text: import torch
  import torch.nn as nn
  import torch.nn.functional
  import torch.nn.functional as F
  from functools import partial
  from base_networks import *
  
  import timm
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
  import types
  import math
  from abc import ABCMeta, abstractmethod
  from mmcv.cnn import ConvModule
  import pdb
  
  class EncoderTransformer(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 embedding definitions
          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])
          # for Intra-patch transformer blocks
  
          self.mini_patch_embed1 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0],
                            embed_dim=embed_dims[1])
          self.mini_patch_embed2 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1],
                                                embed_dim=embed_dims[2])
          self.mini_patch_embed3 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_chans=embed_dims[2],
                                                embed_dim=embed_dims[3])
          self.mini_patch_embed4 = OverlapPatchEmbed(img_size=img_size // 32, patch_size=3, stride=2, in_chans=embed_dims[0],
                                                embed_dim=embed_dims[3])
  
          # main  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])
          # intra-patch encoder
          self.patch_block1 = nn.ModuleList([Block(
              dim=embed_dims[1], 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(1)])
          self.pnorm1 = norm_layer(embed_dims[1])
          # main  encoder
          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])
          # intra-patch encoder
          self.patch_block2 = nn.ModuleList([Block(
              dim=embed_dims[2], 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(1)])
          self.pnorm2 = norm_layer(embed_dims[2])
          # main  encoder
          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])
          # intra-patch encoder
          self.patch_block3 = nn.ModuleList([Block(
              dim=embed_dims[3], num_heads=num_heads[1], 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(1)])
          self.pnorm3 = norm_layer(embed_dims[3])
          # main  encoder
          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])
  
          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 init_weights(self, pretrained=None):
          if isinstance(pretrained, str):
              logger = get_root_logger()
              load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
  
      def reset_drop_path(self, drop_path_rate):
          dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
          cur = 0
          for i in range(self.depths[0]):
              self.block1[i].drop_path.drop_prob = dpr[cur + i]
  
          cur += self.depths[0]
          for i in range(self.depths[1]):
              self.block2[i].drop_path.drop_prob = dpr[cur + i]
  
          cur += self.depths[1]
          for i in range(self.depths[2]):
              self.block3[i].drop_path.drop_prob = dpr[cur + i]
  
          cur += self.depths[2]
          for i in range(self.depths[3]):
              self.block4[i].drop_path.drop_prob = dpr[cur + i]
  
      def forward_features(self, x):
          B = x.shape[0]
          outs = []
          embed_dims=[64, 128, 320, 512]
          # stage 1
          x1, H1, W1 = self.patch_embed1(x)
          x2, H2, W2 = self.mini_patch_embed1(x1.permute(0,2,1).reshape(B,embed_dims[0],H1,W1))
  
          for i, blk in enumerate(self.block1):
              x1 = blk(x1, H1, W1)
          x1 = self.norm1(x1)
          x1 = x1.reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
          
          for i, blk in enumerate(self.patch_block1):
              x2 = blk(x2, H2, W2)
          x2 = self.pnorm1(x2)
          x2 = x2.reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous()
  
          outs.append(x1)
  
          # stage 2
          x1, H1, W1 = self.patch_embed2(x1)
          x1 = x1.permute(0,2,1).reshape(B,embed_dims[1],H1,W1)+x2
          x2, H2, W2 = self.mini_patch_embed2(x1)
  
          x1 = x1.view(x1.shape[0],x1.shape[1],-1).permute(0,2,1)
  
          for i, blk in enumerate(self.block2):
              x1 = blk(x1, H1, W1)
          x1 = self.norm2(x1)
          x1 = x1.reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
          outs.append(x1)
  
          for i, blk in enumerate(self.patch_block2):
              x2 = blk(x2, H2, W2)
          x2 = self.pnorm2(x2)
          x2 = x2.reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous()
  
          
          # stage 3
          x1, H1, W1 = self.patch_embed3(x1)
          x1 = x1.permute(0,2,1).reshape(B,embed_dims[2],H1,W1)+x2
          x2, H2, W2 = self.mini_patch_embed3(x1)
  
          x1 = x1.view(x1.shape[0],x1.shape[1],-1).permute(0,2,1)
  
          for i, blk in enumerate(self.block3):
              x1 = blk(x1, H1, W1)
          x1 = self.norm3(x1)
          x1 = x1.reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
          outs.append(x1)
          
          for i, blk in enumerate(self.patch_block3):
              x2 = blk(x2, H2, W2)
          x2 = self.pnorm3(x2)
          x2 = x2.reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous()
  
          # stage 4
          x1, H1, W1 = self.patch_embed4(x1)
          x1 = x1.permute(0,2,1).reshape(B,embed_dims[3],H1,W1)+x2
  
          x1 = x1.view(x1.shape[0],x1.shape[1],-1).permute(0,2,1)
  
          for i, blk in enumerate(self.block4):
              x1 = blk(x1, H1, W1)
          x1 = self.norm4(x1)
          x1 = x1.reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
          outs.append(x1)
  
          return outs
  
      def forward(self, x):
          x = self.forward_features(x)
  
          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)
  
          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):
          # pdb.set_trace()
          x = self.proj(x)
          _, _, H, W = x.shape
          x = x.flatten(2).transpose(1, 2)
          x = self.norm(x)
  
          return x, H, W
  
  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):
      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 Attention_dec(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.task_query = nn.Parameter(torch.randn(1,48,dim))
          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
          task_q = self.task_query
          
          # This is because we fix the task parameters to be of a certain dimension, so with varying batch size, we just stack up the same queries to operate on the entire batch
          if B>1:
              
              task_q = task_q.unsqueeze(0).repeat(B,1,1,1)
              task_q = task_q.squeeze(1)
  
          q = self.q(task_q).reshape(B, task_q.shape[1], 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]
          q = torch.nn.functional.interpolate(q,size= (v.shape[2],v.shape[3]))
          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_dec(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_dec(
              dim,
              num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
              attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
  
          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)
  
          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 = 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 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)
  
          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)
  
          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 = 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 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 DecoderTransformer(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//16, patch_size=3, stride=2, in_chans=embed_dims[3],
                                                embed_dim=embed_dims[3])
  
          # transformer decoder
          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_dec(
              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[0])])
          self.norm1 = norm_layer(embed_dims[3])
  
          cur += depths[0]
          
  
          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 init_weights(self, pretrained=None):
          if isinstance(pretrained, str):
              logger = get_root_logger()
              load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
  
      def reset_drop_path(self, drop_path_rate):
          dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
          cur = 0
          for i in range(self.depths[0]):
              self.block1[i].drop_path.drop_prob = dpr[cur + i]
  
          cur += self.depths[0]
          for i in range(self.depths[1]):
              self.block2[i].drop_path.drop_prob = dpr[cur + i]
  
          cur += self.depths[1]
          for i in range(self.depths[2]):
              self.block3[i].drop_path.drop_prob = dpr[cur + i]
  
          cur += self.depths[2]
          for i in range(self.depths[3]):
              self.block4[i].drop_path.drop_prob = dpr[cur + i]
  
      
      def forward_features(self, x):
          x=x[3]
          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)
  
          return outs
  
      def forward(self, x):
          x = self.forward_features(x)
          # x = self.head(x)
  
          return x
  
  class Tenc(EncoderTransformer):
      def __init__(self, **kwargs):
          super(Tenc, self).__init__(
              patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 4, 4], mlp_ratios=[2, 2, 2, 2],
              qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[4, 2, 2, 1],
              drop_rate=0.0, drop_path_rate=0.1)
  
  class Tdec(DecoderTransformer):
      def __init__(self, **kwargs):
          super(Tdec, 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 convprojection(nn.Module):
      def __init__(self, path=None, **kwargs):
          super(convprojection,self).__init__()
  
          self.convd32x = UpsampleConvLayer(512, 512, kernel_size=4, stride=2)
          self.convd16x = UpsampleConvLayer(512, 320, kernel_size=4, stride=2)
          self.dense_4 = nn.Sequential(ResidualBlock(320))
          self.convd8x = UpsampleConvLayer(320, 128, kernel_size=4, stride=2)
          self.dense_3 = nn.Sequential(ResidualBlock(128))
          self.convd4x = UpsampleConvLayer(128, 64, kernel_size=4, stride=2)
          self.dense_2 = nn.Sequential(ResidualBlock(64))
          self.convd2x = UpsampleConvLayer(64, 16, kernel_size=4, stride=2)
          self.dense_1 = nn.Sequential( ResidualBlock(16))
          self.convd1x = UpsampleConvLayer(16, 8, kernel_size=4, stride=2)
          self.conv_output = ConvLayer(8, 3, kernel_size=3, stride=1, padding=1)
  
          self.active = nn.Tanh()        
  
      def forward(self,x1,x2):
  
          res32x = self.convd32x(x2[0])
  
          if x1[3].shape[3] != res32x.shape[3] and x1[3].shape[2] != res32x.shape[2]:
              p2d = (0,-1,0,-1)
              res32x = F.pad(res32x,p2d,"constant",0)
              
          elif x1[3].shape[3] != res32x.shape[3] and x1[3].shape[2] == res32x.shape[2]:
              p2d = (0,-1,0,0)
              res32x = F.pad(res32x,p2d,"constant",0)
          elif x1[3].shape[3] == res32x.shape[3] and x1[3].shape[2] != res32x.shape[2]:
              p2d = (0,0,0,-1)
              res32x = F.pad(res32x,p2d,"constant",0)
  
          res16x = res32x + x1[3]
          res16x = self.convd16x(res16x) 
  
          if x1[2].shape[3] != res16x.shape[3] and x1[2].shape[2] != res16x.shape[2]:
              p2d = (0,-1,0,-1)
              res16x = F.pad(res16x,p2d,"constant",0)
          elif x1[2].shape[3] != res16x.shape[3] and x1[2].shape[2] == res16x.shape[2]:
              p2d = (0,-1,0,0)
              res16x = F.pad(res16x,p2d,"constant",0)
          elif x1[2].shape[3] == res16x.shape[3] and x1[2].shape[2] != res16x.shape[2]:
              p2d = (0,0,0,-1)
              res16x = F.pad(res16x,p2d,"constant",0)
  
          res8x = self.dense_4(res16x) + x1[2]
          res8x = self.convd8x(res8x) 
          res4x = self.dense_3(res8x) + x1[1]
          res4x = self.convd4x(res4x)
          res2x = self.dense_2(res4x) + x1[0]
          res2x = self.convd2x(res2x)
          x = res2x
          x = self.dense_1(x)
          x = self.convd1x(x)
  
          return x
  
  class convprojection_base(nn.Module):
      def __init__(self, path=None, **kwargs):
          super(convprojection_base,self).__init__()
  
          # self.convd32x = UpsampleConvLayer(512, 512, kernel_size=4, stride=2)
          self.convd16x = UpsampleConvLayer(512, 320, kernel_size=4, stride=2)
          self.dense_4 = nn.Sequential(ResidualBlock(320))
          self.convd8x = UpsampleConvLayer(320, 128, kernel_size=4, stride=2)
          self.dense_3 = nn.Sequential(ResidualBlock(128))
          self.convd4x = UpsampleConvLayer(128, 64, kernel_size=4, stride=2)
          self.dense_2 = nn.Sequential(ResidualBlock(64))
          self.convd2x = UpsampleConvLayer(64, 16, kernel_size=4, stride=2)
          self.dense_1 = nn.Sequential( ResidualBlock(16))
          self.convd1x = UpsampleConvLayer(16, 8, kernel_size=4, stride=2)
          self.conv_output = ConvLayer(8, 3, kernel_size=3, stride=1, padding=1)
  
          self.active = nn.Tanh()        
  
      def forward(self,x1):
  
  #         if x1[3].shape[3] != res32x.shape[3] and x1[3].shape[2] != res32x.shape[2]:
  #             p2d = (0,-1,0,-1)
  #             res32x = F.pad(res32x,p2d,"constant",0)
              
  #         elif x1[3].shape[3] != res32x.shape[3] and x1[3].shape[2] == res32x.shape[2]:
  #             p2d = (0,-1,0,0)
  #             res32x = F.pad(res32x,p2d,"constant",0)
  #         elif x1[3].shape[3] == res32x.shape[3] and x1[3].shape[2] != res32x.shape[2]:
  #             p2d = (0,0,0,-1)
  #             res32x = F.pad(res32x,p2d,"constant",0)
  
  #         res16x = res32x + x1[3]
          res16x = self.convd16x(x1[3]) 
  
          if x1[2].shape[3] != res16x.shape[3] and x1[2].shape[2] != res16x.shape[2]:
              p2d = (0,-1,0,-1)
              res16x = F.pad(res16x,p2d,"constant",0)
          elif x1[2].shape[3] != res16x.shape[3] and x1[2].shape[2] == res16x.shape[2]:
              p2d = (0,-1,0,0)
              res16x = F.pad(res16x,p2d,"constant",0)
          elif x1[2].shape[3] == res16x.shape[3] and x1[2].shape[2] != res16x.shape[2]:
              p2d = (0,0,0,-1)
              res16x = F.pad(res16x,p2d,"constant",0)
  
          res8x = self.dense_4(res16x) + x1[2]
          res8x = self.convd8x(res8x) 
          res4x = self.dense_3(res8x) + x1[1]
          res4x = self.convd4x(res4x)
          res2x = self.dense_2(res4x) + x1[0]
          res2x = self.convd2x(res2x)
          x = res2x
          x = self.dense_1(x)
          x = self.convd1x(x)
  
          return x
  
  
  ## The following is the network which can be fine-tuned for specific datasets
  
  class Transweather_base(nn.Module):
  
      def __init__(self, path=None, **kwargs):
          super(Transweather_base, self).__init__()
  
          self.Tenc = Tenc()
          
          self.convproj = convprojection_base()
  
          self.clean = ConvLayer(8, 3, kernel_size=3, stride=1, padding=1)
  
          self.active = nn.Tanh()
          
          if path is not None:
              self.load(path)
  
      def forward(self, x):
  
          x1 = self.Tenc(x)
  
          x = self.convproj(x1)
  
          clean = self.active(self.clean(x))
  
          return clean
  
      def load(self, path):
          """
          Load checkpoint.
          """
          checkpoint = torch.load(path, map_location=lambda storage, loc: storage)
          model_state_dict_keys = self.state_dict().keys()
          checkpoint_state_dict_noprefix = strip_prefix_if_present(checkpoint['state_dict'], "module.")
          self.load_state_dict(checkpoint_state_dict_noprefix, strict=False)
          del checkpoint
          torch.cuda.empty_cache()
  
  
  ## The following is original network found in paper which solves all-weather removal problems 
  ## using a single model
  
  class Transweather(nn.Module):
  
      def __init__(self, path=None, **kwargs):
          super(Transweather, self).__init__()
  
          self.Tenc = Tenc()
          
          self.Tdec = Tdec()
          
          self.convtail = convprojection()
  
          self.clean = ConvLayer(8, 3, kernel_size=3, stride=1, padding=1)
  
          self.active = nn.Tanh()
          
          if path is not None:
              self.load(path)
  
      def forward(self, x):
  
          x1 = self.Tenc(x)
  
          x2 = self.Tdec(x1)
  
          x = self.convtail(x1,x2)
  
          clean = self.active(self.clean(x))
  
          return clean
  
      def load(self, path):
          """
          Load checkpoint.
          """
          checkpoint = torch.load(path, map_location=lambda storage, loc: storage)
          model_state_dict_keys = self.state_dict().keys()
          checkpoint_state_dict_noprefix = strip_prefix_if_present(checkpoint['state_dict'], "module.")
          self.load_state_dict(checkpoint_state_dict_noprefix, strict=False)
          del checkpoint
          torch.cuda.empty_cache()
  
  
  
  