model.py

import math
import warnings
from functools import partial

import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from torch.nn import Conv2d, Module
from lib.pvtv2 import pvt_v2_b2


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.dwconv = DWConv(hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

        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)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

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


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
            self.norm = nn.LayerNorm(dim)

        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)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x, H, W):
        B, N, C = x.shape
        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)

        if self.sr_ratio > 1:
            x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
            x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
            x_ = self.norm(x_)
            kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        else:
            kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        k, v = kv[0], kv[1]

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        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


class Block(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
            attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        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)

    def forward(self, x, H, W):
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        x = x + self.drop_path(self.mlp(self.norm2(x), H, W))

        return x


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

    def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)

        self.img_size = img_size
        self.patch_size = patch_size
        self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
        self.num_patches = self.H * self.W
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
                              padding=(patch_size[0] // 2, patch_size[1] // 2))
        self.norm = nn.LayerNorm(embed_dim)

    def forward(self, x):
        x = self.proj(x)
        _, _, H, W = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = self.norm(x)

        return x, H, W


class MixVisionTransformer(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
                 num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
                 depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
        super().__init__()
        self.num_classes = num_classes
        self.depths = depths

        # patch_embed
        self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_chans=in_chans,
                                              embed_dim=embed_dims[0])
        self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0],
                                              embed_dim=embed_dims[1])
        self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1],
                                              embed_dim=embed_dims[2])
        self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_chans=embed_dims[2],
                                              embed_dim=embed_dims[3])

        # transformer encoder
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
        cur = 0
        self.block1 = nn.ModuleList([Block(
            dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[0])
            for i in range(depths[0])])
        self.norm1 = norm_layer(embed_dims[0])

        cur += depths[0]
        self.block2 = nn.ModuleList([Block(
            dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[1])
            for i in range(depths[1])])
        self.norm2 = norm_layer(embed_dims[1])

        cur += depths[1]
        self.block3 = nn.ModuleList([Block(
            dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[2])
            for i in range(depths[2])])
        self.norm3 = norm_layer(embed_dims[2])

        cur += depths[2]
        self.block4 = nn.ModuleList([Block(
            dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[3])
            for i in range(depths[3])])
        self.norm4 = norm_layer(embed_dims[3])

    def forward_features(self, x):
        B = x.shape[0]
        outs = []

        # stage 1
        x, H, W = self.patch_embed1(x)
        for i, blk in enumerate(self.block1):
            x = blk(x, H, W)
        x = self.norm1(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
        outs.append(x)

        # stage 2
        x, H, W = self.patch_embed2(x)
        for i, blk in enumerate(self.block2):
            x = blk(x, H, W)
        x = self.norm2(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
        outs.append(x)

        # stage 3
        x, H, W = self.patch_embed3(x)
        for i, blk in enumerate(self.block3):
            x = blk(x, H, W)
        x = self.norm3(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
        outs.append(x)

        # stage 4
        x, H, W = self.patch_embed4(x)
        for i, blk in enumerate(self.block4):
            x = blk(x, H, W)
        x = self.norm4(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
        outs.append(x)

        return outs

    def forward(self, x):
        x = self.forward_features(x)

        #        x = self.head(x[3])

        return x


class DWConv(nn.Module):
    def __init__(self, dim=768):
        super(DWConv, self).__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)

    def forward(self, x, H, W):
        B, N, C = x.shape
        x = x.transpose(1, 2).view(B, C, H, W)
        x = self.dwconv(x)
        x = x.flatten(2).transpose(1, 2)

        return x


class mit_b0(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b0, self).__init__(
            patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b1(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b1, self).__init__(
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b2(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b2, self).__init__(
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b3(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b3, self).__init__(
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b4(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b4, self).__init__(
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b5(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b5, self).__init__(
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)




def resize(input,
           size=None,
           scale_factor=None,
           mode='nearest',
           align_corners=None,
           warning=True):
    if warning:
        if size is not None and align_corners:
            input_h, input_w = tuple(int(x) for x in input.shape[2:])
            output_h, output_w = tuple(int(x) for x in size)
            if output_h > input_h or output_w > output_h:
                if ((output_h > 1 and output_w > 1 and input_h > 1
                     and input_w > 1) and (output_h - 1) % (input_h - 1)
                        and (output_w - 1) % (input_w - 1)):
                    warnings.warn(
                        f'When align_corners={align_corners}, '
                        'the output would more aligned if '
                        f'input size {(input_h, input_w)} is `x+1` and '
                        f'out size {(output_h, output_w)} is `nx+1`')
    return F.interpolate(input, size, scale_factor, mode, align_corners)


class MLP(nn.Module):
    """
    Linear Embedding
    """

    def __init__(self, input_dim=512, embed_dim=768):
        super().__init__()
        self.proj = nn.Linear(input_dim, embed_dim)

    def forward(self, x):
        x = x.flatten(2).transpose(1, 2)
        x = self.proj(x)
        return x


class conv(nn.Module):
    """
    Linear Embedding
    """

    def __init__(self, input_dim=512, embed_dim=768, k_s=3):
        super().__init__()

        self.proj = nn.Sequential(nn.Conv2d(input_dim, embed_dim, 3, padding=1, bias=False), nn.ReLU(),
                                  nn.Conv2d(embed_dim, embed_dim, 3, padding=1, bias=False), nn.ReLU())

    def forward(self, x):
        x = self.proj(x)
        x = x.flatten(2).transpose(1, 2)
        return x


class SSFormerDecoder(Module):
    """
    Optain from Decoder of SSFormer
    """

    def __init__(self, dims, dim, class_num=2):
        super().__init__()
        self.num_classes = class_num

        c1_in_channels, c2_in_channels, c3_in_channels, c4_in_channels = dims[0], dims[1], dims[2], dims[3]
        embedding_dim = dim

        self.linear_c4 = conv(input_dim=c4_in_channels, embed_dim=embedding_dim)
        self.linear_c3 = conv(input_dim=c3_in_channels, embed_dim=embedding_dim)
        self.linear_c2 = conv(input_dim=c2_in_channels, embed_dim=embedding_dim)
        self.linear_c1 = conv(input_dim=c1_in_channels, embed_dim=embedding_dim)

        self.linear_fuse = ConvModule(in_channels=embedding_dim * 4, out_channels=embedding_dim, kernel_size=1,norm_cfg=dict(type='BN', requires_grad=True))
        self.linear_fuse34 = ConvModule(in_channels=embedding_dim * 2, out_channels=embedding_dim, kernel_size=1,norm_cfg=dict(type='BN', requires_grad=True))
        self.linear_fuse2 = ConvModule(in_channels=embedding_dim * 2, out_channels=embedding_dim, kernel_size=1,norm_cfg=dict(type='BN', requires_grad=True))
        self.linear_fuse1 = ConvModule(in_channels=embedding_dim * 2, out_channels=embedding_dim, kernel_size=1,norm_cfg=dict(type='BN', requires_grad=True))

        self.linear_pred = Conv2d(embedding_dim, self.num_classes, kernel_size=1)
        self.dropout = nn.Dropout(0.1)

    def forward(self, inputs):
        c1, c2, c3, c4 = inputs
        ############## MLP decoder on C1-C4 ###########
        n, _, h, w = c4.shape
        
        _c4 = self.linear_c4(c4).permute(0, 2, 1).reshape(n, -1, c4.shape[2], c4.shape[3])
        _c4 = resize(_c4, size=c1.size()[2:], mode='bilinear', align_corners=False)
        _c3 = self.linear_c3(c3).permute(0, 2, 1).reshape(n, -1, c3.shape[2], c3.shape[3])
        _c3 = resize(_c3, size=c1.size()[2:], mode='bilinear', align_corners=False)
        _c2 = self.linear_c2(c2).permute(0, 2, 1).reshape(n, -1, c2.shape[2], c2.shape[3])
        _c2 = resize(_c2, size=c1.size()[2:], mode='bilinear', align_corners=False)
        _c1 = self.linear_c1(c1).permute(0, 2, 1).reshape(n, -1, c1.shape[2], c1.shape[3])

        L34 = self.linear_fuse34(torch.cat([_c4, _c3], dim=1))
        L2 = self.linear_fuse2(torch.cat([L34, _c2], dim=1))
        _c = self.linear_fuse1(torch.cat([L2, _c1], dim=1))


        x = self.dropout(_c)
        x = self.linear_pred(x)

        x = F.interpolate(x, scale_factor=4, mode='bilinear')

        return x


class BasicConv2d(nn.Module):
    def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1):
        super(BasicConv2d, self).__init__()

        self.conv = nn.Conv2d(in_planes, out_planes,
                              kernel_size=kernel_size, stride=stride,
                              padding=padding, dilation=dilation, bias=False)
        self.bn = nn.BatchNorm2d(out_planes)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        return x


class CFM(nn.Module):
    def __init__(self, channel):
        super(CFM, self).__init__()
        self.relu = nn.ReLU(True)

        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        self.conv_upsample1 = BasicConv2d(channel, channel, 3, padding=1)
        self.conv_upsample2 = BasicConv2d(channel, channel, 3, padding=1)
        self.conv_upsample3 = BasicConv2d(channel, channel, 3, padding=1)
        self.conv_upsample4 = BasicConv2d(channel, channel, 3, padding=1)
        self.conv_upsample5 = BasicConv2d(2 * channel, 2 * channel, 3, padding=1)

        self.conv_concat2 = BasicConv2d(2 * channel, 2 * channel, 3, padding=1)
        self.conv_concat3 = BasicConv2d(3 * channel, 3 * channel, 3, padding=1)
        self.conv4 = BasicConv2d(3 * channel, channel, 3, padding=1)

    def forward(self, x1, x2, x3):
        x1_1 = x1
        x2_1 = self.conv_upsample1(self.upsample(x1)) * x2
        x3_1 = self.conv_upsample2(self.upsample(self.upsample(x1))) \
               * self.conv_upsample3(self.upsample(x2)) * x3

        x2_2 = torch.cat((x2_1, self.conv_upsample4(self.upsample(x1_1))), 1)
        x2_2 = self.conv_concat2(x2_2)

        x3_2 = torch.cat((x3_1, self.conv_upsample5(self.upsample(x2_2))), 1)
        x3_2 = self.conv_concat3(x3_2)

        x1 = self.conv4(x3_2)

        return x1


class GCN(nn.Module):
    def __init__(self, num_state, num_node, bias=False):
        super(GCN, self).__init__()
        self.conv1 = nn.Conv1d(num_node, num_node, kernel_size=1)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv1d(num_state, num_state, kernel_size=1, bias=bias)

    def forward(self, x):
        h = self.conv1(x.permute(0, 2, 1)).permute(0, 2, 1)
        h = h - x
        h = self.relu(self.conv2(h))
        return h


class SAM(nn.Module):
    def __init__(self, num_in=32, plane_mid=16, mids=4, normalize=False):
        super(SAM, self).__init__()

        self.normalize = normalize
        self.num_s = int(plane_mid)
        self.num_n = (mids) * (mids)
        self.priors = nn.AdaptiveAvgPool2d(output_size=(mids + 2, mids + 2))

        self.conv_state = nn.Conv2d(num_in, self.num_s, kernel_size=1)
        self.conv_proj = nn.Conv2d(num_in, self.num_s, kernel_size=1)
        self.gcn = GCN(num_state=self.num_s, num_node=self.num_n)
        self.conv_extend = nn.Conv2d(self.num_s, num_in, kernel_size=1, bias=False)

    def forward(self, x, edge):
        edge = F.upsample(edge, (x.size()[-2], x.size()[-1]))

        n, c, h, w = x.size()
        edge = torch.nn.functional.softmax(edge, dim=1)[:, 1, :, :].unsqueeze(1)

        x_state_reshaped = self.conv_state(x).view(n, self.num_s, -1)
        x_proj = self.conv_proj(x)
        x_mask = x_proj * edge

        x_anchor1 = self.priors(x_mask)
        x_anchor2 = self.priors(x_mask)[:, :, 1:-1, 1:-1].reshape(n, self.num_s, -1)
        x_anchor = self.priors(x_mask)[:, :, 1:-1, 1:-1].reshape(n, self.num_s, -1)

        x_proj_reshaped = torch.matmul(x_anchor.permute(0, 2, 1), x_proj.reshape(n, self.num_s, -1))
        x_proj_reshaped = torch.nn.functional.softmax(x_proj_reshaped, dim=1)

        x_rproj_reshaped = x_proj_reshaped

        x_n_state = torch.matmul(x_state_reshaped, x_proj_reshaped.permute(0, 2, 1))
        if self.normalize:
            x_n_state = x_n_state * (1. / x_state_reshaped.size(2))
        x_n_rel = self.gcn(x_n_state)

        x_state_reshaped = torch.matmul(x_n_rel, x_rproj_reshaped)
        x_state = x_state_reshaped.view(n, self.num_s, *x.size()[2:])
        out = x + (self.conv_extend(x_state))

        return out


class ChannelAttention(nn.Module):
    def __init__(self, in_planes, ratio=16):
        super(ChannelAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)

        self.fc1   = nn.Conv2d(in_planes, in_planes // 16, 1, bias=False)
        self.relu1 = nn.ReLU()
        self.fc2   = nn.Conv2d(in_planes // 16, in_planes, 1, bias=False)

        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
        max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
        out = avg_out + max_out
        return self.sigmoid(out)


class SpatialAttention(nn.Module):
    def __init__(self, kernel_size=7):
        super(SpatialAttention, self).__init__()

        assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
        padding = 3 if kernel_size == 7 else 1

        self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = torch.mean(x, dim=1, keepdim=True)
        max_out, _ = torch.max(x, dim=1, keepdim=True)
        x = torch.cat([avg_out, max_out], dim=1)
        x = self.conv1(x)
        return self.sigmoid(x)


class SegmHead(nn.Module):
    def __init__(self, channel=32):
        super().__init__()

        self.Translayer2_0 = BasicConv2d(64, channel, 1)
        self.Translayer2_1 = BasicConv2d(128, channel, 1)
        self.Translayer3_1 = BasicConv2d(320, channel, 1)
        self.Translayer4_1 = BasicConv2d(512, channel, 1)

        self.CFM = CFM(channel)
        self.ca = ChannelAttention(64)
        self.sa = SpatialAttention()
        self.SAM = SAM()
        
        self.down05 = nn.Upsample(scale_factor=0.5, mode='bilinear', align_corners=True)
        self.out_SAM = nn.Conv2d(channel, 1, 1)
        self.out_CFM = nn.Conv2d(channel, 1, 1)


    def forward(self, x):

        # backbone
        pvt = self.backbone(x)
        x1 = pvt[0]
        x2 = pvt[1]
        x3 = pvt[2]
        x4 = pvt[3]
        
        # CIM
        x1 = self.ca(x1) * x1 # channel attention
        cim_feature = self.sa(x1) * x1 # spatial attention


        # CFM
        x2_t = self.Translayer2_1(x2)  
        x3_t = self.Translayer3_1(x3)  
        x4_t = self.Translayer4_1(x4)  
        cfm_feature = self.CFM(x4_t, x3_t, x2_t)

        # SAM
        T2 = self.Translayer2_0(cim_feature)
        T2 = self.down05(T2)
        sam_feature = self.SAM(cfm_feature, T2)

        prediction1 = self.out_CFM(cfm_feature)
        prediction2 = self.out_SAM(sam_feature)

        prediction1_8 = F.interpolate(prediction1, scale_factor=8, mode='bilinear') 
        prediction2_8 = F.interpolate(prediction2, scale_factor=8, mode='bilinear')  
        return prediction1_8, prediction2_8

class HRSeg(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = pvt_v2_b2()
        self.segm_head = SSFormerDecoder(dims=[64, 128, 320, 512], dim=256, class_num=1)
        self.att_head = nn.Sequential(
            SSFormerDecoder(dims=[64, 128, 320, 512], dim=256, class_num=1),
            nn.Sigmoid()
        )
        self._init_weights()

    def _init_weights(self):
        pretrained_dict = torch.load('pretrained_pth/pvt_v2_b2.pth')
        model_dict = self.encoder.state_dict()
        pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
        model_dict.update(pretrained_dict)
        self.encoder.load_state_dict(model_dict)
        print("Loaded state dict for encoder: pretrained_pth/pvt_v2_b2.pth")

if __name__ == "__main__":
    x1 = torch.rand(1, 64, 72, 72)
    x2 = torch.rand(1, 128, 36, 36)
    x3 = torch.rand(1, 320, 18, 18)
    x4 = torch.rand(1, 512, 9, 9)