BBNet.py

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 torchvision import models
from Res import resnet18
import numpy as np

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)

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


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

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


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

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


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

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_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 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        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)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        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, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        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.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)

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

        attn = self.attn_drop(attn)

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

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

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


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

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, 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):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

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

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

        self.register_buffer("atten_mask", atten_mask)

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

        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.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

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

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

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

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

        return x

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

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


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

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

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

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

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

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

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

        return x

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

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


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

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):

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

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

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

    def forward(self, x):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        if self.downsample is not None:
            x = self.downsample(x)
        return x

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

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


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

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

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

        self.in_chans = in_chans
        self.embed_dim = embed_dim

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

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

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

class ASPP_2468(nn.Module):

    def __init__(self, dim_in, dim_out, rate=1, bn_mom=0.1):
        super(ASPP_2468, self).__init__()
        self.branch1 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 1, 1, padding=0, dilation=rate, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )
        self.branch2 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=2 * rate, dilation=2 * rate, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )
        self.branch3 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=4 * rate, dilation=4 * rate, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )
        self.branch4 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=6 * rate, dilation=6 * rate, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )
        self.branch5 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=8 * rate, dilation=8 * rate, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )
    def forward(self, x,randx):
        if randx == 0:
            return self.branch1(x)
        if randx == 1:
            return self.branch2(x)
        if randx == 2:
            return self.branch3(x)
        if randx == 3:
            return self.branch4(x)
        return self.branch5(x)


class DCAM(nn.Module):
    def __init__(self,inc1,inc2,outc):
        super(DCAM, self).__init__()
        self.conv1 = nn.Conv2d(inc1, inc2, kernel_size=1, stride=1, padding=0)
        self.conv2 = nn.Conv2d(inc2, inc1, kernel_size=1, stride=1, padding=0)
        self.conv3 = nn.Conv2d(inc1, outc, kernel_size=1, stride=1, padding=0)
        self.conv4 = nn.Conv2d(inc2, outc, kernel_size=1, stride=1, padding=0)
        self.convAB1 = nn.Conv2d(inc1+inc2, outc, kernel_size=3, stride=1, padding=1)
        self.convAB2 = nn.Conv2d(outc, outc, kernel_size=3, stride=1, padding=1)
        self.bnAB1 = nn.BatchNorm2d(outc)
        self.bnAB2 = nn.BatchNorm2d(outc)
        self.convAB3 = nn.Conv2d(outc, outc, kernel_size=3, stride=1, padding=1)
        self.convAB4 = nn.Conv2d(outc, outc, kernel_size=3, stride=1, padding=1)
        self.bnAB3 = nn.BatchNorm2d(outc)
        self.bnAB4 = nn.BatchNorm2d(outc)
        self.gap = nn.AdaptiveAvgPool2d((1, 1))
        self.convg = nn.Conv2d(inc1, inc1, 1)
        self.sftmax = nn.Softmax(dim=1)

    def forward(self, x, y):
        yy = F.interpolate(self.conv1(x), size=y.size()[2:], mode='bilinear', align_corners=True)
        xx = self.convg(self.gap(self.conv2(y)))
        xx = torch.mul(self.sftmax(xx) * xx.shape[1], x)
        xx = F.interpolate(xx, size=y.size()[2:], mode='bilinear', align_corners=True)
        yy = y*F.sigmoid(yy)
        ans = torch.cat((xx,yy),1)
        ans = F.relu(self.bnAB1(self.convAB1(ans)), inplace=True)
        ans = ans+self.conv4(y)+F.interpolate(self.conv3(x), size=y.size()[2:], mode='bilinear', align_corners=True)
        ans = F.relu(self.bnAB2(self.convAB2(ans)), inplace=True)
        return ans

class Decoder(nn.Module):
    def __init__(self):
        super(Decoder,self).__init__()

        self.SUM1 =DCAM(512,512,64)
        self.SUM2 =DCAM(512,256,64)
        self.SUM3 =DCAM(256,128,64)
        self.SUM4 =DCAM(128,64,64)

        self.aspp = ASPP_2468(64,64)
        self.conv0 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.bn0   = nn.BatchNorm2d(64)
        self.conv1 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.bn1   = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.bn2   = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.bn3   = nn.BatchNorm2d(64)
        self.conv4 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.bn4   = nn.BatchNorm2d(64)       
        self.relu = nn.ReLU(inplace=False)

    def forward(self,s1,s2,s3,s4,x1,x2,x3,x4,randx):

        s4= self.SUM1(s4,x4)
        s3= self.SUM2(s3,x3)
        s2= self.SUM3(s2,x2)
        s1= self.SUM4(s1,x1)

        s=[]
        d=[]
        for i in range(4):
            if i != randx:
                with torch.no_grad():
                    s5_ = self.aspp(s4, i)
                    s5_ = self.relu(self.bn0(self.conv0(s5_)))
                    s5_ = F.interpolate(s5_, size=s4.size()[2:], mode='bilinear', align_corners=True)
                    s4_ = self.relu(self.bn1(self.conv1(s4 + s5_)))
                    s4_ = F.interpolate(s4_, size=s3.size()[2:], mode='bilinear', align_corners=True)
                    s3_ = self.relu(self.bn2(self.conv2(s4_ + s3)))
                    s3_ = F.interpolate(s3_, size=s2.size()[2:], mode='bilinear', align_corners=True)
                    s2_ = self.relu(self.bn3(self.conv3(s3_ + s2)))
                    s2_ = F.interpolate(s2_, size=s1.size()[2:], mode='bilinear', align_corners=True)
                    s1_ = self.relu(self.bn4(self.conv4(s2_ + s1)))
                    s.append(s1_)
            else:
                s5_ = self.aspp(s4, i)
                s5_ = self.relu(self.bn0(self.conv0(s5_)))
                s5_ = F.interpolate(s5_, size=s4.size()[2:], mode='bilinear', align_corners=True)
                s4_ = self.relu(self.bn1(self.conv1(s4 + s5_)))
                s4_ = F.interpolate(s4_, size=s3.size()[2:], mode='bilinear', align_corners=True)
                s3_ = self.relu(self.bn2(self.conv2(s4_ + s3)))
                s3_ = F.interpolate(s3_, size=s2.size()[2:], mode='bilinear', align_corners=True)
                s2_ = self.relu(self.bn3(self.conv3(s3_ + s2)))
                s2_ = F.interpolate(s2_, size=s1.size()[2:], mode='bilinear', align_corners=True)
                s1_ = self.relu(self.bn4(self.conv4(s2_ + s1)))
                s.append(s1_)
                d.append(s2_)
                d.append(s3_)
                d.append(s4_)
        return s,d


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

    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
                 embed_dim=128, depths=[2, 2, 18,2], num_heads=[4, 8, 16, 32],
                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.5,
                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                 use_checkpoint=False, **kwargs):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution

        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.absolute_pos_embed, std=.02)

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

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

        # build layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers-1):
            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
                               input_resolution=(patches_resolution[0] // (2 ** i_layer if i_layer < self.num_layers - 2 else 4),
                                                 patches_resolution[1] // (2 ** i_layer if i_layer < self.num_layers - 2 else 4)),
                               depth=depths[i_layer],
                               num_heads=num_heads[i_layer],
                               window_size=window_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[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                               norm_layer=norm_layer,
                               downsample=PatchMerging if (i_layer < self.num_layers - 2) else None,
                               use_checkpoint=use_checkpoint)
            self.layers.append(layer)

        self.norm = norm_layer(self.num_features)
        self.avgpool = nn.AdaptiveAvgPool1d(1)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

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

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

    def forward_features(self, x):
        b,c,h,w = x.shape
        x = self.patch_embed(x)

        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)
        s = []
        s.append(x.view(b, int((x.shape[1])**0.5),int((x.shape[1])**(0.5)), -1).permute(0, 3, 1, 2).contiguous())
        for layer in self.layers:
            x = layer(x)
            s.append(x.view(b, int((x.shape[1])**0.5),int((x.shape[1])**(0.5)), -1).permute(0, 3, 1, 2).contiguous())

      #  x = self.norm(x)  # B L C
      #  x = self.avgpool(x.transpose(1, 2))  # B C 1
      #  x = torch.flatten(x, 1)
        return s

    def forward(self, x):
        x = self.forward_features(x)
     #   x = self.head(x)
        return x

    def flops(self):
        flops = 0
        flops += self.patch_embed.flops()
        for i, layer in enumerate(self.layers):
            flops += layer.flops()
        return flops



class BBNet(nn.Module):
    def __init__(self, cfg=None):
        super(BBNet, self).__init__()
        self.cfg      = cfg
        self.bkbone   = SwinTransformer(224)
        self.resnet    = resnet18()
        self.bkbone.load_state_dict(torch.load('../model/swin_224.pth'),strict=False)
        self.resnet.load_state_dict(torch.load('../model/resnet18.pth'),strict=False)
        self.decode   = Decoder()
        self.linear1 = nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)
        self.linear2 = nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)
        self.linear3 = nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)
        self.linearr1_88 = nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)
        self.linearr1_80 = nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)
        self.linearr1_72 = nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)
        self.linearr1_64 = nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)
        self.linearr1_56 = nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)

    def forward(self, x,shape=None,mask=None):
        shape = x.size()[2:] if shape is None else shape
        y = F.interpolate(x, size=(224,224), mode='bilinear',align_corners=True)
        x1,x2,x3,x4 = self.resnet(x)
        s1,s2,s3,s4 = self.bkbone(y)
        if self.cfg.mode == 'train':
            randx = np.random.randint(0,4)
            (p0,p1,p2,p3),(pred2,pred3,pred4) = self.decode(s1,s2,s3,s4,x1,x2,x3,x4,randx)
            pred = []
            if randx == 0:
                pred.append(F.interpolate(self.linearr1_88(p0), size=shape, mode='bilinear'))
            else:
                with torch.no_grad():
                    pred.append(F.interpolate(self.linearr1_88(p0), size=shape, mode='bilinear'))
            if randx == 1:
                pred.append(F.interpolate(self.linearr1_80(p1), size=shape, mode='bilinear'))
            else:
                with torch.no_grad():
                    pred.append(F.interpolate(self.linearr1_80(p1), size=shape, mode='bilinear'))
            if randx == 2:
                pred.append(F.interpolate(self.linearr1_72(p2), size=shape, mode='bilinear'))
            else:
                with torch.no_grad():
                    pred.append(F.interpolate(self.linearr1_72(p2), size=shape, mode='bilinear'))
            if randx == 3:
                pred.append(F.interpolate(self.linearr1_64(p3), size=shape, mode='bilinear'))
            else:
                with torch.no_grad():
                    pred.append(F.interpolate(self.linearr1_64(p3), size=shape, mode='bilinear'))
            B,C,W,H = x.shape
            with torch.no_grad():
                w = torch.zeros(B,1,W,H).cuda()
                for i in range(4):
                    if i == ((randx+3)%4):
                        w = w+F.binary_cross_entropy_with_logits(pred[i], mask,reduction='none')
            ma = torch.max(w)
            mi = torch.min(w)
            #print(w.max())
            if ma > mi:
                w = ((w-mi)/(ma-mi))+1
            else:
                w = w+1
            pred2 = F.interpolate(self.linear1(pred2), size=shape, mode='bilinear')
            pred3 = F.interpolate(self.linear2(pred3), size=shape, mode='bilinear')
            pred4 = F.interpolate(self.linear3(pred4), size=shape, mode='bilinear')
            return pred[randx],pred2,pred3,pred4,w
        else:
            (p0,p1,p2,p3),_ = self.decode(s1,s2,s3,s4,x1,x2,x3,x4,-1)
            pred1 = F.interpolate(self.linearr1_88(p0), size=shape, mode='bilinear')
            pred2 = F.interpolate(self.linearr1_80(p1), size=shape, mode='bilinear')
            pred3 = F.interpolate(self.linearr1_72(p2), size=shape, mode='bilinear')
            pred4 = F.interpolate(self.linearr1_64(p3), size=shape, mode='bilinear')
            return (pred1+pred2+pred3+pred4)