uformer_arch.py

"""
## Uformer: A General U-Shaped Transformer for Image Restoration
## Zhendong Wang, Xiaodong Cun, Jianmin Bao, Jianzhuang Liu
## https://arxiv.org/abs/2106.03106
"""

import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
import torch.nn.functional as F
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
import math
import numpy as np
import time
from torch import einsum
from basicsr.utils.registry import ARCH_REGISTRY


#########################################
class ConvBlock(nn.Module):
    def __init__(self, in_channel, out_channel, strides=1):
        super(ConvBlock, self).__init__()
        self.strides = strides
        self.in_channel = in_channel
        self.out_channel = out_channel
        self.block = nn.Sequential(
            nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=strides, padding=1),
            nn.LeakyReLU(inplace=True),
            nn.Conv2d(out_channel, out_channel, kernel_size=3, stride=strides, padding=1),
            nn.LeakyReLU(inplace=True),
        )
        self.conv11 = nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=strides, padding=0)

    def forward(self, x):
        out1 = self.block(x)
        out2 = self.conv11(x)
        out = out1 + out2
        return out

    def flops(self, H, W):
        flops = H * W * self.in_channel * self.out_channel * (
                    3 * 3 + 1) + H * W * self.out_channel * self.out_channel * 3 * 3
        return flops





#########################################
class PosCNN(nn.Module):
    def __init__(self, in_chans, embed_dim=768, s=1):
        super(PosCNN, self).__init__()
        self.proj = nn.Sequential(nn.Conv2d(in_chans, embed_dim, 3, s, 1, bias=True, groups=embed_dim))
        self.s = s

    def forward(self, x, H=None, W=None):
        B, N, C = x.shape
        H = H or int(math.sqrt(N))
        W = W or int(math.sqrt(N))
        feat_token = x
        cnn_feat = feat_token.transpose(1, 2).view(B, C, H, W)
        if self.s == 1:
            x = self.proj(cnn_feat) + cnn_feat
        else:
            x = self.proj(cnn_feat)
        x = x.flatten(2).transpose(1, 2)
        return x

    def no_weight_decay(self):
        return ['proj.%d.weight' % i for i in range(4)]


class SELayer(nn.Module):
    def __init__(self, channel, reduction=16):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool1d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):  # x: [B, N, C]
        x = torch.transpose(x, 1, 2)  # [B, C, N]
        b, c, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1)
        x = x * y.expand_as(x)
        x = torch.transpose(x, 1, 2)  # [B, N, C]
        return x


class SepConv2d(torch.nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1, act_layer=nn.ReLU):
        super(SepConv2d, self).__init__()
        self.depthwise = torch.nn.Conv2d(in_channels,
                                         in_channels,
                                         kernel_size=kernel_size,
                                         stride=stride,
                                         padding=padding,
                                         dilation=dilation,
                                         groups=in_channels)
        self.pointwise = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.act_layer = act_layer() if act_layer is not None else nn.Identity()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride

    def forward(self, x):
        x = self.depthwise(x)
        x = self.act_layer(x)
        x = self.pointwise(x)
        return x

    def flops(self, H, W):
        flops = 0
        flops += H * W * self.in_channels * self.kernel_size ** 2 / self.stride ** 2
        flops += H * W * self.in_channels * self.out_channels
        return flops


#########################################
######## Embedding for q,k,v ########
class ConvProjection(nn.Module):
    def __init__(self, dim, heads=8, dim_head=64, kernel_size=3, q_stride=1, k_stride=1, v_stride=1, dropout=0.,
                 last_stage=False, bias=True):
        super().__init__()

        inner_dim = dim_head * heads
        self.heads = heads
        pad = (kernel_size - q_stride) // 2
        self.to_q = SepConv2d(dim, inner_dim, kernel_size, q_stride, pad, bias)
        self.to_k = SepConv2d(dim, inner_dim, kernel_size, k_stride, pad, bias)
        self.to_v = SepConv2d(dim, inner_dim, kernel_size, v_stride, pad, bias)

    def forward(self, x, attn_kv=None):
        b, n, c, h = *x.shape, self.heads
        l = int(math.sqrt(n))
        w = int(math.sqrt(n))

        attn_kv = x if attn_kv is None else attn_kv
        x = rearrange(x, 'b (l w) c -> b c l w', l=l, w=w)
        attn_kv = rearrange(attn_kv, 'b (l w) c -> b c l w', l=l, w=w)
        # print(attn_kv)
        q = self.to_q(x)
        q = rearrange(q, 'b (h d) l w -> b h (l w) d', h=h)

        k = self.to_k(attn_kv)
        v = self.to_v(attn_kv)
        k = rearrange(k, 'b (h d) l w -> b h (l w) d', h=h)
        v = rearrange(v, 'b (h d) l w -> b h (l w) d', h=h)
        return q, k, v

    def flops(self, H, W):
        flops = 0
        flops += self.to_q.flops(H, W)
        flops += self.to_k.flops(H, W)
        flops += self.to_v.flops(H, W)
        return flops


class LinearProjection(nn.Module):
    def __init__(self, dim, heads=8, dim_head=64, dropout=0., bias=True):
        super().__init__()
        inner_dim = dim_head * heads
        self.heads = heads
        self.to_q = nn.Linear(dim, inner_dim, bias=bias)
        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=bias)
        self.dim = dim
        self.inner_dim = inner_dim

    def forward(self, x, attn_kv=None):
        B_, N, C = x.shape
        attn_kv = x if attn_kv is None else attn_kv
        q = self.to_q(x).reshape(B_, N, 1, self.heads, C // self.heads).permute(2, 0, 3, 1, 4)
        kv = self.to_kv(attn_kv).reshape(B_, N, 2, self.heads, C // self.heads).permute(2, 0, 3, 1, 4)
        q = q[0]
        k, v = kv[0], kv[1]
        return q, k, v

    def flops(self, H, W):
        flops = H * W * self.dim * self.inner_dim * 3
        return flops


class LinearProjection_Concat_kv(nn.Module):
    def __init__(self, dim, heads=8, dim_head=64, dropout=0., bias=True):
        super().__init__()
        inner_dim = dim_head * heads
        self.heads = heads
        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=bias)
        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=bias)
        self.dim = dim
        self.inner_dim = inner_dim

    def forward(self, x, attn_kv=None):
        B_, N, C = x.shape
        attn_kv = x if attn_kv is None else attn_kv
        qkv_dec = self.to_qkv(x).reshape(B_, N, 3, self.heads, C // self.heads).permute(2, 0, 3, 1, 4)
        kv_enc = self.to_kv(attn_kv).reshape(B_, N, 2, self.heads, C // self.heads).permute(2, 0, 3, 1, 4)
        q, k_d, v_d = qkv_dec[0], qkv_dec[1], qkv_dec[2]  # make torchscript happy (cannot use tensor as tuple)
        k_e, v_e = kv_enc[0], kv_enc[1]
        k = torch.cat((k_d, k_e), dim=2)
        v = torch.cat((v_d, v_e), dim=2)
        return q, k, v

    def flops(self, H, W):
        flops = H * W * self.dim * self.inner_dim * 5
        return flops

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


########### window-based self-attention #############
class WindowAttention(nn.Module):
    def __init__(self, dim, win_size, num_heads, token_projection='linear', qkv_bias=True, qk_scale=None, attn_drop=0.,
                 proj_drop=0., se_layer=False):

        super().__init__()
        self.dim = dim
        self.win_size = win_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * win_size[0] - 1) * (2 * win_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.win_size[0])  # [0,...,Wh-1]
        coords_w = torch.arange(self.win_size[1])  # [0,...,Ww-1]
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.win_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.win_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.win_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        # self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        if token_projection == 'conv':
            self.qkv = ConvProjection(dim, num_heads, dim // num_heads, bias=qkv_bias)
        elif token_projection == 'linear_concat':
            self.qkv = LinearProjection_Concat_kv(dim, num_heads, dim // num_heads, bias=qkv_bias)
        else:
            self.qkv = LinearProjection(dim, num_heads, dim // num_heads, bias=qkv_bias)

        self.token_projection = token_projection
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.se_layer = SELayer(dim) if se_layer else nn.Identity()
        self.proj_drop = nn.Dropout(proj_drop)

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

    def forward(self, x, attn_kv=None, mask=None):
        B_, N, C = x.shape
        q, k, v = self.qkv(x, attn_kv)
        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.win_size[0] * self.win_size[1], self.win_size[0] * self.win_size[1], -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        ratio = attn.size(-1) // relative_position_bias.size(-1)
        relative_position_bias = repeat(relative_position_bias, 'nH l c -> nH l (c d)', d=ratio)

        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            mask = repeat(mask, 'nW m n -> nW m (n d)', d=ratio)
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N * ratio) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N * ratio)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

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

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

    def flops(self, H, W):
        # calculate flops for 1 window with token length of N
        # print(N, self.dim)
        flops = 0
        N = self.win_size[0] * self.win_size[1]
        nW = H * W / N
        # qkv = self.qkv(x)
        # flops += N * self.dim * 3 * self.dim
        flops += self.qkv.flops(H, W)
        # attn = (q @ k.transpose(-2, -1))
        if self.token_projection != 'linear_concat':
            flops += nW * self.num_heads * N * (self.dim // self.num_heads) * N
            #  x = (attn @ v)
            flops += nW * self.num_heads * N * N * (self.dim // self.num_heads)
        else:
            flops += nW * self.num_heads * N * (self.dim // self.num_heads) * N * 2
            #  x = (attn @ v)
            flops += nW * self.num_heads * N * N * 2 * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += nW * N * self.dim * self.dim
        print("W-MSA:{%.2f}" % (flops / 1e9))
        return flops


#########################################
########### feed-forward network #############
class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)
        self.in_features = in_features
        self.hidden_features = hidden_features
        self.out_features = out_features

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

    def flops(self, H, W):
        flops = 0
        # fc1
        flops += H * W * self.in_features * self.hidden_features
        # fc2
        flops += H * W * self.hidden_features * self.out_features
        print("MLP:{%.2f}" % (flops / 1e9))
        return flops


class MSFN(nn.Module):
    def __init__(self, dim=32, hidden_dim=128, bias=False,act_layer=nn.GELU, drop=0.):
        super().__init__()

        hidden_features = hidden_dim

        self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias)

        self.dwconv3x3 = nn.Conv2d(hidden_features * 2, hidden_features * 2, kernel_size=3, stride=1, padding=1,
                                   groups=hidden_features * 2, bias=bias)
        self.dwconv5x5 = nn.Conv2d(hidden_features * 2, hidden_features * 2, kernel_size=5, stride=1, padding=2,
                                   groups=hidden_features * 2, bias=bias)
        self.relu3 = nn.ReLU()
        self.relu5 = nn.ReLU()

        self.dwconv3x3_1 = nn.Conv2d(hidden_features * 2, hidden_features, kernel_size=3, stride=1, padding=1,
                                     groups=hidden_features, bias=bias)
        self.dwconv5x5_1 = nn.Conv2d(hidden_features * 2, hidden_features, kernel_size=5, stride=1, padding=2,
                                     groups=hidden_features, bias=bias)

        self.relu3_1 = nn.ReLU()
        self.relu5_1 = nn.ReLU()

        self.project_out = nn.Conv2d(hidden_features * 2, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        bs, hw, c = x.size()
        hh = int(math.sqrt(hw))
        x = rearrange(x, ' b (h w) (c) -> b c h w ', h=hh, w=hh)
        x = self.project_in(x)
        x1_3, x2_3 = self.relu3(self.dwconv3x3(x)).chunk(2, dim=1)
        x1_5, x2_5 = self.relu5(self.dwconv5x5(x)).chunk(2, dim=1)

        x1 = torch.cat([x1_3, x1_5], dim=1)
        x2 = torch.cat([x2_3, x2_5], dim=1)

        x1 = self.relu3_1(self.dwconv3x3_1(x1))
        x2 = self.relu5_1(self.dwconv5x5_1(x2))

        x = torch.cat([x1, x2], dim=1)

        x = self.project_out(x)
        x = rearrange(x, ' b c h w -> b (h w) c', h=hh, w=hh)

        return x
    def flops(self, H, W):
        flops = 0
        # fc1
        flops += H * W * self.dim * self.hidden_dim
        # dwconv
        flops += H * W * self.hidden_dim * 3 * 3
        # fc2
        flops += H * W * self.hidden_dim * self.dim
        print("LeFF:{%.2f}" % (flops / 1e9))
        return flops


#########################################
########### window operation#############
def window_partition(x, win_size, dilation_rate=1):
    B, H, W, C = x.shape
    if dilation_rate != 1:
        x = x.permute(0, 3, 1, 2)  # B, C, H, W
        assert type(dilation_rate) is int, 'dilation_rate should be a int'
        x = F.unfold(x, kernel_size=win_size, dilation=dilation_rate, padding=4 * (dilation_rate - 1),
                     stride=win_size)  # B, C*Wh*Ww, H/Wh*W/Ww
        windows = x.permute(0, 2, 1).contiguous().view(-1, C, win_size, win_size)  # B' ,C ,Wh ,Ww
        windows = windows.permute(0, 2, 3, 1).contiguous()  # B' ,Wh ,Ww ,C
    else:
        x = x.view(B, H // win_size, win_size, W // win_size, win_size, C)
        windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, win_size, win_size, C)  # B' ,Wh ,Ww ,C
    return windows


def window_reverse(windows, win_size, H, W, dilation_rate=1):
    # B' ,Wh ,Ww ,C
    B = int(windows.shape[0] / (H * W / win_size / win_size))
    x = windows.view(B, H // win_size, W // win_size, win_size, win_size, -1)
    if dilation_rate != 1:
        x = windows.permute(0, 5, 3, 4, 1, 2).contiguous()  # B, C*Wh*Ww, H/Wh*W/Ww
        x = F.fold(x, (H, W), kernel_size=win_size, dilation=dilation_rate, padding=4 * (dilation_rate - 1),
                   stride=win_size)
    else:
        x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x

#########################################
class Attention(nn.Module):
    def __init__(self, dim, num_heads, bias=False):
        super().__init__()
        self.num_heads = num_heads

        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))

        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
        self.qkv_dwconv = nn.Conv2d(dim * 3, dim * 3, kernel_size=3, stride=1, padding=1, groups=dim * 3, bias=bias)
        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
        self.attn_drop = nn.Dropout(0.)

        self.attn1 = torch.nn.Parameter(torch.tensor([0.2]), requires_grad=True)
        self.attn2 = torch.nn.Parameter(torch.tensor([0.2]), requires_grad=True)
        self.attn3 = torch.nn.Parameter(torch.tensor([0.2]), requires_grad=True)
        self.attn4 = torch.nn.Parameter(torch.tensor([0.2]), requires_grad=True)

    def forward(self, x):
        bs, hw, c = x.size()
        hh = int(math.sqrt(hw))
        x = rearrange(x, ' b (h w) (c) -> b c h w ', h=hh, w=hh)
        b, c, h, w = x.shape

        qkv = self.qkv_dwconv(self.qkv(x))
        q, k, v = qkv.chunk(3, dim=1)

        q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
        k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
        v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=self.num_heads)

        q = torch.nn.functional.normalize(q, dim=-1)
        k = torch.nn.functional.normalize(k, dim=-1)

        _, _, C, _ = q.shape

        mask1 = torch.zeros(b, self.num_heads, C, C, device=x.device, requires_grad=False)
        mask2 = torch.zeros(b, self.num_heads, C, C, device=x.device, requires_grad=False)
        mask3 = torch.zeros(b, self.num_heads, C, C, device=x.device, requires_grad=False)
        mask4 = torch.zeros(b, self.num_heads, C, C, device=x.device, requires_grad=False)

        attn = (q @ k.transpose(-2, -1)) * self.temperature

        index = torch.topk(attn, k=int(C/2), dim=-1, largest=True)[1]
        mask1.scatter_(-1, index, 1.)
        attn1 = torch.where(mask1 > 0, attn, torch.full_like(attn, float('-inf')))

        index = torch.topk(attn, k=int(C*2/3), dim=-1, largest=True)[1]
        mask2.scatter_(-1, index, 1.)
        attn2 = torch.where(mask2 > 0, attn, torch.full_like(attn, float('-inf')))

        index = torch.topk(attn, k=int(C*3/4), dim=-1, largest=True)[1]
        mask3.scatter_(-1, index, 1.)
        attn3 = torch.where(mask3 > 0, attn, torch.full_like(attn, float('-inf')))

        index = torch.topk(attn, k=int(C*4/5), dim=-1, largest=True)[1]
        mask4.scatter_(-1, index, 1.)
        attn4 = torch.where(mask4 > 0, attn, torch.full_like(attn, float('-inf')))

        attn1 = attn1.softmax(dim=-1)
        attn2 = attn2.softmax(dim=-1)
        attn3 = attn3.softmax(dim=-1)
        attn4 = attn4.softmax(dim=-1)

        out1 = (attn1 @ v)
        out2 = (attn2 @ v)
        out3 = (attn3 @ v)
        out4 = (attn4 @ v)

        out = out1 * self.attn1 + out2 * self.attn2 + out3 * self.attn3 + out4 * self.attn4

        out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=self.num_heads, h=h, w=w)

        out = self.project_out(out)
        out = rearrange(out, ' b c h w -> b (h w) c', h=hh, w=hh)
        return out
#########################################
# Downsample Block
class Downsample(nn.Module):
    def __init__(self, in_channel, out_channel):
        super(Downsample, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channel, out_channel, kernel_size=4, stride=2, padding=1),
        )
        self.in_channel = in_channel
        self.out_channel = out_channel

    def forward(self, x):
        B, L, C = x.shape
        # import pdb;pdb.set_trace()
        H = int(math.sqrt(L))
        W = int(math.sqrt(L))
        x = x.transpose(1, 2).contiguous().view(B, C, H, W)
        out = self.conv(x).flatten(2).transpose(1, 2).contiguous()  # B H*W C
        return out

    def flops(self, H, W):
        flops = 0
        # conv
        flops += H / 2 * W / 2 * self.in_channel * self.out_channel * 4 * 4
        print("Downsample:{%.2f}" % (flops / 1e9))
        return flops


# Upsample Block
class Upsample(nn.Module):
    def __init__(self, in_channel, out_channel):
        super(Upsample, self).__init__()
        self.deconv = nn.Sequential(
            nn.ConvTranspose2d(in_channel, out_channel, kernel_size=2, stride=2),
        )
        self.in_channel = in_channel
        self.out_channel = out_channel

    def forward(self, x):
        B, L, C = x.shape
        H = int(math.sqrt(L))
        W = int(math.sqrt(L))
        x = x.transpose(1, 2).contiguous().view(B, C, H, W)
        out = self.deconv(x).flatten(2).transpose(1, 2).contiguous()  # B H*W C
        return out

    def flops(self, H, W):
        flops = 0
        # conv
        flops += H * 2 * W * 2 * self.in_channel * self.out_channel * 2 * 2
        print("Upsample:{%.2f}" % (flops / 1e9))
        return flops


# Input Projection
class InputProj(nn.Module):
    def __init__(self, in_channel=3, out_channel=64, kernel_size=3, stride=1, norm_layer=None, act_layer=nn.LeakyReLU):
        super().__init__()
        self.proj = nn.Sequential(
            nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=stride, padding=kernel_size // 2),
            act_layer(inplace=True)
        )
        if norm_layer is not None:
            self.norm = norm_layer(out_channel)
        else:
            self.norm = None
        self.in_channel = in_channel
        self.out_channel = out_channel

    def forward(self, x):
        B, C, H, W = x.shape
        x = self.proj(x).flatten(2).transpose(1, 2).contiguous()  # B H*W C
        if self.norm is not None:
            x = self.norm(x)
        return x

    def flops(self, H, W):
        flops = 0
        # conv
        flops += H * W * self.in_channel * self.out_channel * 3 * 3

        if self.norm is not None:
            flops += H * W * self.out_channel
        print("Input_proj:{%.2f}" % (flops / 1e9))
        return flops


# Output Projection
class OutputProj(nn.Module):
    def __init__(self, in_channel=64, out_channel=3, kernel_size=3, stride=1, norm_layer=None, act_layer=None):
        super().__init__()
        self.proj = nn.Sequential(
            nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=stride, padding=kernel_size // 2),
        )
        if act_layer is not None:
            self.proj.add_module(act_layer(inplace=True))
        if norm_layer is not None:
            self.norm = norm_layer(out_channel)
        else:
            self.norm = None
        self.in_channel = in_channel
        self.out_channel = out_channel

    def forward(self, x):
        B, L, C = x.shape
        H = int(math.sqrt(L))
        W = int(math.sqrt(L))
        x = x.transpose(1, 2).view(B, C, H, W)
        x = self.proj(x)
        if self.norm is not None:
            x = self.norm(x)
        return x

    def flops(self, H, W):
        flops = 0
        # conv
        flops += H * W * self.in_channel * self.out_channel * 3 * 3

        if self.norm is not None:
            flops += H * W * self.out_channel
        print("Output_proj:{%.2f}" % (flops / 1e9))
        return flops


#########################################
########### LeWinTransformer #############
class STBlock(nn.Module):
    def __init__(self, dim, input_resolution, num_heads, win_size=8, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, token_projection='linear', token_mlp='leff',
                 se_layer=False):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution

        self.num_heads = num_heads
        self.Attention = Attention(dim=dim, num_heads=self.num_heads)
        self.win_size = win_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        self.token_mlp = token_mlp
        if min(self.input_resolution) <= self.win_size:
            self.shift_size = 0
            self.win_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.win_size, "shift_size must in 0-win_size"

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

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer,
                       drop=drop) if token_mlp == 'ffn' else MSFN(dim, mlp_hidden_dim, act_layer=act_layer, drop=drop)

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

    def forward(self, x):
        B, L, C = x.shape

        shortcut = x
        x = self.norm1(x)
        x =self.Attention(x)
        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x

    def flops(self):
        flops = 0
        H, W = self.input_resolution
        # norm1
        flops += self.dim * H * W
        # W-MSA/SW-MSA
        flops += self.attn.flops(H, W)
        # norm2
        flops += self.dim * H * W
        # mlp
        flops += self.mlp.flops(H, W)
        print("LeWin:{%.2f}" % (flops / 1e9))
        return flops


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

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint
        # build blocks
        self.blocks = nn.ModuleList([
            STBlock(dim=dim, input_resolution=input_resolution,
                                  num_heads=num_heads, win_size=win_size,
                                  shift_size=0 if (i % 2 == 0) else win_size // 2,
                                  mlp_ratio=mlp_ratio,
                                  qkv_bias=qkv_bias, qk_scale=qk_scale,
                                  drop=drop, attn_drop=attn_drop,
                                  drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                  norm_layer=norm_layer, token_projection=token_projection, token_mlp=token_mlp,
                                  se_layer=se_layer)
            for i in range(depth)])

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

    def forward(self, x, mask=None):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        return x

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






@ARCH_REGISTRY.register()
class Uformer(nn.Module):
    def __init__(self, img_size=128, img_ch=3, output_ch=3,
                 embed_dim=32, depths=[4, 6, 6, 8, 8, 8, 6, 6, 4], num_heads=[1, 2, 4, 8, 16, 16, 8, 4, 2],
                 win_size=8, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, patch_norm=True,
                 use_checkpoint=False, token_projection='linear', token_mlp='ffn', se_layer=False,
                 dowsample=Downsample, upsample=Upsample, **kwargs):
        super().__init__()
        in_chans = img_ch
        out_chans = output_ch
        self.num_enc_layers = len(depths) // 2
        self.num_dec_layers = len(depths) // 2
        self.embed_dim = embed_dim
        self.patch_norm = patch_norm
        self.mlp_ratio = mlp_ratio
        self.token_projection = token_projection
        self.mlp = token_mlp
        self.win_size = win_size
        self.reso = img_size
        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        enc_dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths[:self.num_enc_layers]))]
        conv_dpr = [drop_path_rate] * depths[4]
        dec_dpr = enc_dpr[::-1]

        # build layers

        # Input/Output
        self.input_proj = InputProj(in_channel=in_chans, out_channel=embed_dim, kernel_size=3, stride=1,
                                    act_layer=nn.LeakyReLU)
        self.output_proj = OutputProj(in_channel=2 * embed_dim, out_channel=out_chans, kernel_size=3, stride=1)

        # Encoder
        self.encoderlayer_0 = BasicUformerLayer(dim=embed_dim,
                                                output_dim=embed_dim,
                                                input_resolution=(img_size,
                                                                  img_size),
                                                depth=depths[0],
                                                num_heads=num_heads[0],
                                                win_size=win_size,
                                                mlp_ratio=self.mlp_ratio,
                                                qkv_bias=qkv_bias, qk_scale=qk_scale,
                                                drop=drop_rate, attn_drop=attn_drop_rate,
                                                drop_path=enc_dpr[sum(depths[:0]):sum(depths[:1])],
                                                norm_layer=norm_layer,
                                                use_checkpoint=use_checkpoint,
                                                token_projection=token_projection, token_mlp=token_mlp,
                                                se_layer=se_layer)
        self.dowsample_0 = dowsample(embed_dim, embed_dim * 2)
        self.encoderlayer_1 = BasicUformerLayer(dim=embed_dim * 2,
                                                output_dim=embed_dim * 2,
                                                input_resolution=(img_size // 2,
                                                                  img_size // 2),
                                                depth=depths[1],
                                                num_heads=num_heads[1],
                                                win_size=win_size,
                                                mlp_ratio=self.mlp_ratio,
                                                qkv_bias=qkv_bias, qk_scale=qk_scale,
                                                drop=drop_rate, attn_drop=attn_drop_rate,
                                                drop_path=enc_dpr[sum(depths[:1]):sum(depths[:2])],
                                                norm_layer=norm_layer,
                                                use_checkpoint=use_checkpoint,
                                                token_projection=token_projection, token_mlp=token_mlp,
                                                se_layer=se_layer)
        self.dowsample_1 = dowsample(embed_dim * 2, embed_dim * 4)
        self.encoderlayer_2 = BasicUformerLayer(dim=embed_dim * 4,
                                                output_dim=embed_dim * 4,
                                                input_resolution=(img_size // (2 ** 2),
                                                                  img_size // (2 ** 2)),
                                                depth=depths[2],
                                                num_heads=num_heads[2],
                                                win_size=win_size,
                                                mlp_ratio=self.mlp_ratio,
                                                qkv_bias=qkv_bias, qk_scale=qk_scale,
                                                drop=drop_rate, attn_drop=attn_drop_rate,
                                                drop_path=enc_dpr[sum(depths[:2]):sum(depths[:3])],
                                                norm_layer=norm_layer,
                                                use_checkpoint=use_checkpoint,
                                                token_projection=token_projection, token_mlp=token_mlp,
                                                se_layer=se_layer)
        self.dowsample_2 = dowsample(embed_dim * 4, embed_dim * 8)
        self.encoderlayer_3 = BasicUformerLayer(dim=embed_dim * 8,
                                                output_dim=embed_dim * 8,
                                                input_resolution=(img_size // (2 ** 3),
                                                                  img_size // (2 ** 3)),
                                                depth=depths[3],
                                                num_heads=num_heads[3],
                                                win_size=win_size,
                                                mlp_ratio=self.mlp_ratio,
                                                qkv_bias=qkv_bias, qk_scale=qk_scale,
                                                drop=drop_rate, attn_drop=attn_drop_rate,
                                                drop_path=enc_dpr[sum(depths[:3]):sum(depths[:4])],
                                                norm_layer=norm_layer,
                                                use_checkpoint=use_checkpoint,
                                                token_projection=token_projection, token_mlp=token_mlp,
                                                se_layer=se_layer)
        self.dowsample_3 = dowsample(embed_dim * 8, embed_dim * 16)

        # Bottleneck
        self.conv = BasicUformerLayer(dim=embed_dim * 16,
                                      output_dim=embed_dim * 16,
                                      input_resolution=(img_size // (2 ** 4),
                                                        img_size // (2 ** 4)),
                                      depth=depths[4],
                                      num_heads=num_heads[4],
                                      win_size=win_size,
                                      mlp_ratio=self.mlp_ratio,
                                      qkv_bias=qkv_bias, qk_scale=qk_scale,
                                      drop=drop_rate, attn_drop=attn_drop_rate,
                                      drop_path=conv_dpr,
                                      norm_layer=norm_layer,
                                      use_checkpoint=use_checkpoint,
                                      token_projection=token_projection, token_mlp=token_mlp, se_layer=se_layer)

        # Decoder
        self.upsample_0 = upsample(embed_dim * 16, embed_dim * 8)
        self.decoderlayer_0 = BasicUformerLayer(dim=embed_dim * 16,
                                                output_dim=embed_dim * 16,
                                                input_resolution=(img_size // (2 ** 3),
                                                                  img_size // (2 ** 3)),
                                                depth=depths[5],
                                                num_heads=num_heads[5],
                                                win_size=win_size,
                                                mlp_ratio=self.mlp_ratio,
                                                qkv_bias=qkv_bias, qk_scale=qk_scale,
                                                drop=drop_rate, attn_drop=attn_drop_rate,
                                                drop_path=dec_dpr[:depths[5]],
                                                norm_layer=norm_layer,
                                                use_checkpoint=use_checkpoint,
                                                token_projection=token_projection, token_mlp=token_mlp,
                                                se_layer=se_layer)
        self.upsample_1 = upsample(embed_dim * 16, embed_dim * 4)
        self.decoderlayer_1 = BasicUformerLayer(dim=embed_dim * 8,
                                                output_dim=embed_dim * 8,
                                                input_resolution=(img_size // (2 ** 2),
                                                                  img_size // (2 ** 2)),
                                                depth=depths[6],
                                                num_heads=num_heads[6],
                                                win_size=win_size,
                                                mlp_ratio=self.mlp_ratio,
                                                qkv_bias=qkv_bias, qk_scale=qk_scale,
                                                drop=drop_rate, attn_drop=attn_drop_rate,
                                                drop_path=dec_dpr[sum(depths[5:6]):sum(depths[5:7])],
                                                norm_layer=norm_layer,
                                                use_checkpoint=use_checkpoint,
                                                token_projection=token_projection, token_mlp=token_mlp,
                                                se_layer=se_layer)
        self.upsample_2 = upsample(embed_dim * 8, embed_dim * 2)
        self.decoderlayer_2 = BasicUformerLayer(dim=embed_dim * 4,
                                                output_dim=embed_dim * 4,
                                                input_resolution=(img_size // 2,
                                                                  img_size // 2),
                                                depth=depths[7],
                                                num_heads=num_heads[7],
                                                win_size=win_size,
                                                mlp_ratio=self.mlp_ratio,
                                                qkv_bias=qkv_bias, qk_scale=qk_scale,
                                                drop=drop_rate, attn_drop=attn_drop_rate,
                                                drop_path=dec_dpr[sum(depths[5:7]):sum(depths[5:8])],
                                                norm_layer=norm_layer,
                                                use_checkpoint=use_checkpoint,
                                                token_projection=token_projection, token_mlp=token_mlp,
                                                se_layer=se_layer)
        self.upsample_3 = upsample(embed_dim * 4, embed_dim)
        self.decoderlayer_3 = BasicUformerLayer(dim=embed_dim * 2,
                                                output_dim=embed_dim * 2,
                                                input_resolution=(img_size,
                                                                  img_size),
                                                depth=depths[8],
                                                num_heads=num_heads[8],
                                                win_size=win_size,
                                                mlp_ratio=self.mlp_ratio,
                                                qkv_bias=qkv_bias, qk_scale=qk_scale,
                                                drop=drop_rate, attn_drop=attn_drop_rate,
                                                drop_path=dec_dpr[sum(depths[5:8]):sum(depths[5:9])],
                                                norm_layer=norm_layer,
                                                use_checkpoint=use_checkpoint,
                                                token_projection=token_projection, token_mlp=token_mlp,
                                                se_layer=se_layer)
        self.activation = nn.Sequential(nn.Sigmoid())
        self.apply(self._init_weights)

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

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

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

    def extra_repr(self) -> str:
        return f"embed_dim={self.embed_dim}, token_projection={self.token_projection}, token_mlp={self.mlp},win_size={self.win_size}"

    def forward(self, x):
        # Input Projection
        y = self.input_proj(x)
        y = self.pos_drop(y)
        # Encoder
        conv0 = self.encoderlayer_0(y)
        pool0 = self.dowsample_0(conv0)
        conv1 = self.encoderlayer_1(pool0)
        pool1 = self.dowsample_1(conv1)
        conv2 = self.encoderlayer_2(pool1)
        pool2 = self.dowsample_2(conv2)
        conv3 = self.encoderlayer_3(pool2)
        pool3 = self.dowsample_3(conv3)

        # Bottleneck
        conv4 = self.conv(pool3)

        # Decoder
        up0 = self.upsample_0(conv4)
        deconv0 = torch.cat([up0, conv3], -1)
        deconv0 = self.decoderlayer_0(deconv0)

        up1 = self.upsample_1(deconv0)
        deconv1 = torch.cat([up1, conv2], -1)
        deconv1 = self.decoderlayer_1(deconv1)

        up2 = self.upsample_2(deconv1)
        deconv2 = torch.cat([up2, conv1], -1)
        deconv2 = self.decoderlayer_2(deconv2)

        up3 = self.upsample_3(deconv2)
        deconv3 = torch.cat([up3, conv0], -1)
        deconv3 = self.decoderlayer_3(deconv3)

        # Output Projection
        y = self.output_proj(deconv3)
        y = self.activation(y)
        return y

    def flops(self):
        flops = 0
        # Input Projection
        flops += self.input_proj.flops(self.reso, self.reso)
        # Encoder
        flops += self.encoderlayer_0.flops() + self.dowsample_0.flops(self.reso, self.reso)
        flops += self.encoderlayer_1.flops() + self.dowsample_1.flops(self.reso // 2, self.reso // 2)
        flops += self.encoderlayer_2.flops() + self.dowsample_2.flops(self.reso // 2 ** 2, self.reso // 2 ** 2)
        flops += self.encoderlayer_3.flops() + self.dowsample_3.flops(self.reso // 2 ** 3, self.reso // 2 ** 3)

        # Bottleneck
        flops += self.conv.flops()

        # Decoder
        flops += self.upsample_0.flops(self.reso // 2 ** 4, self.reso // 2 ** 4) + self.decoderlayer_0.flops()
        flops += self.upsample_1.flops(self.reso // 2 ** 3, self.reso // 2 ** 3) + self.decoderlayer_1.flops()
        flops += self.upsample_2.flops(self.reso // 2 ** 2, self.reso // 2 ** 2) + self.decoderlayer_2.flops()
        flops += self.upsample_3.flops(self.reso // 2, self.reso // 2) + self.decoderlayer_3.flops()

        # Output Projection
        flops += self.output_proj.flops(self.reso, self.reso)
        return flops









if __name__ == "__main__":
    arch = Uformer
    input_size = 256
    # arch = Uformer_Cross
    depths = [2, 2, 2, 2, 2, 2, 2, 2, 2]
    # model_restoration = UNet(dim=32)
    model_restoration = arch(img_size=input_size, embed_dim=44, depths=depths,
                             win_size=8, mlp_ratio=4., qkv_bias=True,
                             token_projection='linear', token_mlp='leff',
                             downsample=Downsample, upsample=Upsample, se_layer=False)
    # arch = LeWinformer
    # depth = 20
    # model_restoration = arch(embed_dim=16,depth=depth,
    #              win_size=8, mlp_ratio=4., qkv_bias=True,
    #              token_projection='linear', token_mlp='leff',se_layer=False)
    # from ptflops import get_model_complexity_info
    # macs, params = get_model_complexity_info(model_restoration, (3, input_size, input_size), as_strings=True,
    #                                             print_per_layer_stat=True, verbose=True)
    # print('{:<30}  {:<8}'.format('Computational complexity: ', macs))
    # print('{:<30}  {:<8}'.format('Number of parameters: ', params))
    # print("number of GFLOPs: %.2f G"%(model_restoration.flops(input_size,input_size) / 1e9))
    print("number of GFLOPs: %.2f G" % (model_restoration.flops() / 1e9))