architecture/dat.py

'''
    DAT network from https://github.com/zhengchen1999/DAT (https://openaccess.thecvf.com/content/ICCV2023/papers/Chen_Dual_Aggregation_Transformer_for_Image_Super-Resolution_ICCV_2023_paper.pdf)
'''

import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from torch import Tensor
from torch.nn import functional as F

from timm.models.layers import DropPath, trunc_normal_
from einops.layers.torch import Rearrange
from einops import rearrange

import math
import numpy as np



def img2windows(img, H_sp, W_sp):
    """
    Input: Image (B, C, H, W)
    Output: Window Partition (B', N, C)
    """
    B, C, H, W = img.shape
    img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp)
    img_perm = img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp* W_sp, C)
    return img_perm


def windows2img(img_splits_hw, H_sp, W_sp, H, W):
    """
    Input: Window Partition (B', N, C)
    Output: Image (B, H, W, C)
    """
    B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp))

    img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1)
    img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return img


class SpatialGate(nn.Module):
    """ Spatial-Gate.
    Args:
        dim (int): Half of input channels.
    """
    def __init__(self, dim):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.conv = nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim) # DW Conv

    def forward(self, x, H, W):
        # Split
        x1, x2 = x.chunk(2, dim = -1)
        B, N, C = x.shape
        x2 = self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C//2, H, W)).flatten(2).transpose(-1, -2).contiguous()

        return x1 * x2


class SGFN(nn.Module):
    """ Spatial-Gate Feed-Forward Network.
    Args:
        in_features (int): Number of input channels.
        hidden_features (int | None): Number of hidden channels. Default: None
        out_features (int | None): Number of output channels. Default: None
        act_layer (nn.Module): Activation layer. Default: nn.GELU
        drop (float): Dropout rate. Default: 0.0
    """
    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.sg = SpatialGate(hidden_features//2)
        self.fc2 = nn.Linear(hidden_features//2, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x, H, W):
        """
        Input: x: (B, H*W, C), H, W
        Output: x: (B, H*W, C)
        """
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)

        x = self.sg(x, H, W)
        x = self.drop(x)

        x = self.fc2(x)
        x = self.drop(x)
        return x


class DynamicPosBias(nn.Module):
    # The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py
    """ Dynamic Relative Position Bias.
    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads.
        residual (bool):  If True, use residual strage to connect conv.
    """
    def __init__(self, dim, num_heads, residual):
        super().__init__()
        self.residual = residual
        self.num_heads = num_heads
        self.pos_dim = dim // 4
        self.pos_proj = nn.Linear(2, self.pos_dim)
        self.pos1 = nn.Sequential(
            nn.LayerNorm(self.pos_dim),
            nn.ReLU(inplace=True),
            nn.Linear(self.pos_dim, self.pos_dim),
        )
        self.pos2 = nn.Sequential(
            nn.LayerNorm(self.pos_dim),
            nn.ReLU(inplace=True),
            nn.Linear(self.pos_dim, self.pos_dim)
        )
        self.pos3 = nn.Sequential(
            nn.LayerNorm(self.pos_dim),
            nn.ReLU(inplace=True),
            nn.Linear(self.pos_dim, self.num_heads)
        )
    def forward(self, biases):
        if self.residual:
            pos = self.pos_proj(biases) # 2Gh-1 * 2Gw-1, heads
            pos = pos + self.pos1(pos)
            pos = pos + self.pos2(pos)
            pos = self.pos3(pos)
        else:
            pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases))))
        return pos


class Spatial_Attention(nn.Module):
    """ Spatial Window Self-Attention.
    It supports rectangle window (containing square window).
    Args:
        dim (int): Number of input channels.
        idx (int): The indentix of window. (0/1)
        split_size (tuple(int)): Height and Width of spatial window.
        dim_out (int | None): The dimension of the attention output. Default: None
        num_heads (int): Number of attention heads. Default: 6
        attn_drop (float): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float): Dropout ratio of output. Default: 0.0
        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set
        position_bias (bool): The dynamic relative position bias. Default: True
    """
    def __init__(self, dim, idx, split_size=[8,8], dim_out=None, num_heads=6, attn_drop=0., proj_drop=0., qk_scale=None, position_bias=True):
        super().__init__()
        self.dim = dim
        self.dim_out = dim_out or dim
        self.split_size = split_size
        self.num_heads = num_heads
        self.idx = idx
        self.position_bias = position_bias

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

        if idx == 0:
            H_sp, W_sp = self.split_size[0], self.split_size[1]
        elif idx == 1:
            W_sp, H_sp = self.split_size[0], self.split_size[1]
        else:
            print ("ERROR MODE", idx)
            exit(0)
        self.H_sp = H_sp
        self.W_sp = W_sp

        if self.position_bias:
            self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False)
            # generate mother-set
            position_bias_h = torch.arange(1 - self.H_sp, self.H_sp)
            position_bias_w = torch.arange(1 - self.W_sp, self.W_sp)
            biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w]))
            biases = biases.flatten(1).transpose(0, 1).contiguous().float()
            self.register_buffer('rpe_biases', biases)

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

        self.attn_drop = nn.Dropout(attn_drop)

    def im2win(self, x, H, W):
        B, N, C = x.shape
        x = x.transpose(-2,-1).contiguous().view(B, C, H, W)
        x = img2windows(x, self.H_sp, self.W_sp)
        x = x.reshape(-1, self.H_sp* self.W_sp, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3).contiguous()
        return x

    def forward(self, qkv, H, W, mask=None):
        """
        Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size
        Output: x (B, H, W, C)
        """
        q,k,v = qkv[0], qkv[1], qkv[2]

        B, L, C = q.shape
        assert L == H * W, "flatten img_tokens has wrong size"

        # partition the q,k,v, image to window
        q = self.im2win(q, H, W)
        k = self.im2win(k, H, W)
        v = self.im2win(v, H, W)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))  # B head N C @ B head C N --> B head N N

        # calculate drpe
        if self.position_bias:
            pos = self.pos(self.rpe_biases)
            # select position bias
            relative_position_bias = pos[self.relative_position_index.view(-1)].view(
                self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1)
            relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
            attn = attn + relative_position_bias.unsqueeze(0)

        N = attn.shape[3]

        # use mask for shift window
        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)

        attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype)
        attn = self.attn_drop(attn)

        x = (attn @ v)
        x = x.transpose(1, 2).reshape(-1, self.H_sp* self.W_sp, C)  # B head N N @ B head N C

        # merge the window, window to image
        x = windows2img(x, self.H_sp, self.W_sp, H, W)  # B H' W' C

        return x


class Adaptive_Spatial_Attention(nn.Module):
    # The implementation builds on CAT code https://github.com/Zhengchen1999/CAT
    """ Adaptive Spatial Self-Attention
    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads. Default: 6
        split_size (tuple(int)): Height and Width of spatial window.
        shift_size (tuple(int)): Shift size for spatial window.
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set.
        drop (float): Dropout rate. Default: 0.0
        attn_drop (float): Attention dropout rate. Default: 0.0
        rg_idx (int): The indentix of Residual Group (RG)
        b_idx (int): The indentix of Block in each RG
    """
    def __init__(self, dim, num_heads, 
                 reso=64, split_size=[8,8], shift_size=[1,2], qkv_bias=False, qk_scale=None,
                 drop=0., attn_drop=0., rg_idx=0, b_idx=0):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.split_size = split_size
        self.shift_size = shift_size
        self.b_idx  = b_idx
        self.rg_idx = rg_idx
        self.patches_resolution = reso
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)

        assert 0 <= self.shift_size[0] < self.split_size[0], "shift_size must in 0-split_size0"
        assert 0 <= self.shift_size[1] < self.split_size[1], "shift_size must in 0-split_size1"

        self.branch_num = 2

        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(drop)

        self.attns = nn.ModuleList([
                Spatial_Attention(
                    dim//2, idx = i,
                    split_size=split_size, num_heads=num_heads//2, dim_out=dim//2,
                    qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, position_bias=True)
                for i in range(self.branch_num)])

        if (self.rg_idx % 2 == 0 and self.b_idx  > 0 and (self.b_idx  - 2) % 4 == 0) or (self.rg_idx % 2 != 0 and self.b_idx  % 4 == 0):
            attn_mask = self.calculate_mask(self.patches_resolution, self.patches_resolution)
            self.register_buffer("attn_mask_0", attn_mask[0])
            self.register_buffer("attn_mask_1", attn_mask[1])
        else:
            attn_mask = None
            self.register_buffer("attn_mask_0", None)
            self.register_buffer("attn_mask_1", None)
        
        self.dwconv = nn.Sequential(
            nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1,groups=dim),
            nn.BatchNorm2d(dim),
            nn.GELU()
        )
        self.channel_interaction = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(dim, dim // 8, kernel_size=1),
            nn.BatchNorm2d(dim // 8),
            nn.GELU(),
            nn.Conv2d(dim // 8, dim, kernel_size=1),
        )
        self.spatial_interaction = nn.Sequential(
            nn.Conv2d(dim, dim // 16, kernel_size=1),
            nn.BatchNorm2d(dim // 16),
            nn.GELU(),
            nn.Conv2d(dim // 16, 1, kernel_size=1)
        )

    def calculate_mask(self, H, W):
        # The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
        # calculate attention mask for shift window
        img_mask_0 = torch.zeros((1, H, W, 1))  # 1 H W 1 idx=0
        img_mask_1 = torch.zeros((1, H, W, 1))  # 1 H W 1 idx=1
        h_slices_0 = (slice(0, -self.split_size[0]),
                    slice(-self.split_size[0], -self.shift_size[0]),
                    slice(-self.shift_size[0], None))
        w_slices_0 = (slice(0, -self.split_size[1]),
                    slice(-self.split_size[1], -self.shift_size[1]),
                    slice(-self.shift_size[1], None))

        h_slices_1 = (slice(0, -self.split_size[1]),
                    slice(-self.split_size[1], -self.shift_size[1]),
                    slice(-self.shift_size[1], None))
        w_slices_1 = (slice(0, -self.split_size[0]),
                    slice(-self.split_size[0], -self.shift_size[0]),
                    slice(-self.shift_size[0], None))
        cnt = 0
        for h in h_slices_0:
            for w in w_slices_0:
                img_mask_0[:, h, w, :] = cnt
                cnt += 1
        cnt = 0
        for h in h_slices_1:
            for w in w_slices_1:
                img_mask_1[:, h, w, :] = cnt
                cnt += 1

        # calculate mask for window-0
        img_mask_0 = img_mask_0.view(1, H // self.split_size[0], self.split_size[0], W // self.split_size[1], self.split_size[1], 1)
        img_mask_0 = img_mask_0.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, self.split_size[0], self.split_size[1], 1) # nW, sw[0], sw[1], 1
        mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1])
        attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2)
        attn_mask_0 = attn_mask_0.masked_fill(attn_mask_0 != 0, float(-100.0)).masked_fill(attn_mask_0 == 0, float(0.0))

        # calculate mask for window-1
        img_mask_1 = img_mask_1.view(1, H // self.split_size[1], self.split_size[1], W // self.split_size[0], self.split_size[0], 1)
        img_mask_1 = img_mask_1.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, self.split_size[1], self.split_size[0], 1) # nW, sw[1], sw[0], 1
        mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0])
        attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2)
        attn_mask_1 = attn_mask_1.masked_fill(attn_mask_1 != 0, float(-100.0)).masked_fill(attn_mask_1 == 0, float(0.0))

        return attn_mask_0, attn_mask_1

    def forward(self, x, H, W):
        """
        Input: x: (B, H*W, C), H, W
        Output: x: (B, H*W, C)
        """
        B, L, C = x.shape
        assert L == H * W, "flatten img_tokens has wrong size"

        qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3) # 3, B, HW, C
        # V without partition
        v = qkv[2].transpose(-2,-1).contiguous().view(B, C, H, W)

        # image padding
        max_split_size = max(self.split_size[0], self.split_size[1])
        pad_l = pad_t = 0
        pad_r = (max_split_size - W % max_split_size) % max_split_size
        pad_b = (max_split_size - H % max_split_size) % max_split_size

        qkv = qkv.reshape(3*B, H, W, C).permute(0, 3, 1, 2) # 3B C H W
        qkv = F.pad(qkv, (pad_l, pad_r, pad_t, pad_b)).reshape(3, B, C, -1).transpose(-2, -1) # l r t b
        _H = pad_b + H
        _W = pad_r + W
        _L = _H * _W

        # window-0 and window-1 on split channels [C/2, C/2]; for square windows (e.g., 8x8), window-0 and window-1 can be merged
        # shift in block: (0, 4, 8, ...), (2, 6, 10, ...), (0, 4, 8, ...), (2, 6, 10, ...), ...
        if (self.rg_idx % 2 == 0 and self.b_idx  > 0 and (self.b_idx  - 2) % 4 == 0) or (self.rg_idx % 2 != 0 and self.b_idx  % 4 == 0):
            qkv = qkv.view(3, B, _H, _W, C)
            qkv_0 = torch.roll(qkv[:,:,:,:,:C//2], shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(2, 3))
            qkv_0 = qkv_0.view(3, B, _L, C//2)
            qkv_1 = torch.roll(qkv[:,:,:,:,C//2:], shifts=(-self.shift_size[1], -self.shift_size[0]), dims=(2, 3))
            qkv_1 = qkv_1.view(3, B, _L, C//2)

            if self.patches_resolution != _H or self.patches_resolution != _W:
                mask_tmp = self.calculate_mask(_H, _W)
                x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device))
                x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device))
            else:
                x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0)
                x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1)

            x1 = torch.roll(x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2))
            x2 = torch.roll(x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2))
            x1 = x1[:, :H, :W, :].reshape(B, L, C//2)
            x2 = x2[:, :H, :W, :].reshape(B, L, C//2)
            # attention output
            attened_x = torch.cat([x1,x2], dim=2)

        else:
            x1 = self.attns[0](qkv[:,:,:,:C//2], _H, _W)[:, :H, :W, :].reshape(B, L, C//2)
            x2 = self.attns[1](qkv[:,:,:,C//2:], _H, _W)[:, :H, :W, :].reshape(B, L, C//2)
            # attention output
            attened_x = torch.cat([x1,x2], dim=2)
        
        # convolution output
        conv_x = self.dwconv(v)

        # Adaptive Interaction Module (AIM)
        # C-Map (before sigmoid)
        channel_map = self.channel_interaction(conv_x).permute(0, 2, 3, 1).contiguous().view(B, 1, C)
        # S-Map (before sigmoid)
        attention_reshape = attened_x.transpose(-2,-1).contiguous().view(B, C, H, W)
        spatial_map = self.spatial_interaction(attention_reshape)

        # C-I
        attened_x = attened_x * torch.sigmoid(channel_map)
        # S-I
        conv_x = torch.sigmoid(spatial_map) * conv_x
        conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, L, C)

        x = attened_x + conv_x

        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class Adaptive_Channel_Attention(nn.Module):
    # The implementation builds on XCiT code https://github.com/facebookresearch/xcit
    """ Adaptive Channel Self-Attention
    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads. Default: 6
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set.
        attn_drop (float): Attention dropout rate. Default: 0.0
        drop_path (float): Stochastic depth rate. Default: 0.0
    """
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))

        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)

        self.dwconv = nn.Sequential(
            nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1,groups=dim),
            nn.BatchNorm2d(dim),
            nn.GELU()
        )
        self.channel_interaction = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(dim, dim // 8, kernel_size=1),
            nn.BatchNorm2d(dim // 8),
            nn.GELU(),
            nn.Conv2d(dim // 8, dim, kernel_size=1),
        )
        self.spatial_interaction = nn.Sequential(
            nn.Conv2d(dim, dim // 16, kernel_size=1),
            nn.BatchNorm2d(dim // 16),
            nn.GELU(),
            nn.Conv2d(dim // 16, 1, kernel_size=1)
        )

    def forward(self, x, H, W):
        """
        Input: x: (B, H*W, C), H, W
        Output: x: (B, H*W, C)
        """
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
        qkv = qkv.permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]

        q = q.transpose(-2, -1)
        k = k.transpose(-2, -1)
        v = v.transpose(-2, -1)

        v_ = v.reshape(B, C, N).contiguous().view(B, C, H, W)

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

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

        # attention output
        attened_x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C)

        # convolution output
        conv_x = self.dwconv(v_)

        # Adaptive Interaction Module (AIM)
        # C-Map (before sigmoid)
        attention_reshape = attened_x.transpose(-2,-1).contiguous().view(B, C, H, W)
        channel_map = self.channel_interaction(attention_reshape)
        # S-Map (before sigmoid)
        spatial_map = self.spatial_interaction(conv_x).permute(0, 2, 3, 1).contiguous().view(B, N, 1)

        # S-I
        attened_x = attened_x * torch.sigmoid(spatial_map)
        # C-I
        conv_x = conv_x * torch.sigmoid(channel_map)
        conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, N, C)

        x = attened_x + conv_x

        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class DATB(nn.Module):
    def __init__(self, dim, num_heads, reso=64, split_size=[2,4],shift_size=[1,2], expansion_factor=4., qkv_bias=False, qk_scale=None, drop=0.,
                 attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, rg_idx=0, b_idx=0):
        super().__init__()

        self.norm1 = norm_layer(dim)

        if b_idx % 2 == 0:
            # DSTB
            self.attn = Adaptive_Spatial_Attention(
                dim, num_heads=num_heads, reso=reso, split_size=split_size, shift_size=shift_size, qkv_bias=qkv_bias, qk_scale=qk_scale,
                drop=drop, attn_drop=attn_drop, rg_idx=rg_idx, b_idx=b_idx
            )
        else:
            # DCTB
            self.attn = Adaptive_Channel_Attention(
                dim, 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()

        ffn_hidden_dim = int(dim * expansion_factor)
        self.ffn = SGFN(in_features=dim, hidden_features=ffn_hidden_dim, out_features=dim, act_layer=act_layer)
        self.norm2 = norm_layer(dim)

    def forward(self, x, x_size):
        """
        Input: x: (B, H*W, C), x_size: (H, W)
        Output: x: (B, H*W, C)
        """        
        H , W = x_size
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        x = x + self.drop_path(self.ffn(self.norm2(x), H, W))

        return x


class ResidualGroup(nn.Module):
    """ ResidualGroup
    Args:
        dim (int): Number of input channels.
        reso (int): Input resolution.
        num_heads (int): Number of attention heads.
        split_size (tuple(int)): Height and Width of spatial window.
        expansion_factor (float): Ratio of ffn hidden dim to embedding dim.
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop (float): Dropout rate. Default: 0
        attn_drop(float): Attention dropout rate. Default: 0
        drop_paths (float | None): Stochastic depth rate.
        act_layer (nn.Module): Activation layer. Default: nn.GELU
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm
        depth (int): Number of dual aggregation Transformer blocks in residual group.
        use_chk (bool): Whether to use checkpointing to save memory.
        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
    """
    def __init__(   self,
                    dim,
                    reso,
                    num_heads,
                    split_size=[2,4],
                    expansion_factor=4.,
                    qkv_bias=False,
                    qk_scale=None,
                    drop=0.,
                    attn_drop=0.,
                    drop_paths=None,
                    act_layer=nn.GELU,
                    norm_layer=nn.LayerNorm,
                    depth=2,
                    use_chk=False,
                    resi_connection='1conv',
                    rg_idx=0):
        super().__init__()
        self.use_chk = use_chk
        self.reso = reso

        self.blocks = nn.ModuleList([
        DATB(
            dim=dim,
            num_heads=num_heads,
            reso = reso,
            split_size = split_size,
            shift_size = [split_size[0]//2, split_size[1]//2],
            expansion_factor=expansion_factor,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            drop=drop,
            attn_drop=attn_drop,
            drop_path=drop_paths[i],
            act_layer=act_layer,
            norm_layer=norm_layer,
            rg_idx = rg_idx,
            b_idx = i,
            )for i in range(depth)])

        if resi_connection == '1conv':
            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
        elif resi_connection == '3conv':
            self.conv = nn.Sequential(
                nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
                nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True),
                nn.Conv2d(dim // 4, dim, 3, 1, 1))

    def forward(self, x, x_size):
        """
        Input: x: (B, H*W, C), x_size: (H, W)
        Output: x: (B, H*W, C)
        """
        H, W = x_size
        res = x
        for blk in self.blocks:
            if self.use_chk:
                x = checkpoint.checkpoint(blk, x, x_size)
            else:
                x = blk(x, x_size)
        x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W)
        x = self.conv(x)
        x = rearrange(x, "b c h w -> b (h w) c")
        x = res + x

        return x


class Upsample(nn.Sequential):
    """Upsample module.
    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.
    """
    def __init__(self, scale, num_feat):
        m = []
        if (scale & (scale - 1)) == 0:  # scale = 2^n
            for _ in range(int(math.log(scale, 2))):
                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
                m.append(nn.PixelShuffle(2))
        elif scale == 3:
            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
            m.append(nn.PixelShuffle(3))
        else:
            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
        super(Upsample, self).__init__(*m)


class UpsampleOneStep(nn.Sequential):
    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
       Used in lightweight SR to save parameters.

    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.

    """

    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
        self.num_feat = num_feat
        self.input_resolution = input_resolution
        m = []
        m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))
        m.append(nn.PixelShuffle(scale))
        super(UpsampleOneStep, self).__init__(*m)

    def flops(self):
        h, w = self.input_resolution
        flops = h * w * self.num_feat * 3 * 9
        return flops


class DAT(nn.Module):
    """ Dual Aggregation Transformer
    Args:
        img_size (int): Input image size. Default: 64
        in_chans (int): Number of input image channels. Default: 3
        embed_dim (int): Patch embedding dimension. Default: 180
        depths (tuple(int)): Depth of each residual group (number of DATB in each RG).
        split_size (tuple(int)): Height and Width of spatial window.
        num_heads (tuple(int)): Number of attention heads in different residual groups.
        expansion_factor (float): Ratio of ffn 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 | None): 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
        act_layer (nn.Module): Activation layer. Default: nn.GELU
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm
        use_chk (bool): Whether to use checkpointing to save memory.
        upscale: Upscale factor. 2/3/4 for image SR
        img_range: Image range. 1. or 255.
        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
    """
    def __init__(self,
                img_size=64,
                in_chans=3,
                embed_dim=180,
                split_size=[2,4],
                depth=[2,2,2,2],
                num_heads=[2,2,2,2],
                expansion_factor=4.,
                qkv_bias=True,
                qk_scale=None,
                drop_rate=0.,
                attn_drop_rate=0.,
                drop_path_rate=0.1,
                act_layer=nn.GELU,
                norm_layer=nn.LayerNorm,
                use_chk=False,
                upscale=2,
                img_range=1.,
                resi_connection='1conv',
                upsampler='pixelshuffle',
                **kwargs):
        super().__init__()

        num_in_ch = in_chans
        num_out_ch = in_chans
        num_feat = 64
        self.img_range = img_range
        if in_chans == 3:
            rgb_mean = (0.4488, 0.4371, 0.4040)
            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
        else:
            self.mean = torch.zeros(1, 1, 1, 1)
        self.upscale = upscale
        self.upsampler = upsampler

        # ------------------------- 1, Shallow Feature Extraction ------------------------- #
        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)

        # ------------------------- 2, Deep Feature Extraction ------------------------- #
        self.num_layers = len(depth)
        self.use_chk = use_chk
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        heads=num_heads

        self.before_RG = nn.Sequential(
            Rearrange('b c h w -> b (h w) c'),
            nn.LayerNorm(embed_dim)
        )

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

        self.layers = nn.ModuleList()
        for i in range(self.num_layers):
            layer = ResidualGroup(
                dim=embed_dim,
                num_heads=heads[i],
                reso=img_size,
                split_size=split_size,
                expansion_factor=expansion_factor,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_paths=dpr[sum(depth[:i]):sum(depth[:i + 1])],
                act_layer=act_layer,
                norm_layer=norm_layer,
                depth=depth[i],
                use_chk=use_chk,
                resi_connection=resi_connection,
                rg_idx=i)
            self.layers.append(layer)

        self.norm = norm_layer(curr_dim)
        # build the last conv layer in deep feature extraction
        if resi_connection == '1conv':
            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
        elif resi_connection == '3conv':
            # to save parameters and memory
            self.conv_after_body = nn.Sequential(
                nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
                nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True),
                nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))

        # ------------------------- 3, Reconstruction ------------------------- #
        if self.upsampler == 'pixelshuffle':
            # for classical SR
            self.conv_before_upsample = nn.Sequential(
                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True))
            self.upsample = Upsample(upscale, num_feat)
            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
        elif self.upsampler == 'pixelshuffledirect':
            # for lightweight SR (to save parameters)
            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
                                            (img_size, img_size))

        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.BatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d)):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def forward_features(self, x):
        _, _, H, W = x.shape
        x_size = [H, W]
        x = self.before_RG(x)
        for layer in self.layers:
            x = layer(x, x_size)
        x = self.norm(x)
        x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W)

        return x

    def forward(self, x):
        """
        Input: x: (B, C, H, W)
        """
        self.mean = self.mean.type_as(x)
        x = (x - self.mean) * self.img_range

        if self.upsampler == 'pixelshuffle':
            # for image SR
            x = self.conv_first(x)
            x = self.conv_after_body(self.forward_features(x)) + x
            x = self.conv_before_upsample(x)
            x = self.conv_last(self.upsample(x))
        elif self.upsampler == 'pixelshuffledirect':
            # for lightweight SR
            x = self.conv_first(x)
            x = self.conv_after_body(self.forward_features(x)) + x
            x = self.upsample(x)

        x = x / self.img_range + self.mean
        return x


if __name__ == '__main__':
    upscale = 1
    height = 64
    width = 64
    model = DAT(upscale=4,
                in_chans=3,
                img_size=64,
                img_range=1.,
                depth=[18],
                embed_dim=60,
                num_heads=[6],
                expansion_factor=2,
                resi_connection='3conv',
                split_size=[8,32],
                upsampler='pixelshuffledirect',
                ).cuda().eval()

    print(height, width)

    x = torch.randn((1, 3, height, width)).cuda()
    x = model(x)

    print(x.shape)