model.py

import os
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
import cv2
import scipy.misc
import utils
from mtmt_model.MTMT import create_mtmt_model
from mtmt_model.utils import ramps, losses
#########################################
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(4, 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

##########################################################################
## Channel Attention Layer
class CALayer(nn.Module):
    def __init__(self, channel, reduction=16, bias=False):
        super(CALayer, self).__init__()
        # global average pooling: feature --> point
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        # feature channel downscale and upscale --> channel weight
        self.conv_du = nn.Sequential(
                nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=bias),
                nn.ReLU(inplace=True),
                nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=bias),
                nn.Sigmoid()
        )

    def forward(self, x):
        y = self.avg_pool(x)
        y = self.conv_du(y)
        return x * y

def conv(in_channels, out_channels, kernel_size, bias=False, stride = 1):
    return nn.Conv2d(
        in_channels, out_channels, kernel_size,
        padding=(kernel_size//2), bias=bias, stride = stride)

##########################################################################
## Channel Attention Block (CAB)
class CAB(nn.Module):
    def __init__(self, n_feat, kernel_size, reduction, bias, act):
        super(CAB, self).__init__()
        modules_body = []
        modules_body.append(conv(n_feat, n_feat, kernel_size, bias=bias))
        modules_body.append(act)
        modules_body.append(conv(n_feat, n_feat, kernel_size, bias=bias))

        self.CA = CALayer(n_feat, reduction, bias=bias)
        self.body = nn.Sequential(*modules_body)

    def forward(self, x):
        res = self.body(x)
        res = self.CA(res)
        res += x
        return res

#########################################
######## 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.,
                 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., 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., 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 

#########################################

########### SIA #############
class WindowAttention(nn.Module):
    def __init__(self, dim, win_size, num_heads, token_projection='linear', qkv_bias=True, qk_scale=None, attn_drop=0.,
                 proj_drop=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)
        self.ll = nn.Identity()
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, xm, attn_kv=None, mask=None):
        B_, N, C = x.shape
        # x = self.se_layer(x)
        one = torch.ones_like(xm)
        zero = torch.zeros_like(xm)
        xm = torch.where(xm < 0.1, one, one*2)
        mm = xm @ xm.transpose(-2, -1)
        one = torch.ones_like(mm)
        mm = torch.where(mm==2, one, one*0.2)
        mm = torch.unsqueeze(mm, dim=1)
        q, k, v = self.qkv(x, attn_kv)
        q = q * self.scale
        attn = (q @ k.transpose(-2, -1)) * mm

        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.ll(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.):
        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.):
        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, img_size=(128,128)):
        # bs x hw x c
        bs, hw, c = x.size()
        # hh = int(math.sqrt(hw))
        hh = img_size[0]
        ww = img_size[1]

        x = self.linear1(x)

        # spatial restore
        x = rearrange(x, ' b (h w) (c) -> b c h w ', h = hh, w = ww)
        # bs,hidden_dim,32x32

        x = self.dwconv(x)

        # flaten
        x = rearrange(x, ' b c h w -> b (h w) c', h = hh, w = ww)

        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, img_size=(128,128)):
        B, L, C = x.shape
        H = img_size[0]
        W = img_size[1]
        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, img_size=(128,128)):
        B, L, C = x.shape
        H = img_size[0]
        W = img_size[1]
        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, img_size=(128,128)):
        B, L, C = x.shape
        H = img_size[0]
        W = img_size[1]
        # 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


#########################################
########### CA Transformer #############
class CATransformerBlock(nn.Module):
    def __init__(self, dim, input_resolution, num_heads, win_size=10, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=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.drop_path = DropPath(drop_path) if drop_path > 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)
        self.CAB = CAB(dim, kernel_size=3, reduction=4, bias=False, act=nn.PReLU())


    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, xm, mask=None, img_size=(128, 128)):
        B, L, C = x.shape
        H = img_size[0]
        W = img_size[1]
        assert L == W * H, \
            f"Input image size ({H}*{W} doesn't match model ({L})."

        shortcut = x
        x = self.norm1(x)

        # spatial restore
        x = rearrange(x, ' b (h w) (c) -> b c h w ', h=H, w=W)
        # bs,hidden_dim,32x32

        x = self.CAB(x)

        # flaten
        x = rearrange(x, ' b c h w -> b (h w) c', h=H, w=W)
        x = x.view(B, H * W, C)

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

#########################################
########### SIM Transformer #############
class SIMTransformerBlock(nn.Module):
    def __init__(self, dim, input_resolution, num_heads, win_size=10, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=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. 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)
        self.CAB = CAB(dim, kernel_size=3, reduction=4, bias=False, act=nn.PReLU())


    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, xm, mask=None, img_size = (128, 128)):
        B, L, C = x.shape
        H = img_size[0]
        W = img_size[1]
        assert L == W * H, \
            f"Input image size ({H}*{W} doesn't match model ({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)
        xm = xm.permute(0, 2, 3, 1)
        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
            shifted_m = torch.roll(xm, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
        else:
            shifted_x = x
            shifted_m = xm

        # 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
        m_windows = window_partition(shifted_m, self.win_size)  # nW*B, win_size, win_size, C  N*C->C
        m_windows = m_windows.view(-1, self.win_size * self.win_size, 1)  # nW*B, win_size*win_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, m_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)

        x = rearrange(x, ' b (h w) (c) -> b c h w ', h=H, w=W)
        # bs,hidden_dim,32x32

        x = self.CAB(x)

        # flaten
        x = rearrange(x, ' b c h w -> b (h w) c', h=H, w=W)

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

        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


#########################################
########### Basic layer of ShadowFormer ################
class BasicShadowFormer(nn.Module):
    def __init__(self, dim, output_dim, input_resolution, depth, num_heads, win_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, use_checkpoint=False,
                 token_projection='linear',token_mlp='ffn',se_layer=False,cab=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint
        # build blocks
        if cab:
            self.blocks = nn.ModuleList([
                CATransformerBlock(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)])
        else:
            self.blocks = nn.ModuleList([
                SIMTransformerBlock(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, xm, mask=None, img_size=(128,128)):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x, xm, mask, img_size)
        return x

    def flops(self):
        flops = 0
        for blk in self.blocks:
            flops += blk.flops()
        return flops


class ShadowFormer(nn.Module):
    def __init__(self, img_size=256, in_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., 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='leff', se_layer=True,
                 dowsample=Downsample, upsample=Upsample, opt=None, **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)
        self.opt = opt
        self.self_feature_lambda = opt.self_feature_lambda

        # 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
        if opt.w_hsv:
            self.input_proj = InputProj(in_channel=7, out_channel=embed_dim, kernel_size=3, stride=1, act_layer=nn.LeakyReLU)
        else:
            self.input_proj = InputProj(in_channel=4, out_channel=embed_dim, kernel_size=3, stride=1, act_layer=nn.LeakyReLU)
        self.output_proj = OutputProj(in_channel=2*embed_dim, out_channel=in_chans, kernel_size=3, stride=1)
        # self.CAB = CAB(embed_dim, kernel_size=3, reduction=4, bias=False, act=nn.PReLU())

        # Encoder
        self.encoderlayer_0 = BasicShadowFormer(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,cab=True)
        self.dowsample_0 = dowsample(embed_dim, embed_dim*2)
        self.encoderlayer_1 = BasicShadowFormer(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, cab=True)
        self.dowsample_1 = dowsample(embed_dim*2, embed_dim*4)
        self.encoderlayer_2 = BasicShadowFormer(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)

        # Bottleneck
        self.conv = BasicShadowFormer(dim=embed_dim*8,
                            output_dim=embed_dim*8,
                            input_resolution=(img_size // (2 ** 3),
                                                img_size // (2 ** 3)),
                            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*8, embed_dim*4)
        self.decoderlayer_0 = BasicShadowFormer(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_1 = upsample(embed_dim*8, embed_dim*2)
        self.decoderlayer_1 = BasicShadowFormer(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, cab=True)
        self.upsample_2 = upsample(embed_dim*4, embed_dim)
        self.decoderlayer_2 = BasicShadowFormer(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,cab=True)
        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, xm, mask=None):
        # Input  Projection
        xi = torch.cat((x, xm), dim=1)
        self.img_size = (x.shape[2], x.shape[3])
        y = self.input_proj(xi)
        y = self.pos_drop(y)

        #Encoder
        conv0 = self.encoderlayer_0(y, xm, mask=mask, img_size = self.img_size)
        pool0 = self.dowsample_0(conv0, img_size = self.img_size)
        m = nn.MaxPool2d(2)
        xm1 = m(xm)
        self.img_size = (int(self.img_size[0]/2), int(self.img_size[1]/2))
        conv1 = self.encoderlayer_1(pool0, xm1, mask=mask, img_size = self.img_size)
        pool1 = self.dowsample_1(conv1, img_size = self.img_size)
        m = nn.MaxPool2d(2)
        xm2 = m(xm1)
        self.img_size = (int(self.img_size[0] / 2), int(self.img_size[1] / 2))
        conv2 = self.encoderlayer_2(pool1, xm2, mask=mask, img_size = self.img_size)
        pool2 = self.dowsample_2(conv2, img_size = self.img_size)
        self.img_size = (int(self.img_size[0] / 2), int(self.img_size[1] / 2))
        m = nn.MaxPool2d(2)
        xm3 = m(xm2)

        # Bottleneck
        conv3 = self.conv(pool2, xm3, mask=mask, img_size = self.img_size)

        #Decoder
        up0 = self.upsample_0(conv3, img_size = self.img_size)
        self.img_size = (int(self.img_size[0] * 2), int(self.img_size[1] * 2))
        deconv0 = torch.cat([up0,conv2],-1)
        deconv0 = self.decoderlayer_0(deconv0, xm2, mask=mask, img_size = self.img_size)

        up1 = self.upsample_1(deconv0, img_size = self.img_size)
        self.img_size = (int(self.img_size[0] * 2), int(self.img_size[1] * 2))
        deconv1 = torch.cat([up1,conv1],-1)
        deconv1 = self.decoderlayer_1(deconv1, xm1, mask=mask, img_size = self.img_size)

        up2 = self.upsample_2(deconv1, img_size = self.img_size)
        self.img_size = (int(self.img_size[0] * 2), int(self.img_size[1] * 2))
        deconv2 = torch.cat([up2,conv0],-1)
        deconv2 = self.decoderlayer_2(deconv2, xm, mask=mask, img_size = self.img_size)

        # Output Projection
        y = self.output_proj(deconv2, img_size = self.img_size) + x[:, :3]
        
        if self.self_feature_lambda:
            return y, conv3
        else:
            return y, None


class ShadowFormerJointMTMT(nn.Module):
    def __init__(self, img_size=256, in_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., 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='leff', se_layer=True,
                 dowsample=Downsample, upsample=Upsample, opt=None, **kwargs):
        super().__init__()

        self.mtmt = create_mtmt_model()
        if opt.mtmt_pretrain_weights:
            self.mtmt.load_state_dict(torch.load(opt.mtmt_pretrain_weights))

        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)
        self.opt = opt
        self.self_feature_lambda = opt.self_feature_lambda

        # 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
        if opt.w_hsv:
            self.input_proj = InputProj(in_channel=7, out_channel=embed_dim, kernel_size=3, stride=1, act_layer=nn.LeakyReLU)
        else:
            self.input_proj = InputProj(in_channel=4, out_channel=embed_dim, kernel_size=3, stride=1, act_layer=nn.LeakyReLU)
        self.output_proj = OutputProj(in_channel=2*embed_dim, out_channel=in_chans, kernel_size=3, stride=1)
        # self.CAB = CAB(embed_dim, kernel_size=3, reduction=4, bias=False, act=nn.PReLU())

        # Encoder
        self.encoderlayer_0 = BasicShadowFormer(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,cab=True)
        self.dowsample_0 = dowsample(embed_dim, embed_dim*2)
        self.encoderlayer_1 = BasicShadowFormer(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, cab=True)
        self.dowsample_1 = dowsample(embed_dim*2, embed_dim*4)
        self.encoderlayer_2 = BasicShadowFormer(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)

        # Bottleneck
        self.conv = BasicShadowFormer(dim=embed_dim*8,
                            output_dim=embed_dim*8,
                            input_resolution=(img_size // (2 ** 3),
                                                img_size // (2 ** 3)),
                            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*8, embed_dim*4)
        self.decoderlayer_0 = BasicShadowFormer(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_1 = upsample(embed_dim*8, embed_dim*2)
        self.decoderlayer_1 = BasicShadowFormer(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, cab=True)
        self.upsample_2 = upsample(embed_dim*4, embed_dim)
        self.decoderlayer_2 = BasicShadowFormer(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,cab=True)
        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, xm, xe, number_per, mask=None, opt=None):
        up_edge, up_shadow, up_subitizing, up_shadow_final = self.mtmt(x)
        x_mask = torch.sigmoid(up_shadow_final[-1])

        loss_shadow = None
        if torch.is_grad_enabled():
            # shadow loss
            labeled_bs = 4
            ## calculate the loss
            ## subitizing loss
            subitizing_loss = losses.sigmoid_mse_loss(up_subitizing[:labeled_bs], number_per[:labeled_bs])
            ## edge loss
            edge_loss = []
            for ix in up_edge:
                edge_loss.append(losses.bce2d_new(ix[:labeled_bs], xe[:labeled_bs], reduction='mean'))
            edge_loss = sum(edge_loss)
            shadow_loss1 = []
            shadow_loss2 = []
            for ix in up_shadow:
                shadow_loss1.append(F.binary_cross_entropy_with_logits(ix[:labeled_bs], xm[:labeled_bs], reduction='mean'))

            for ix in up_shadow_final:
                shadow_loss2.append(F.binary_cross_entropy_with_logits(ix[:labeled_bs], xm[:labeled_bs], reduction='mean'))

            loss_shadow = sum(shadow_loss1) + sum(shadow_loss2)
            loss_shadow = loss_shadow + edge_loss * self.opt.mtmt_edge + subitizing_loss*self.opt.mtmt_subitizing


        # ----------------
        
        # Input  Projection
        xi = torch.cat((x, x_mask), dim=1)
        self.img_size = (x.shape[2], x.shape[3])
        y = self.input_proj(xi)
        y = self.pos_drop(y)

        #Encoder
        conv0 = self.encoderlayer_0(y, x_mask, mask=mask, img_size = self.img_size)
        pool0 = self.dowsample_0(conv0, img_size = self.img_size)
        m = nn.MaxPool2d(2)
        xm1 = m(x_mask)
        self.img_size = (int(self.img_size[0]/2), int(self.img_size[1]/2))
        conv1 = self.encoderlayer_1(pool0, xm1, mask=mask, img_size = self.img_size)
        pool1 = self.dowsample_1(conv1, img_size = self.img_size)
        m = nn.MaxPool2d(2)
        xm2 = m(xm1)
        self.img_size = (int(self.img_size[0] / 2), int(self.img_size[1] / 2))
        conv2 = self.encoderlayer_2(pool1, xm2, mask=mask, img_size = self.img_size)
        pool2 = self.dowsample_2(conv2, img_size = self.img_size)
        self.img_size = (int(self.img_size[0] / 2), int(self.img_size[1] / 2))
        m = nn.MaxPool2d(2)
        xm3 = m(xm2)

        # Bottleneck
        conv3 = self.conv(pool2, xm3, mask=mask, img_size = self.img_size)

        #Decoder
        up0 = self.upsample_0(conv3, img_size = self.img_size)
        self.img_size = (int(self.img_size[0] * 2), int(self.img_size[1] * 2))
        deconv0 = torch.cat([up0,conv2],-1)
        deconv0 = self.decoderlayer_0(deconv0, xm2, mask=mask, img_size = self.img_size)

        up1 = self.upsample_1(deconv0, img_size = self.img_size)
        self.img_size = (int(self.img_size[0] * 2), int(self.img_size[1] * 2))
        deconv1 = torch.cat([up1,conv1],-1)
        deconv1 = self.decoderlayer_1(deconv1, xm1, mask=mask, img_size = self.img_size)

        up2 = self.upsample_2(deconv1, img_size = self.img_size)
        self.img_size = (int(self.img_size[0] * 2), int(self.img_size[1] * 2))
        deconv2 = torch.cat([up2,conv0],-1)
        deconv2 = self.decoderlayer_2(deconv2, x_mask, mask=mask, img_size = self.img_size)

        # Output Projection
        y = self.output_proj(deconv2, img_size = self.img_size) + x[:, :3]
        
        if self.self_feature_lambda:
            return y, x_mask, loss_shadow, conv3
        else:
            return y, x_mask, loss_shadow, None