# code is borrowed from the original repo and fit into our training framework
# https://github.com/HuCaoFighting/Swin-Unet/tree/4375a8d6fa7d9c38184c5d3194db990a00a3e912


from __future__ import absolute_import

from __future__ import division

from __future__ import print_function


import torch

import torch.nn as nn

import torch.utils.checkpoint as checkpoint

from einops import rearrange

from timm.models.layers import DropPath, to_2tuple, trunc_normal_




import copy

import logging

import math


from os.path import join as pjoin

import numpy as np



from torch.nn import CrossEntropyLoss, Dropout, Softmax, Linear, Conv2d, LayerNorm

from torch.nn.modules.utils import _pair

from scipy import ndimage





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)

            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)

            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))

        else:

            attn_mask = None



        self.register_buffer("attn_mask", attn_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.attn_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 PatchExpand(nn.Module):

    def __init__(self, input_resolution, dim, dim_scale=2, norm_layer=nn.LayerNorm):

        super().__init__()

        self.input_resolution = input_resolution

        self.dim = dim

        self.expand = nn.Linear(dim, 2*dim, bias=False) if dim_scale==2 else nn.Identity()

        self.norm = norm_layer(dim // dim_scale)



    def forward(self, x):

        """

        x: B, H*W, C

        """

        H, W = self.input_resolution

        x = self.expand(x)

        B, L, C = x.shape

        assert L == H * W, "input feature has wrong size"



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

        x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=2, p2=2, c=C//4)

        x = x.view(B,-1,C//4)

        x= self.norm(x)



        return x



class FinalPatchExpand_X4(nn.Module):

    def __init__(self, input_resolution, dim, dim_scale=4, norm_layer=nn.LayerNorm):

        super().__init__()

        self.input_resolution = input_resolution

        self.dim = dim

        self.dim_scale = dim_scale

        self.expand = nn.Linear(dim, 16*dim, bias=False)

        self.output_dim = dim 

        self.norm = norm_layer(self.output_dim)



    def forward(self, x):

        """

        x: B, H*W, C

        """

        H, W = self.input_resolution

        x = self.expand(x)

        B, L, C = x.shape

        assert L == H * W, "input feature has wrong size"



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

        x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=self.dim_scale, p2=self.dim_scale, c=C//(self.dim_scale**2))

        x = x.view(B,-1,self.output_dim)

        x= self.norm(x)



        return x



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 BasicLayer_up(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, upsample=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 upsample is not None:

            self.upsample = PatchExpand(input_resolution, dim=dim, dim_scale=2, norm_layer=norm_layer)

        else:

            self.upsample = 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.upsample is not None:

            x = self.upsample(x)

        return x



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 SwinTransformerSys(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=96, depths=[2, 2, 2, 2], depths_decoder=[1, 2, 2, 2], num_heads=[3, 6, 12, 24],

                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,

                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,

                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,

                 use_checkpoint=False, final_upsample="expand_first", **kwargs):

        super().__init__()



        print("SwinTransformerSys expand initial----depths:{};depths_decoder:{};drop_path_rate:{};num_classes:{}".format(depths,

        depths_decoder,drop_path_rate,num_classes))



        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.num_features_up = int(embed_dim * 2)

        self.mlp_ratio = mlp_ratio

        self.final_upsample = final_upsample



        # 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 encoder and bottleneck layers

        self.layers = nn.ModuleList()

        for i_layer in range(self.num_layers):

            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),

                               input_resolution=(patches_resolution[0] // (2 ** i_layer),

                                                 patches_resolution[1] // (2 ** i_layer)),

                               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 - 1) else None,

                               use_checkpoint=use_checkpoint)

            self.layers.append(layer)

        

        # build decoder layers

        self.layers_up = nn.ModuleList()

        self.concat_back_dim = nn.ModuleList()

        for i_layer in range(self.num_layers):

            concat_linear = nn.Linear(2*int(embed_dim*2**(self.num_layers-1-i_layer)),

            int(embed_dim*2**(self.num_layers-1-i_layer))) if i_layer > 0 else nn.Identity()

            if i_layer ==0 :

                layer_up = PatchExpand(input_resolution=(patches_resolution[0] // (2 ** (self.num_layers-1-i_layer)),

                patches_resolution[1] // (2 ** (self.num_layers-1-i_layer))), dim=int(embed_dim * 2 ** (self.num_layers-1-i_layer)), dim_scale=2, norm_layer=norm_layer)

            else:

                layer_up = BasicLayer_up(dim=int(embed_dim * 2 ** (self.num_layers-1-i_layer)),

                                input_resolution=(patches_resolution[0] // (2 ** (self.num_layers-1-i_layer)),

                                                    patches_resolution[1] // (2 ** (self.num_layers-1-i_layer))),

                                depth=depths[(self.num_layers-1-i_layer)],

                                num_heads=num_heads[(self.num_layers-1-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[:(self.num_layers-1-i_layer)]):sum(depths[:(self.num_layers-1-i_layer) + 1])],

                                norm_layer=norm_layer,

                                upsample=PatchExpand if (i_layer < self.num_layers - 1) else None,

                                use_checkpoint=use_checkpoint)

            self.layers_up.append(layer_up)

            self.concat_back_dim.append(concat_linear)



        self.norm = norm_layer(self.num_features)

        self.norm_up= norm_layer(self.embed_dim)



        if self.final_upsample == "expand_first":

            print("---final upsample expand_first---")

            self.up = FinalPatchExpand_X4(input_resolution=(img_size//patch_size,img_size//patch_size),dim_scale=4,dim=embed_dim)

            self.output = nn.Conv2d(in_channels=embed_dim,out_channels=self.num_classes,kernel_size=1,bias=False)



        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'}



    #Encoder and Bottleneck

    def forward_features(self, x):

        x = self.patch_embed(x)

        if self.ape:

            x = x + self.absolute_pos_embed

        x = self.pos_drop(x)

        x_downsample = []



        for layer in self.layers:

            x_downsample.append(x)

            x = layer(x)



        x = self.norm(x)  # B L C

  

        return x, x_downsample



    #Dencoder and Skip connection

    def forward_up_features(self, x, x_downsample):

        for inx, layer_up in enumerate(self.layers_up):

            if inx == 0:

                x = layer_up(x)

            else:

                x = torch.cat([x,x_downsample[3-inx]],-1)

                x = self.concat_back_dim[inx](x)

                x = layer_up(x)



        x = self.norm_up(x)  # B L C

  

        return x



    def up_x4(self, x):

        H, W = self.patches_resolution

        B, L, C = x.shape

        assert L == H*W, "input features has wrong size"



        if self.final_upsample=="expand_first":

            x = self.up(x)

            x = x.view(B,4*H,4*W,-1)

            x = x.permute(0,3,1,2) #B,C,H,W

            x = self.output(x)

            

        return x



    def forward(self, x):

        x, x_downsample = self.forward_features(x)

        x = self.forward_up_features(x,x_downsample)

        x = self.up_x4(x)



        return x



    def flops(self):

        flops = 0

        flops += self.patch_embed.flops()

        for i, layer in enumerate(self.layers):

            flops += layer.flops()

        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)

        flops += self.num_features * self.num_classes

        return flops




logger = logging.getLogger(__name__)



class SwinUnet_config():
    def __init__(self):
        self.patch_size = 4
        self.in_chans = 3
        self.num_classes = 4
        self.embed_dim = 96
        self.depths = [2, 2, 6, 2]
        self.num_heads = [3, 6, 12, 24]
        self.window_size = 7
        self.mlp_ratio = 4.
        self.qkv_bias = True
        self.qk_scale = None
        self.drop_rate = 0.
        self.drop_path_rate = 0.1
        self.ape = False
        self.patch_norm = True
        self.use_checkpoint = False

class SwinUnet(nn.Module):

    def __init__(self, config, img_size=224, num_classes=21843, zero_head=False, vis=False):

        super(SwinUnet, self).__init__()

        self.num_classes = num_classes

        self.zero_head = zero_head

        self.config = config



        self.swin_unet = SwinTransformerSys(img_size=img_size,

                                patch_size=config.patch_size,

                                in_chans=config.in_chans,

                                num_classes=self.num_classes,

                                embed_dim=config.embed_dim,

                                depths=config.depths,

                                num_heads=config.num_heads,

                                window_size=config.window_size,

                                mlp_ratio=config.mlp_ratio,

                                qkv_bias=config.qkv_bias,

                                qk_scale=config.qk_scale,

                                drop_rate=config.drop_rate,

                                drop_path_rate=config.drop_path_rate,

                                ape=config.ape,

                                patch_norm=config.patch_norm,

                                use_checkpoint=config.use_checkpoint)



    def forward(self, x):

        if x.size()[1] == 1:

            x = x.repeat(1,3,1,1)

        logits = self.swin_unet(x)

        return logits



    def load_from(self, pretrained_path):


        if pretrained_path is not None:

            print("pretrained_path:{}".format(pretrained_path))

            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

            pretrained_dict = torch.load(pretrained_path, map_location=device)

            if "model"  not in pretrained_dict:

                print("---start load pretrained modle by splitting---")

                pretrained_dict = {k[17:]:v for k,v in pretrained_dict.items()}

                for k in list(pretrained_dict.keys()):

                    if "output" in k:

                        print("delete key:{}".format(k))

                        del pretrained_dict[k]

                msg = self.swin_unet.load_state_dict(pretrained_dict,strict=False)

                # print(msg)

                return

            pretrained_dict = pretrained_dict['model']

            print("---start load pretrained modle of swin encoder---")



            model_dict = self.swin_unet.state_dict()

            full_dict = copy.deepcopy(pretrained_dict)

            for k, v in pretrained_dict.items():

                if "layers." in k:

                    current_layer_num = 3-int(k[7:8])

                    current_k = "layers_up." + str(current_layer_num) + k[8:]

                    full_dict.update({current_k:v})

            for k in list(full_dict.keys()):

                if k in model_dict:

                    if full_dict[k].shape != model_dict[k].shape:

                        print("delete:{};shape pretrain:{};shape model:{}".format(k,v.shape,model_dict[k].shape))

                        del full_dict[k]



            msg = self.swin_unet.load_state_dict(full_dict, strict=False)

            # print(msg)

        else:

            print("none pretrain")
