW, win_size*win_size, win_size*win_size
+            attn_mask = attn_mask.masked_fill(
+                attn_mask != 0, float(-100.0)
+            ).masked_fill(attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+
+        ## shift mask
+        if self.shift_size > 0:
+            # calculate attention mask for SW-MSA
+            shift_mask = torch.zeros((1, H, W, 1)).type_as(x)
+            h_slices = (
+                slice(0, -self.win_size),
+                slice(-self.win_size, -self.shift_size),
+                slice(-self.shift_size, None),
+            )
+            w_slices = (
+                slice(0, -self.win_size),
+                slice(-self.win_size, -self.shift_size),
+                slice(-self.shift_size, None),
+            )
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    shift_mask[:, h, w, :] = cnt
+                    cnt += 1
+            shift_mask_windows = window_partition(
+                shift_mask, self.win_size
+            )  # nW, win_size, win_size, 1
+            shift_mask_windows = shift_mask_windows.view(
+                -1, self.win_size * self.win_size
+            )  # nW, win_size*win_size
+            shift_attn_mask = shift_mask_windows.unsqueeze(
+                1
+            ) - shift_mask_windows.unsqueeze(
+                2
+            )  # nW, win_size*win_size, win_size*win_size
+            shift_attn_mask = shift_attn_mask.masked_fill(
+                shift_attn_mask != 0, float(-100.0)
+            ).masked_fill(shift_attn_mask == 0, float(0.0))
+            attn_mask = (
+                attn_mask + shift_attn_mask
+                if attn_mask is not None
+                else shift_attn_mask
+            )
+
+        shortcut = x
+        x = self.norm1(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.win_size
+        )  # nW*B, win_size, win_size, C  N*C->C
+        x_windows = x_windows.view(
+            -1, self.win_size * self.win_size, C
+        )  # nW*B, win_size*win_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(
+            x_windows, mask=attn_mask
+        )  # nW*B, win_size*win_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.win_size, self.win_size, C)
+        shifted_x = window_reverse(attn_windows, self.win_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)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        del attn_mask
+        return x
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        flops += self.attn.flops(H, W)
+        # norm2
+        flops += self.dim * H * W
+        # mlp
+        flops += self.mlp.flops(H, W)
+        print("LeWin:{%.2f}" % (flops / 1e9))
+        return flops
+
+
+########### LeWinTransformer_Cross #############
+class LeWinTransformer_Cross(nn.Module):
+    def __init__(
+        self,
+        dim,
+        input_resolution,
+        num_heads,
+        win_size=8,
+        shift_size=0,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        act_layer=nn.GELU,
+        norm_layer=nn.LayerNorm,
+        token_projection="linear",
+        token_mlp="ffn",
+    ):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.win_size = win_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.win_size:
+            self.shift_size = 0
+            self.win_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.win_size, "shift_size must in 0-win_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim,
+            win_size=to_2tuple(self.win_size),
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+            token_projection=token_projection,
+        )
+
+        self.norm2 = norm_layer(dim)
+        self.norm_kv = norm_layer(dim)
+        self.cross_attn = WindowAttention(
+            dim,
+            win_size=to_2tuple(self.win_size),
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+            token_projection=token_projection,
+        )
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.norm3 = 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 token_mlp == "ffn"
+            else LeFF(dim, mlp_hidden_dim, act_layer=act_layer, drop=drop)
+        )
+
+    def extra_repr(self) -> str:
+        return (
+            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
+            f"win_size={self.win_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+        )
+
+    def forward(self, x, attn_kv=None, mask=None):
+        B, L, C = x.shape
+        H = int(math.sqrt(L))
+        W = int(math.sqrt(L))
+
+        ## input mask
+        if mask != None:
+            input_mask = F.interpolate(mask, size=(H, W)).permute(0, 2, 3, 1)
+            input_mask_windows = window_partition(
+                input_mask, self.win_size
+            )  # nW, win_size, win_size, 1
+            attn_mask = input_mask_windows.view(
+                -1, self.win_size * self.win_size
+            )  # nW, win_size*win_size
+            attn_mask = attn_mask.unsqueeze(2) * attn_mask.unsqueeze(
+                1
+            )  # nW, win_size*win_size, win_size*win_size
+            attn_mask = attn_mask.masked_fill(
+                attn_mask != 0, float(-100.0)
+            ).masked_fill(attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+
+        ## shift mask
+        if self.shift_size > 0:
+            # calculate attention mask for SW-MSA
+            shift_mask = torch.zeros((1, H, W, 1)).type_as(x)
+            h_slices = (
+                slice(0, -self.win_size),
+                slice(-self.win_size, -self.shift_size),
+                slice(-self.shift_size, None),
+            )
+            w_slices = (
+                slice(0, -self.win_size),
+                slice(-self.win_size, -self.shift_size),
+                slice(-self.shift_size, None),
+            )
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    shift_mask[:, h, w, :] = cnt
+                    cnt += 1
+            shift_mask_windows = window_partition(
+                shift_mask, self.win_size
+            )  # nW, win_size, win_size, 1
+            shift_mask_windows = shift_mask_windows.view(
+                -1, self.win_size * self.win_size
+            )  # nW, win_size*win_size
+            shift_attn_mask = shift_mask_windows.unsqueeze(
+                1
+            ) - shift_mask_windows.unsqueeze(
+                2
+            )  # nW, win_size*win_size, win_size*win_size
+            shift_attn_mask = shift_attn_mask.masked_fill(
+                shift_attn_mask != 0, float(-100.0)
+            ).masked_fill(shift_attn_mask == 0, float(0.0))
+            attn_mask = (
+                attn_mask + shift_attn_mask
+                if attn_mask is not None
+                else shift_attn_mask
+            )
+
+        attn_kv = attn_kv.view(B, H, W, C)
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_kv = torch.roll(
+                attn_kv, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
+            )
+        else:
+            shifted_kv = attn_kv
+        # partition windows
+        attn_kv_windows = window_partition(
+            shifted_kv, self.win_size
+        )  # nW*B, win_size, win_size, C
+        attn_kv_windows = attn_kv_windows.view(
+            -1, self.win_size * self.win_size, C
+        )  # nW*B, win_size*win_size, C
+
+        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.win_size
+        )  # nW*B, win_size, win_size, C
+        x_windows = x_windows.view(
+            -1, self.win_size * self.win_size, C
+        )  # nW*B, win_size*win_size, C
+
+        ### multi-head self-attention
+        shortcut1 = x_windows
+        # prenorm
+        x_windows = self.norm1(x_windows)
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(
+            x_windows, mask=attn_mask
+        )  # nW*B, win_size*win_size, C
+        x_windows = shortcut1 + self.drop_path(attn_windows)
+
+        ### multi-head cross-attention
+        shortcut2 = x_windows
+        # prenorm
+        x_windows = self.norm2(x_windows)
+        attn_kv_windows = self.norm_kv(attn_kv_windows)
+        # W-MCA/SW-MCA
+        attn_windows = self.cross_attn(
+            x_windows, attn_kv=attn_kv_windows, mask=attn_mask
+        )  # nW*B, win_size*win_size, C
+        attn_windows = shortcut2 + self.drop_path(attn_windows)
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.win_size, self.win_size, C)
+        shifted_x = window_reverse(attn_windows, self.win_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)
+        # FFN
+        x = x + self.drop_path(self.mlp(self.norm3(x)))
+        del attn_mask
+        return x
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        flops += self.attn.flops(H, W)
+        flops += self.cross_attn.flops(H, W)
+        # norm2
+        flops += self.dim * H * W
+        # mlp
+        flops += self.mlp.flops(H, W)
+        print("LeWin:{%.2f}" % (flops / 1e9))
+        return flops
+
+
+########### LeWinTransformer_CatCross #############
+class LeWinTransformer_CatCross(nn.Module):
+    def __init__(
+        self,
+        dim,
+        input_resolution,
+        num_heads,
+        win_size=8,
+        shift_size=0,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        act_layer=nn.GELU,
+        norm_layer=nn.LayerNorm,
+        token_projection="linear",
+        token_mlp="ffn",
+    ):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.win_size = win_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.win_size:
+            self.shift_size = 0
+            self.win_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.win_size, "shift_size must in 0-win_size"
+
+        self.norm1 = norm_layer(dim)
+        self.norm_kv = norm_layer(dim)
+        self.cross_attn = WindowAttention(
+            dim,
+            win_size=to_2tuple(self.win_size),
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+            token_projection="linear_concat",
+        )
+
+        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 token_mlp == "ffn"
+            else LeFF(dim, mlp_hidden_dim, act_layer=act_layer, drop=drop)
+        )
+
+    def extra_repr(self) -> str:
+        return (
+            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
+            f"win_size={self.win_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+        )
+
+    def forward(self, x, attn_kv=None, mask=None):
+        B, L, C = x.shape
+        H = int(math.sqrt(L))
+        W = int(math.sqrt(L))
+
+        ## input mask
+        if mask != None:
+            input_mask = F.interpolate(mask, size=(H, W)).permute(0, 2, 3, 1)
+            input_mask_windows = window_partition(
+                input_mask, self.win_size
+            )  # nW, win_size, win_size, 1
+            attn_mask = input_mask_windows.view(
+                -1, self.win_size * self.win_size
+            )  # nW, win_size*win_size
+            attn_mask = attn_mask.unsqueeze(2) * attn_mask.unsqueeze(
+                1
+            )  # nW, win_size*win_size, win_size*win_size
+            attn_mask = attn_mask.masked_fill(
+                attn_mask != 0, float(-100.0)
+            ).masked_fill(attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+
+        ## shift mask
+        if self.shift_size > 0:
+            # calculate attention mask for SW-MSA
+            shift_mask = torch.zeros((1, H, W, 1)).type_as(x)
+            h_slices = (
+                slice(0, -self.win_size),
+                slice(-self.win_size, -self.shift_size),
+                slice(-self.shift_size, None),
+            )
+            w_slices = (
+                slice(0, -self.win_size),
+                slice(-self.win_size, -self.shift_size),
+                slice(-self.shift_size, None),
+            )
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    shift_mask[:, h, w, :] = cnt
+                    cnt += 1
+            shift_mask_windows = window_partition(
+                shift_mask, self.win_size
+            )  # nW, win_size, win_size, 1
+            shift_mask_windows = shift_mask_windows.view(
+                -1, self.win_size * self.win_size
+            )  # nW, win_size*win_size
+            shift_attn_mask = shift_mask_windows.unsqueeze(
+                1
+            ) - shift_mask_windows.unsqueeze(
+                2
+            )  # nW, win_size*win_size, win_size*win_size
+            shift_attn_mask = shift_attn_mask.masked_fill(
+                shift_attn_mask != 0, float(-100.0)
+            ).masked_fill(shift_attn_mask == 0, float(0.0))
+            attn_mask = (
+                attn_mask + shift_attn_mask
+                if attn_mask is not None
+                else shift_attn_mask
+            )
+
+        attn_kv = attn_kv.view(B, H, W, C)
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_kv = torch.roll(
+                attn_kv, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
+            )
+        else:
+            shifted_kv = attn_kv
+        # partition windows
+        attn_kv_windows = window_partition(
+            shifted_kv, self.win_size
+        )  # nW*B, win_size, win_size, C
+        attn_kv_windows = attn_kv_windows.view(
+            -1, self.win_size * self.win_size, C
+        )  # nW*B, win_size*win_size, C
+
+        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.win_size
+        )  # nW*B, win_size, win_size, C
+        x_windows = x_windows.view(
+            -1, self.win_size * self.win_size, C
+        )  # nW*B, win_size*win_size, C
+
+        ### multi-head cross-attention
+        shortcut1 = x_windows
+        # prenorm
+        x_windows = self.norm1(x_windows)
+        attn_kv_windows = self.norm_kv(attn_kv_windows)
+        # W-MCA/SW-MCA
+        attn_windows = self.cross_attn(
+            x_windows, attn_kv=attn_kv_windows, mask=attn_mask
+        )  # nW*B, win_size*win_size, C
+        attn_windows = shortcut1 + self.drop_path(attn_windows)
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.win_size, self.win_size, C)
+        shifted_x = window_reverse(attn_windows, self.win_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)
+        # FFN
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        del attn_mask
+        return x
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        flops += self.cross_attn.flops(H, W)
+        # norm2
+        flops += self.dim * H * W
+        # mlp
+        flops += self.mlp.flops(H, W)
+        print("LeWin:{%.2f}" % (flops / 1e9))
+        return flops
+
+
+#########################################
+########### Basic layer of Uformer ################
+class BasicUformerLayer(nn.Module):
+    def __init__(
+        self,
+        dim,
+        output_dim,
+        input_resolution,
+        depth,
+        num_heads,
+        win_size,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        norm_layer=nn.LayerNorm,
+        use_checkpoint=False,
+        token_projection="linear",
+        token_mlp="ffn",
+        se_layer=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(
+            [
+                LeWinTransformerBlock(
+                    dim=dim,
+                    input_resolution=input_resolution,
+                    num_heads=num_heads,
+                    win_size=win_size,
+                    shift_size=0 if (i % 2 == 0) else win_size // 2,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_scale=qk_scale,
+                    drop=drop,
+                    attn_drop=attn_drop,
+                    drop_path=(
+                        drop_path[i] if isinstance(drop_path, list) else drop_path
+                    ),
+                    norm_layer=norm_layer,
+                    token_projection=token_projection,
+                    token_mlp=token_mlp,
+                    se_layer=se_layer,
+                )
+                for i in range(depth)
+            ]
+        )
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def forward(self, x, mask=None):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x, mask)
+        return x
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        return flops
+
+
+########### Basic decoderlayer of Uformer_Cross ################
+class CrossUformerLayer(nn.Module):
+    def __init__(
+        self,
+        dim,
+        output_dim,
+        input_resolution,
+        depth,
+        num_heads,
+        win_size,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        norm_layer=nn.LayerNorm,
+        use_checkpoint=False,
+        token_projection="linear",
+        token_mlp="ffn",
+    ):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+        # build blocks
+
+        self.blocks = nn.ModuleList(
+            [
+                LeWinTransformer_Cross(
+                    dim=dim,
+                    input_resolution=input_resolution,
+                    num_heads=num_heads,
+                    win_size=win_size,
+                    shift_size=0 if (i % 2 == 0) else win_size // 2,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_scale=qk_scale,
+                    drop=drop,
+                    attn_drop=attn_drop,
+                    drop_path=(
+                        drop_path[i] if isinstance(drop_path, list) else drop_path
+                    ),
+                    norm_layer=norm_layer,
+                    token_projection=token_projection,
+                    token_mlp=token_mlp,
+                )
+                for i in range(depth)
+            ]
+        )
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def forward(self, x, attn_kv=None, mask=None):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x, attn_kv, mask)
+        return x
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        return flops
+
+
+########### Basic decoderlayer of Uformer_CatCross ################
+class CatCrossUformerLayer(nn.Module):
+    def __init__(
+        self,
+        dim,
+        output_dim,
+        input_resolution,
+        depth,
+        num_heads,
+        win_size,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        norm_layer=nn.LayerNorm,
+        use_checkpoint=False,
+        token_projection="linear",
+        token_mlp="ffn",
+    ):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+        # build blocks
+
+        self.blocks = nn.ModuleList(
+            [
+                LeWinTransformer_CatCross(
+                    dim=dim,
+                    input_resolution=input_resolution,
+                    num_heads=num_heads,
+                    win_size=win_size,
+                    shift_size=0 if (i % 2 == 0) else win_size // 2,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_scale=qk_scale,
+                    drop=drop,
+                    attn_drop=attn_drop,
+                    drop_path=(
+                        drop_path[i] if isinstance(drop_path, list) else drop_path
+                    ),
+                    norm_layer=norm_layer,
+                    token_projection=token_projection,
+                    token_mlp=token_mlp,
+                )
+                for i in range(depth)
+            ]
+        )
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def forward(self, x, attn_kv=None, mask=None):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x, attn_kv, mask)
+        return x
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        return flops
+
+
+# @ARCH_REGISTRY.register()
+class Uformer(nn.Module):
+    def __init__(
+        self,
+        img_size=128,
+        img_ch=3,
+        output_ch=3,
+        embed_dim=32,
+        depths=[2, 2, 2, 2, 2, 2, 2, 2, 2],
+        num_heads=[1, 2, 4, 8, 16, 16, 8, 4, 2],
+        win_size=8,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.1,
+        norm_layer=nn.LayerNorm,
+        patch_norm=True,
+        use_checkpoint=False,
+        token_projection="linear",
+        token_mlp="ffn",
+        se_layer=False,
+        dowsample=Downsample,
+        upsample=Upsample,
+        **kwargs,
+    ):
+        super().__init__()
+        in_chans = img_ch
+        out_chans = output_ch
+        self.num_enc_layers = len(depths) // 2
+        self.num_dec_layers = len(depths) // 2
+        self.embed_dim = embed_dim
+        self.patch_norm = patch_norm
+        self.mlp_ratio = mlp_ratio
+        self.token_projection = token_projection
+        self.mlp = token_mlp
+        self.win_size = win_size
+        self.reso = img_size
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        enc_dpr = [
+            x.item()
+            for x in torch.linspace(
+                0, drop_path_rate, sum(depths[: self.num_enc_layers])
+            )
+        ]
+        conv_dpr = [drop_path_rate] * depths[4]
+        dec_dpr = enc_dpr[::-1]
+
+        # build layers
+
+        # Input/Output
+        self.input_proj = InputProj(
+            in_channel=in_chans,
+            out_channel=embed_dim,
+            kernel_size=3,
+            stride=1,
+            act_layer=nn.LeakyReLU,
+        )
+        self.output_proj = OutputProj(
+            in_channel=2 * embed_dim, out_channel=out_chans, kernel_size=3, stride=1
+        )
+
+        # Encoder
+        self.encoderlayer_0 = BasicUformerLayer(
+            dim=embed_dim,
+            output_dim=embed_dim,
+            input_resolution=(img_size, img_size),
+            depth=depths[0],
+            num_heads=num_heads[0],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:0]) : sum(depths[:1])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.dowsample_0 = dowsample(embed_dim, embed_dim * 2)
+        self.encoderlayer_1 = BasicUformerLayer(
+            dim=embed_dim * 2,
+            output_dim=embed_dim * 2,
+            input_resolution=(img_size // 2, img_size // 2),
+            depth=depths[1],
+            num_heads=num_heads[1],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:1]) : sum(depths[:2])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.dowsample_1 = dowsample(embed_dim * 2, embed_dim * 4)
+        self.encoderlayer_2 = BasicUformerLayer(
+            dim=embed_dim * 4,
+            output_dim=embed_dim * 4,
+            input_resolution=(img_size // (2**2), img_size // (2**2)),
+            depth=depths[2],
+            num_heads=num_heads[2],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:2]) : sum(depths[:3])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.dowsample_2 = dowsample(embed_dim * 4, embed_dim * 8)
+        self.encoderlayer_3 = BasicUformerLayer(
+            dim=embed_dim * 8,
+            output_dim=embed_dim * 8,
+            input_resolution=(img_size // (2**3), img_size // (2**3)),
+            depth=depths[3],
+            num_heads=num_heads[3],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:3]) : sum(depths[:4])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.dowsample_3 = dowsample(embed_dim * 8, embed_dim * 16)
+
+        # Bottleneck
+        self.conv = BasicUformerLayer(
+            dim=embed_dim * 16,
+            output_dim=embed_dim * 16,
+            input_resolution=(img_size // (2**4), img_size // (2**4)),
+            depth=depths[4],
+            num_heads=num_heads[4],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=conv_dpr,
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+
+        # Decoder
+        self.upsample_0 = upsample(embed_dim * 16, embed_dim * 8)
+        self.decoderlayer_0 = BasicUformerLayer(
+            dim=embed_dim * 16,
+            output_dim=embed_dim * 16,
+            input_resolution=(img_size // (2**3), img_size // (2**3)),
+            depth=depths[5],
+            num_heads=num_heads[5],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[: depths[5]],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.upsample_1 = upsample(embed_dim * 16, embed_dim * 4)
+        self.decoderlayer_1 = BasicUformerLayer(
+            dim=embed_dim * 8,
+            output_dim=embed_dim * 8,
+            input_resolution=(img_size // (2**2), img_size // (2**2)),
+            depth=depths[6],
+            num_heads=num_heads[6],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[sum(depths[5:6]) : sum(depths[5:7])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.upsample_2 = upsample(embed_dim * 8, embed_dim * 2)
+        self.decoderlayer_2 = BasicUformerLayer(
+            dim=embed_dim * 4,
+            output_dim=embed_dim * 4,
+            input_resolution=(img_size // 2, img_size // 2),
+            depth=depths[7],
+            num_heads=num_heads[7],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[sum(depths[5:7]) : sum(depths[5:8])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.upsample_3 = upsample(embed_dim * 4, embed_dim)
+        self.decoderlayer_3 = BasicUformerLayer(
+            dim=embed_dim * 2,
+            output_dim=embed_dim * 2,
+            input_resolution=(img_size, img_size),
+            depth=depths[8],
+            num_heads=num_heads[8],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[sum(depths[5:8]) : sum(depths[5:9])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.activation = nn.Sequential(nn.Sigmoid())
+        self.apply(self._init_weights)
+
+    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 {"relative_position_bias_table"}
+
+    def extra_repr(self) -> str:
+        return f"embed_dim={self.embed_dim}, token_projection={self.token_projection}, token_mlp={self.mlp},win_size={self.win_size}"
+
+    def forward(self, x, mask=None):
+        # Input Projection
+        y = self.input_proj(x)
+        y = self.pos_drop(y)
+        # Encoder
+        conv0 = self.encoderlayer_0(y, mask=mask)
+        pool0 = self.dowsample_0(conv0)
+        conv1 = self.encoderlayer_1(pool0, mask=mask)
+        pool1 = self.dowsample_1(conv1)
+        conv2 = self.encoderlayer_2(pool1, mask=mask)
+        pool2 = self.dowsample_2(conv2)
+        conv3 = self.encoderlayer_3(pool2, mask=mask)
+        pool3 = self.dowsample_3(conv3)
+
+        # Bottleneck
+        conv4 = self.conv(pool3, mask=mask)
+
+        # Decoder
+        up0 = self.upsample_0(conv4)
+        deconv0 = torch.cat([up0, conv3], -1)
+        deconv0 = self.decoderlayer_0(deconv0, mask=mask)
+
+        up1 = self.upsample_1(deconv0)
+        deconv1 = torch.cat([up1, conv2], -1)
+        deconv1 = self.decoderlayer_1(deconv1, mask=mask)
+
+        up2 = self.upsample_2(deconv1)
+        deconv2 = torch.cat([up2, conv1], -1)
+        deconv2 = self.decoderlayer_2(deconv2, mask=mask)
+
+        up3 = self.upsample_3(deconv2)
+        deconv3 = torch.cat([up3, conv0], -1)
+        deconv3 = self.decoderlayer_3(deconv3, mask=mask)
+
+        # Output Projection
+        y = self.output_proj(deconv3)
+        y = self.activation(y)
+        return y
+
+    def flops(self):
+        flops = 0
+        # Input Projection
+        flops += self.input_proj.flops(self.reso, self.reso)
+        # Encoder
+        flops += self.encoderlayer_0.flops() + self.dowsample_0.flops(
+            self.reso, self.reso
+        )
+        flops += self.encoderlayer_1.flops() + self.dowsample_1.flops(
+            self.reso // 2, self.reso // 2
+        )
+        flops += self.encoderlayer_2.flops() + self.dowsample_2.flops(
+            self.reso // 2**2, self.reso // 2**2
+        )
+        flops += self.encoderlayer_3.flops() + self.dowsample_3.flops(
+            self.reso // 2**3, self.reso // 2**3
+        )
+
+        # Bottleneck
+        flops += self.conv.flops()
+
+        # Decoder
+        flops += (
+            self.upsample_0.flops(self.reso // 2**4, self.reso // 2**4)
+            + self.decoderlayer_0.flops()
+        )
+        flops += (
+            self.upsample_1.flops(self.reso // 2**3, self.reso // 2**3)
+            + self.decoderlayer_1.flops()
+        )
+        flops += (
+            self.upsample_2.flops(self.reso // 2**2, self.reso // 2**2)
+            + self.decoderlayer_2.flops()
+        )
+        flops += (
+            self.upsample_3.flops(self.reso // 2, self.reso // 2)
+            + self.decoderlayer_3.flops()
+        )
+
+        # Output Projection
+        flops += self.output_proj.flops(self.reso, self.reso)
+        return flops
+
+
+class Uformer_Cross(nn.Module):
+    def __init__(
+        self,
+        img_size=128,
+        in_chans=3,
+        out_chans=3,
+        embed_dim=32,
+        depths=[2, 2, 2, 2, 2, 2, 2, 2, 2],
+        num_heads=[1, 2, 4, 8, 16, 8, 4, 2, 1],
+        win_size=8,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.1,
+        norm_layer=nn.LayerNorm,
+        patch_norm=True,
+        use_checkpoint=False,
+        token_projection="linear",
+        token_mlp="ffn",
+        dowsample=Downsample,
+        upsample=Upsample,
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.num_enc_layers = len(depths) // 2
+        self.num_dec_layers = len(depths) // 2
+        self.embed_dim = embed_dim
+        self.patch_norm = patch_norm
+        self.mlp_ratio = mlp_ratio
+        self.token_projection = token_projection
+        self.mlp = token_mlp
+        self.win_size = win_size
+        self.reso = img_size
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        enc_dpr = [
+            x.item()
+            for x in torch.linspace(
+                0, drop_path_rate, sum(depths[: self.num_enc_layers])
+            )
+        ]
+        conv_dpr = [drop_path_rate] * depths[4]
+        dec_dpr = enc_dpr[::-1]
+
+        # build layers
+
+        # Input/Output
+        self.input_proj = InputProj(
+            in_channel=in_chans,
+            out_channel=embed_dim,
+            kernel_size=3,
+            stride=1,
+            act_layer=nn.LeakyReLU,
+        )
+        self.output_proj = OutputProj(
+            in_channel=embed_dim, out_channel=out_chans, kernel_size=3, stride=1
+        )
+
+        # Encoder
+        self.encoderlayer_0 = BasicUformerLayer(
+            dim=embed_dim,
+            output_dim=embed_dim,
+            input_resolution=(img_size, img_size),
+            depth=depths[0],
+            num_heads=num_heads[0],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:0]) : sum(depths[:1])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.dowsample_0 = dowsample(embed_dim, embed_dim * 2)
+        self.encoderlayer_1 = BasicUformerLayer(
+            dim=embed_dim * 2,
+            output_dim=embed_dim * 2,
+            input_resolution=(img_size // 2, img_size // 2),
+            depth=depths[1],
+            num_heads=num_heads[1],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:1]) : sum(depths[:2])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.dowsample_1 = dowsample(embed_dim * 2, embed_dim * 4)
+        self.encoderlayer_2 = BasicUformerLayer(
+            dim=embed_dim * 4,
+            output_dim=embed_dim * 4,
+            input_resolution=(img_size // (2**2), img_size // (2**2)),
+            depth=depths[2],
+            num_heads=num_heads[2],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:2]) : sum(depths[:3])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.dowsample_2 = dowsample(embed_dim * 4, embed_dim * 8)
+        self.encoderlayer_3 = BasicUformerLayer(
+            dim=embed_dim * 8,
+            output_dim=embed_dim * 8,
+            input_resolution=(img_size // (2**3), img_size // (2**3)),
+            depth=depths[3],
+            num_heads=num_heads[3],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:3]) : sum(depths[:4])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.dowsample_3 = dowsample(embed_dim * 8, embed_dim * 16)
+
+        # Bottleneck
+        self.conv = BasicUformerLayer(
+            dim=embed_dim * 16,
+            output_dim=embed_dim * 16,
+            input_resolution=(img_size // (2**4), img_size // (2**4)),
+            depth=depths[4],
+            num_heads=num_heads[4],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=conv_dpr,
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+
+        # Decoder
+        self.upsample_0 = upsample(embed_dim * 16, embed_dim * 8)
+        self.decoderlayer_0 = CrossUformerLayer(
+            dim=embed_dim * 8,
+            output_dim=embed_dim * 8,
+            input_resolution=(img_size // (2**3), img_size // (2**3)),
+            depth=depths[5],
+            num_heads=num_heads[5],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[: depths[5]],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.upsample_1 = upsample(embed_dim * 8, embed_dim * 4)
+        self.decoderlayer_1 = CrossUformerLayer(
+            dim=embed_dim * 4,
+            output_dim=embed_dim * 4,
+            input_resolution=(img_size // (2**2), img_size // (2**2)),
+            depth=depths[6],
+            num_heads=num_heads[6],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[sum(depths[5:6]) : sum(depths[5:7])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.upsample_2 = upsample(embed_dim * 4, embed_dim * 2)
+        self.decoderlayer_2 = CrossUformerLayer(
+            dim=embed_dim * 2,
+            output_dim=embed_dim * 2,
+            input_resolution=(img_size // 2, img_size // 2),
+            depth=depths[7],
+            num_heads=num_heads[7],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[sum(depths[5:7]) : sum(depths[5:8])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.upsample_3 = upsample(embed_dim * 2, embed_dim)
+        self.decoderlayer_3 = CrossUformerLayer(
+            dim=embed_dim,
+            output_dim=embed_dim,
+            input_resolution=(img_size, img_size),
+            depth=depths[8],
+            num_heads=num_heads[8],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[sum(depths[5:8]) : sum(depths[5:9])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+
+        self.apply(self._init_weights)
+
+    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 {"relative_position_bias_table"}
+
+    def extra_repr(self) -> str:
+        return f"embed_dim={self.embed_dim}, token_projection={self.token_projection}, token_mlp={self.mlp},win_size={self.win_size}"
+
+    def forward(self, x, mask=None):
+        # Input Projection
+        y = self.input_proj(x)
+        y = self.pos_drop(y)
+
+        # Encoder
+        conv0 = self.encoderlayer_0(y, mask=mask)
+        pool0 = self.dowsample_0(conv0)
+        conv1 = self.encoderlayer_1(pool0, mask=mask)
+        pool1 = self.dowsample_1(conv1)
+        conv2 = self.encoderlayer_2(pool1, mask=mask)
+        pool2 = self.dowsample_2(conv2)
+        conv3 = self.encoderlayer_3(pool2, mask=mask)
+        pool3 = self.dowsample_3(conv3)
+
+        # Bottleneck
+        conv4 = self.conv(pool3, mask=mask)
+
+        # Decoder
+        up0 = self.upsample_0(conv4)
+        deconv0 = self.decoderlayer_0(up0, attn_kv=conv3, mask=mask)
+
+        up1 = self.upsample_1(deconv0)
+        deconv1 = self.decoderlayer_1(up1, attn_kv=conv2, mask=mask)
+
+        up2 = self.upsample_2(deconv1)
+        deconv2 = self.decoderlayer_2(up2, attn_kv=conv1, mask=mask)
+
+        up3 = self.upsample_3(deconv2)
+        deconv3 = self.decoderlayer_3(up3, attn_kv=conv0, mask=mask)
+
+        # Output Projection
+        y = self.output_proj(deconv3)
+        return x + y
+
+    def flops(self):
+        flops = 0
+        # Input Projection
+        flops += self.input_proj.flops(self.reso, self.reso)
+        # Encoder
+        flops += self.encoderlayer_0.flops() + self.dowsample_0.flops(
+            self.reso, self.reso
+        )
+        flops += self.encoderlayer_1.flops() + self.dowsample_1.flops(
+            self.reso // 2, self.reso // 2
+        )
+        flops += self.encoderlayer_2.flops() + self.dowsample_2.flops(
+            self.reso // 2**2, self.reso // 2**2
+        )
+        flops += self.encoderlayer_3.flops() + self.dowsample_3.flops(
+            self.reso // 2**3, self.reso // 2**3
+        )
+
+        # Bottleneck
+        flops += self.conv.flops()
+
+        # Decoder
+        flops += (
+            self.upsample_0.flops(self.reso // 2**4, self.reso // 2**4)
+            + self.decoderlayer_0.flops()
+        )
+        flops += (
+            self.upsample_1.flops(self.reso // 2**3, self.reso // 2**3)
+            + self.decoderlayer_1.flops()
+        )
+        flops += (
+            self.upsample_2.flops(self.reso // 2**2, self.reso // 2**2)
+            + self.decoderlayer_2.flops()
+        )
+        flops += (
+            self.upsample_3.flops(self.reso // 2, self.reso // 2)
+            + self.decoderlayer_3.flops()
+        )
+
+        # Output Projection
+        flops += self.output_proj.flops(self.reso, self.reso)
+        return flops
+
+
+class Uformer_CatCross(nn.Module):
+    def __init__(
+        self,
+        img_size=128,
+        in_chans=3,
+        out_chans=3,
+        embed_dim=32,
+        depths=[2, 2, 2, 2, 2, 2, 2, 2, 2],
+        num_heads=[1, 2, 4, 8, 16, 8, 4, 2, 1],
+        win_size=8,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.1,
+        norm_layer=nn.LayerNorm,
+        patch_norm=True,
+        use_checkpoint=False,
+        token_projection="linear",
+        token_mlp="ffn",
+        dowsample=Downsample,
+        upsample=Upsample,
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.num_enc_layers = len(depths) // 2
+        self.num_dec_layers = len(depths) // 2
+        self.embed_dim = embed_dim
+        self.patch_norm = patch_norm
+        self.mlp_ratio = mlp_ratio
+        self.token_projection = token_projection
+        self.mlp = token_mlp
+        self.win_size = win_size
+        self.reso = img_size
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        enc_dpr = [
+            x.item()
+            for x in torch.linspace(
+                0, drop_path_rate, sum(depths[: self.num_enc_layers])
+            )
+        ]  # stochastic depth decay rule
+        conv_dpr = [drop_path_rate] * depths[4]
+        dec_dpr = enc_dpr[::-1]
+
+        # build layers
+
+        # Input/Output
+        self.input_proj = InputProj(
+            in_channel=in_chans,
+            out_channel=embed_dim,
+            kernel_size=3,
+            stride=1,
+            act_layer=nn.LeakyReLU,
+        )
+        self.output_proj = OutputProj(
+            in_channel=embed_dim, out_channel=out_chans, kernel_size=3, stride=1
+        )
+
+        # Encoder
+        self.encoderlayer_0 = BasicUformerLayer(
+            dim=embed_dim,
+            output_dim=embed_dim,
+            input_resolution=(img_size, img_size),
+            depth=depths[0],
+            num_heads=num_heads[0],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:0]) : sum(depths[:1])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.dowsample_0 = dowsample(embed_dim, embed_dim * 2)
+        self.encoderlayer_1 = BasicUformerLayer(
+            dim=embed_dim * 2,
+            output_dim=embed_dim * 2,
+            input_resolution=(img_size // 2, img_size // 2),
+            depth=depths[1],
+            num_heads=num_heads[1],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:1]) : sum(depths[:2])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.dowsample_1 = dowsample(embed_dim * 2, embed_dim * 4)
+        self.encoderlayer_2 = BasicUformerLayer(
+            dim=embed_dim * 4,
+            output_dim=embed_dim * 4,
+            input_resolution=(img_size // (2**2), img_size // (2**2)),
+            depth=depths[2],
+            num_heads=num_heads[2],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:2]) : sum(depths[:3])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.dowsample_2 = dowsample(embed_dim * 4, embed_dim * 8)
+        self.encoderlayer_3 = BasicUformerLayer(
+            dim=embed_dim * 8,
+            output_dim=embed_dim * 8,
+            input_resolution=(img_size // (2**3), img_size // (2**3)),
+            depth=depths[3],
+            num_heads=num_heads[3],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:3]) : sum(depths[:4])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.dowsample_3 = dowsample(embed_dim * 8, embed_dim * 16)
+
+        # Bottleneck
+        self.conv = BasicUformerLayer(
+            dim=embed_dim * 16,
+            output_dim=embed_dim * 16,
+            input_resolution=(img_size // (2**4), img_size // (2**4)),
+            depth=depths[4],
+            num_heads=num_heads[4],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=conv_dpr,
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+
+        # Decoder
+        self.upsample_0 = upsample(embed_dim * 16, embed_dim * 8)
+        self.decoderlayer_0 = CatCrossUformerLayer(
+            dim=embed_dim * 8,
+            output_dim=embed_dim * 8,
+            input_resolution=(img_size // (2**3), img_size // (2**3)),
+            depth=depths[5],
+            num_heads=num_heads[5],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[: depths[5]],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.upsample_1 = upsample(embed_dim * 8, embed_dim * 4)
+        self.decoderlayer_1 = CatCrossUformerLayer(
+            dim=embed_dim * 4,
+            output_dim=embed_dim * 4,
+            input_resolution=(img_size // (2**2), img_size // (2**2)),
+            depth=depths[6],
+            num_heads=num_heads[6],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[sum(depths[5:6]) : sum(depths[5:7])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.upsample_2 = upsample(embed_dim * 4, embed_dim * 2)
+        self.decoderlayer_2 = CatCrossUformerLayer(
+            dim=embed_dim * 2,
+            output_dim=embed_dim * 2,
+            input_resolution=(img_size // 2, img_size // 2),
+            depth=depths[7],
+            num_heads=num_heads[7],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[sum(depths[5:7]) : sum(depths[5:8])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+        self.upsample_3 = upsample(embed_dim * 2, embed_dim)
+        self.decoderlayer_3 = CatCrossUformerLayer(
+            dim=embed_dim,
+            output_dim=embed_dim,
+            input_resolution=(img_size, img_size),
+            depth=depths[8],
+            num_heads=num_heads[8],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[sum(depths[5:8]) : sum(depths[5:9])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+        )
+
+        self.apply(self._init_weights)
+
+    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 {"relative_position_bias_table"}
+
+    def extra_repr(self) -> str:
+        return f"embed_dim={self.embed_dim}, token_projection={self.token_projection}, token_mlp={self.mlp},win_size={self.win_size}"
+
+    def forward(self, x, mask=None):
+        # Input Projection
+        y = self.input_proj(x)
+        y = self.pos_drop(y)
+        # Encoder
+        conv0 = self.encoderlayer_0(y, mask=mask)
+        pool0 = self.dowsample_0(conv0)
+        conv1 = self.encoderlayer_1(pool0, mask=mask)
+        pool1 = self.dowsample_1(conv1)
+        conv2 = self.encoderlayer_2(pool1, mask=mask)
+        pool2 = self.dowsample_2(conv2)
+        conv3 = self.encoderlayer_3(pool2, mask=mask)
+        pool3 = self.dowsample_3(conv3)
+
+        # Bottleneck
+        conv4 = self.conv(pool3, mask=mask)
+
+        # Decoder
+        up0 = self.upsample_0(conv4)
+        deconv0 = self.decoderlayer_0(up0, attn_kv=conv3, mask=mask)
+
+        up1 = self.upsample_1(deconv0)
+        deconv1 = self.decoderlayer_1(up1, attn_kv=conv2, mask=mask)
+
+        up2 = self.upsample_2(deconv1)
+        deconv2 = self.decoderlayer_2(up2, attn_kv=conv1, mask=mask)
+
+        up3 = self.upsample_3(deconv2)
+        deconv3 = self.decoderlayer_3(up3, attn_kv=conv0, mask=mask)
+
+        # Output Projection
+        y = self.output_proj(deconv3)
+        return x + y
+
+    def flops(self):
+        flops = 0
+        # Input Projection
+        flops += self.input_proj.flops(self.reso, self.reso)
+        # Encoder
+        flops += self.encoderlayer_0.flops() + self.dowsample_0.flops(
+            self.reso, self.reso
+        )
+        flops += self.encoderlayer_1.flops() + self.dowsample_1.flops(
+            self.reso // 2, self.reso // 2
+        )
+        flops += self.encoderlayer_2.flops() + self.dowsample_2.flops(
+            self.reso // 2**2, self.reso // 2**2
+        )
+        flops += self.encoderlayer_3.flops() + self.dowsample_3.flops(
+            self.reso // 2**3, self.reso // 2**3
+        )
+
+        # Bottleneck
+        flops += self.conv.flops()
+
+        # Decoder
+        flops += (
+            self.upsample_0.flops(self.reso // 2**4, self.reso // 2**4)
+            + self.decoderlayer_0.flops()
+        )
+        flops += (
+            self.upsample_1.flops(self.reso // 2**3, self.reso // 2**3)
+            + self.decoderlayer_1.flops()
+        )
+        flops += (
+            self.upsample_2.flops(self.reso // 2**2, self.reso // 2**2)
+            + self.decoderlayer_2.flops()
+        )
+        flops += (
+            self.upsample_3.flops(self.reso // 2, self.reso // 2)
+            + self.decoderlayer_3.flops()
+        )
+
+        # Output Projection
+        flops += self.output_proj.flops(self.reso, self.reso)
+        return flops
+
+
+# class LeWinformer(nn.Module):
+#     def __init__(self, img_size=128, in_chans=3,
+#                  embed_dim=32, depth=12,
+#                  win_size=8, mlp_ratio=4., qkv_bias=True, qk_scale=None,
+#                  drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+#                  norm_layer=nn.LayerNorm, patch_norm=True,
+#                  use_checkpoint=False, token_projection='linear', token_mlp='ffn', se_layer=False,**kwargs):
+#         super().__init__()
+
+#         self.transformer_layers = nn.ModuleList([])
+#         self.embed_dim = embed_dim
+#         self.num_heads = embed_dim//32 or 1
+#         self.patch_norm = patch_norm
+#         self.mlp_ratio = mlp_ratio
+#         self.token_projection = token_projection
+#         self.mlp = token_mlp
+#         self.win_size =win_size
+
+#         self.pos_drop = nn.Dropout(p=drop_rate)
+
+#         # stochastic depth
+#         dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
+
+#         # build layers
+
+#         # Input/Output
+#         self.input_proj = InputProj(in_channel=in_chans, out_channel=embed_dim, kernel_size=3, stride=1, act_layer=nn.LeakyReLU)
+#         self.output_proj = OutputProj(in_channel=embed_dim, out_channel=in_chans, kernel_size=3, stride=1)
+
+#         # LeWin Transformer
+#         for i in range(depth):
+#             dim = embed_dim
+#             self.transformer_layers.append(nn.ModuleList([BasicUformerLayer(dim=dim,
+#                             output_dim=embed_dim,
+#                             input_resolution=(img_size,
+#                                                 img_size),
+#                             depth=1,
+#                             num_heads=self.num_heads,
+#                             win_size=win_size,
+#                             mlp_ratio=self.mlp_ratio,
+#                             qkv_bias=qkv_bias, qk_scale=qk_scale,
+#                             drop=drop_rate, attn_drop=attn_drop_rate,
+#                             drop_path=dpr[i],
+#                             norm_layer=norm_layer,
+#                             use_checkpoint=use_checkpoint,
+#                             token_projection=token_projection,token_mlp=token_mlp,se_layer=se_layer),
+#                 Downsample()
+#             ]))
+
+#         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 extra_repr(self) -> str:
+#         return f"embed_dim={self.embed_dim}, token_projection={self.token_projection}, token_mlp={self.mlp},win_size={self.win_size}"
+
+#     def forward(self, x, mask=None):
+#         # Input Projection
+#         y = self.input_proj(x)
+#         y = self.pos_drop(y)
+#         #Encoder
+#         for lewin in self.transformer_layers:
+#             y = lewin(y)
+
+#         # Output Projection
+#         y = self.output_proj(y)
+#         return x + y
+
+
+### single-scale Uformer is computationally too costly.
+class Uformer_singlescale(nn.Module):
+    def __init__(
+        self,
+        img_size=128,
+        in_chans=3,
+        out_chans=3,
+        embed_dim=32,
+        depths=[2, 2, 2, 2, 2, 2, 2, 2, 2],
+        num_heads=[1, 2, 4, 8, 16, 16, 8, 4, 2],
+        win_size=8,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.1,
+        norm_layer=nn.LayerNorm,
+        patch_norm=True,
+        use_checkpoint=False,
+        token_projection="linear",
+        token_mlp="ffn",
+        se_layer=False,
+        downsample=Downsample,
+        upsample=Upsample,
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.num_enc_layers = len(depths) // 2
+        self.num_dec_layers = len(depths) // 2
+        self.embed_dim = embed_dim
+        self.patch_norm = patch_norm
+        self.mlp_ratio = mlp_ratio
+        self.token_projection = token_projection
+        self.mlp = token_mlp
+        self.win_size = win_size
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        enc_dpr = [
+            x.item()
+            for x in torch.linspace(
+                0, drop_path_rate, sum(depths[: self.num_enc_layers])
+            )
+        ]
+        conv_dpr = [drop_path_rate] * depths[4]
+        dec_dpr = enc_dpr[::-1]
+
+        # build layers
+
+        # Input/Output
+        self.input_proj = InputProj(
+            in_channel=in_chans,
+            out_channel=embed_dim,
+            kernel_size=3,
+            stride=1,
+            act_layer=nn.LeakyReLU,
+        )
+        self.output_proj = OutputProj(
+            in_channel=2 * embed_dim, out_channel=out_chans, kernel_size=3, stride=1
+        )
+
+        # Encoder
+        self.encoderlayer_0 = BasicUformerLayer(
+            dim=embed_dim,
+            output_dim=embed_dim,
+            input_resolution=(img_size, img_size),
+            depth=depths[0],
+            num_heads=num_heads[0],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:0]) : sum(depths[:1])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.downsample_0 = downsample(embed_dim, embed_dim * 2, downsample=False)
+        self.encoderlayer_1 = BasicUformerLayer(
+            dim=embed_dim * 2,
+            output_dim=embed_dim * 2,
+            input_resolution=(img_size, img_size),
+            depth=depths[1],
+            num_heads=num_heads[1],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:1]) : sum(depths[:2])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.downsample_1 = downsample(embed_dim * 2, embed_dim * 4, downsample=False)
+        self.encoderlayer_2 = BasicUformerLayer(
+            dim=embed_dim * 4,
+            output_dim=embed_dim * 4,
+            input_resolution=(img_size, img_size),
+            depth=depths[2],
+            num_heads=num_heads[2],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:2]) : sum(depths[:3])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.downsample_2 = downsample(embed_dim * 4, embed_dim * 8, downsample=False)
+        self.encoderlayer_3 = BasicUformerLayer(
+            dim=embed_dim * 8,
+            output_dim=embed_dim * 8,
+            input_resolution=(img_size, img_size),
+            depth=depths[3],
+            num_heads=num_heads[3],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=enc_dpr[sum(depths[:3]) : sum(depths[:4])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.downsample_3 = downsample(embed_dim * 8, embed_dim * 16, downsample=False)
+
+        # Bottleneck
+        self.conv = BasicUformerLayer(
+            dim=embed_dim * 16,
+            output_dim=embed_dim * 16,
+            input_resolution=(img_size, img_size),
+            depth=depths[4],
+            num_heads=num_heads[4],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=conv_dpr,
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+
+        # Decoder
+        self.upsample_0 = upsample(embed_dim * 16, embed_dim * 8, upsample=False)
+        self.decoderlayer_0 = BasicUformerLayer(
+            dim=embed_dim * 16,
+            output_dim=embed_dim * 16,
+            input_resolution=(img_size, img_size),
+            depth=depths[5],
+            num_heads=num_heads[5],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[: depths[5]],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.upsample_1 = upsample(embed_dim * 16, embed_dim * 4, upsample=False)
+        self.decoderlayer_1 = BasicUformerLayer(
+            dim=embed_dim * 8,
+            output_dim=embed_dim * 8,
+            input_resolution=(img_size, img_size),
+            depth=depths[6],
+            num_heads=num_heads[6],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[sum(depths[5:6]) : sum(depths[5:7])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.upsample_2 = upsample(embed_dim * 8, embed_dim * 2, upsample=False)
+        self.decoderlayer_2 = BasicUformerLayer(
+            dim=embed_dim * 4,
+            output_dim=embed_dim * 4,
+            input_resolution=(img_size, img_size),
+            depth=depths[7],
+            num_heads=num_heads[7],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[sum(depths[5:7]) : sum(depths[5:8])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+        self.upsample_3 = upsample(embed_dim * 4, embed_dim, upsample=False)
+        self.decoderlayer_3 = BasicUformerLayer(
+            dim=embed_dim * 2,
+            output_dim=embed_dim * 2,
+            input_resolution=(img_size, img_size),
+            depth=depths[8],
+            num_heads=num_heads[8],
+            win_size=win_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            drop=drop_rate,
+            attn_drop=attn_drop_rate,
+            drop_path=dec_dpr[sum(depths[5:8]) : sum(depths[5:9])],
+            norm_layer=norm_layer,
+            use_checkpoint=use_checkpoint,
+            token_projection=token_projection,
+            token_mlp=token_mlp,
+            se_layer=se_layer,
+        )
+
+        self.apply(self._init_weights)
+
+    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 {"relative_position_bias_table"}
+
+    def extra_repr(self) -> str:
+        return f"embed_dim={self.embed_dim}, token_projection={self.token_projection}, token_mlp={self.mlp},win_size={self.win_size}"
+
+    def forward(self, x, mask=None):
+        # Input Projection
+        y = self.input_proj(x)
+        y = self.pos_drop(y)
+        # Encoder
+        conv0 = self.encoderlayer_0(y, mask=mask)
+        pool0 = self.downsample_0(conv0)
+        conv1 = self.encoderlayer_1(pool0, mask=mask)
+        pool1 = self.downsample_1(conv1)
+        conv2 = self.encoderlayer_2(pool1, mask=mask)
+        pool2 = self.downsample_2(conv2)
+        conv3 = self.encoderlayer_3(pool2, mask=mask)
+        pool3 = self.downsample_3(conv3)
+
+        # Bottleneck
+        conv4 = self.conv(pool3, mask=mask)
+
+        # Decoder
+        up0 = self.upsample_0(conv4)
+        deconv0 = torch.cat([up0, conv3], -1)
+        deconv0 = self.decoderlayer_0(deconv0, mask=mask)
+
+        up1 = self.upsample_1(deconv0)
+        deconv1 = torch.cat([up1, conv2], -1)
+        deconv1 = self.decoderlayer_1(deconv1, mask=mask)
+
+        up2 = self.upsample_2(deconv1)
+        deconv2 = torch.cat([up2, conv1], -1)
+        deconv2 = self.decoderlayer_2(deconv2, mask=mask)
+
+        up3 = self.upsample_3(deconv2)
+        deconv3 = torch.cat([up3, conv0], -1)
+        deconv3 = self.decoderlayer_3(deconv3, mask=mask)
+
+        # Output Projection
+        y = self.output_proj(deconv3)
+        return x + y
+
+
+if __name__ == "__main__":
+    arch = Uformer
+    input_size = 256
+    # arch = Uformer_Cross
+    depths = [2, 2, 2, 2, 2, 2, 2, 2, 2]
+    # model_restoration = UNet(dim=32)
+    model_restoration = arch(
+        img_size=input_size,
+        embed_dim=44,
+        depths=depths,
+        win_size=8,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        token_projection="linear",
+        token_mlp="leff",
+        downsample=Downsample,
+        upsample=Upsample,
+        se_layer=False,
+    )
+    # arch = LeWinformer
+    # depth = 20
+    # model_restoration = arch(embed_dim=16,depth=depth,
+    #              win_size=8, mlp_ratio=4., qkv_bias=True,
+    #              token_projection='linear', token_mlp='leff',se_layer=False)
+    # from ptflops import get_model_complexity_info
+    # macs, params = get_model_complexity_info(model_restoration, (3, input_size, input_size), as_strings=True,
+    #                                             print_per_layer_stat=True, verbose=True)
+    # print('{:<30}  {:<8}'.format('Computational complexity: ', macs))
+    # print('{:<30}  {:<8}'.format('Number of parameters: ', params))
+    # print("number of GFLOPs: %.2f G"%(model_restoration.flops(input_size,input_size) / 1e9))
+    print("number of GFLOPs: %.2f G" % (model_restoration.flops() / 1e9))