diff --git "a/hit_sng_arch.py" "b/hit_sng_arch.py"
--- "a/hit_sng_arch.py"
+++ "b/hit_sng_arch.py"
@@ -1,1132 +1,1130 @@
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_, _assert
-from torchvision.transforms import functional as TF
-from timm.models.fx_features import register_notrace_function
-
-import numpy as np
-from einops import rearrange
-from huggingface_hub import PyTorchModelHubMixin
-from utils import FileClient, imfrombytes, img2tensor, tensor2img
-
-class DFE(nn.Module):
-    """ Dual Feature Extraction 
-    Args:
-        in_features (int): Number of input channels.
-        out_features (int): Number of output channels.
-    """
-    def __init__(self, in_features, out_features):
-        super().__init__()
-
-        self.out_features = out_features
-
-        self.conv = nn.Sequential(nn.Conv2d(in_features, in_features // 5, 1, 1, 0),
-                        nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                        nn.Conv2d(in_features // 5, in_features // 5, 3, 1, 1),
-                        nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                        nn.Conv2d(in_features // 5, out_features, 1, 1, 0))
-        
-        self.linear = nn.Conv2d(in_features, out_features,1,1,0)
-
-    def forward(self, x, x_size):
-        
-        B, L, C = x.shape
-        H, W = x_size
-        x = x.permute(0, 2, 1).contiguous().view(B, C, H, W)
-        x = self.conv(x) * self.linear(x)
-        x = x.view(B, -1, H*W).permute(0,2,1).contiguous()
-
-        return x
-
-class Mlp(nn.Module):
-    """ MLP-based 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.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
-    wh, ww = H//window_size, W//window_size
-    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, (wh, ww)
-
-@register_notrace_function  # reason: int argument is a Proxy
-def window_unpartition(windows, num_windows):
-    """
-    Args:
-        windows: [B*wh*ww, WH, WW, D]
-        num_windows (tuple[int]): The height and width of the window.
-    Returns:
-        x: [B, ph, pw, D]
-    """
-    x = rearrange(windows, '(p h w) wh ww c -> p (h wh) (w ww) c', h=num_windows[0], w=num_windows[1])
-    return x.contiguous()
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (tuple): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] * (window_size[0] * window_size[1]) / (H * W))
-    x = windows.view(B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    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 heads for spatial self-correlation.
-        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 SCC(nn.Module):
-    """ Spatial-Channel Correlation.
-    Args:
-        dim (int): Number of input channels.
-        base_win_size (tuple[int]): The height and width of the base window.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of heads for spatial self-correlation.
-        value_drop (float, optional): Dropout ratio of value. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, base_win_size, window_size, num_heads, value_drop=0., proj_drop=0.):
-
-        super().__init__()
-        # parameters
-        self.dim = dim
-        self.window_size = window_size 
-        self.num_heads = num_heads
-
-        # feature projection
-        head_dim = dim // (2*num_heads)
-        if dim % (2*num_heads) > 0:
-            head_dim = head_dim + 1
-        self.attn_dim = head_dim * 2 * num_heads
-        self.qv = DFE(dim, self.attn_dim)
-        self.proj = nn.Linear(self.attn_dim, dim)
-
-        # dropout
-        self.value_drop = nn.Dropout(value_drop)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        # base window size
-        min_h = min(self.window_size[0], base_win_size[0])
-        min_w = min(self.window_size[1], base_win_size[1])
-        self.base_win_size = (min_h, min_w)
-
-        # normalization factor and spatial linear layer for S-SC
-        self.scale = head_dim
-        self.spatial_linear = nn.Linear(self.window_size[0]*self.window_size[1] // (self.base_win_size[0]*self.base_win_size[1]), 1)
-
-        # NGram window partition without shifting
-        self.ngram_window_partition = NGramWindowPartition(dim, window_size, 2, num_heads, shift_size=0)
-
-        # define a parameter table of relative position bias
-        self.H_sp, self.W_sp = self.window_size
-        self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False)
-        
-    def spatial_linear_projection(self, x):
-        B, num_h, L, C = x.shape
-        H, W = self.window_size
-        map_H, map_W = self.base_win_size
-
-        x = x.view(B, num_h, map_H, H//map_H, map_W, W//map_W, C).permute(0,1,2,4,6,3,5).contiguous().view(B, num_h, map_H*map_W, C, -1)
-        x = self.spatial_linear(x).view(B, num_h, map_H*map_W, C)
-        return x
-    
-    def spatial_self_correlation(self, q, v):
-        
-        B, num_head, L, C = q.shape
-
-        # spatial projection
-        v = self.spatial_linear_projection(v)
-
-        # compute correlation map
-        corr_map = (q @ v.transpose(-2,-1)) / self.scale
-
-        # add relative position bias
-        position_bias_h = torch.arange(1 - self.H_sp, self.H_sp, device=v.device)
-        position_bias_w = torch.arange(1 - self.W_sp, self.W_sp, device=v.device)
-        biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w]))
-        rpe_biases = biases.flatten(1).transpose(0, 1).contiguous().float()
-        pos = self.pos(rpe_biases)
-
-        # select position bias
-        coords_h = torch.arange(self.H_sp, device=v.device)
-        coords_w = torch.arange(self.W_sp, device=v.device)
-        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)
-        relative_position_bias = pos[relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.base_win_size[0], self.window_size[0]//self.base_win_size[0], self.base_win_size[1], self.window_size[1]//self.base_win_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(0,1,3,5,2,4).contiguous().view(
-            self.window_size[0] * self.window_size[1], self.base_win_size[0]*self.base_win_size[1], self.num_heads, -1).mean(-1)
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() 
-        corr_map = corr_map + relative_position_bias.unsqueeze(0)
-
-        # transformation
-        v_drop = self.value_drop(v)
-        x = (corr_map @ v_drop).permute(0,2,1,3).contiguous().view(B, L, -1) 
-
-        return x
-    
-    def channel_self_correlation(self, q, v):
-        
-        B, num_head, L, C = q.shape
-
-        # apply single head strategy
-        q = q.permute(0,2,1,3).contiguous().view(B, L, num_head*C)
-        v = v.permute(0,2,1,3).contiguous().view(B, L, num_head*C)
-
-        # compute correlation map
-        corr_map = (q.transpose(-2,-1) @ v) / L
-        
-        # transformation
-        v_drop = self.value_drop(v)
-        x = (corr_map @ v_drop.transpose(-2,-1)).permute(0,2,1).contiguous().view(B, L, -1)
-
-        return x
-
-    def forward(self, x):
-        """
-        Args:
-            x: input features with shape of (B, H, W, C)
-        """
-        xB,xH,xW,xC = x.shape
-        qv = self.qv(x.view(xB,-1,xC), (xH,xW)).view(xB, xH, xW, xC)
-
-        # window partition
-        qv = self.ngram_window_partition(qv)
-        qv = qv.view(-1, self.window_size[0]*self.window_size[1], xC)
-
-        # qv splitting
-        B, L, C = qv.shape
-        qv = qv.view(B, L, 2, self.num_heads, C // (2*self.num_heads)).permute(2,0,3,1,4).contiguous()
-        q, v = qv[0], qv[1] # B, num_heads, L, C//num_heads
-
-        # spatial self-correlation (S-SC)
-        x_spatial = self.spatial_self_correlation(q, v)
-        x_spatial = x_spatial.view(-1, self.window_size[0], self.window_size[1], C//2)
-        x_spatial = window_reverse(x_spatial, (self.window_size[0],self.window_size[1]), xH, xW)  # xB xH xW xC
-
-        # channel self-correlation (C-SC)
-        x_channel = self.channel_self_correlation(q, v)
-        x_channel = x_channel.view(-1, self.window_size[0], self.window_size[1], C//2)
-        x_channel = window_reverse(x_channel, (self.window_size[0], self.window_size[1]), xH, xW) # xB xH xW xC
-
-        # spatial-channel information fusion
-        x = torch.cat([x_spatial, x_channel], -1)
-        x = self.proj_drop(self.proj(x))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-class NGramWindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias for NGram attention.
-    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}'
-
-class NGramContext(nn.Module):
-    '''
-    Args:
-        dim (int): Number of input channels.
-        window_size (int or tuple[int]): The height and width of the window.
-        ngram (int): How much windows(or patches) to see.
-        ngram_num_heads (int):
-        padding_mode (str, optional): How to pad.  Default: seq_refl_win_pad
-                                                   Options: ['seq_refl_win_pad', 'zero_pad']
-    '''
-    def __init__(self, dim, window_size, ngram, ngram_num_heads, padding_mode='seq_refl_win_pad'):
-        super(NGramContext, self).__init__()
-        _assert(padding_mode in ['seq_refl_win_pad', 'zero_pad'], "padding mode should be 'seq_refl_win_pad' or 'zero_pad'!!")
-        
-        self.dim = dim
-        self.window_size = to_2tuple(window_size)
-        self.ngram = ngram
-        self.padding_mode = padding_mode
-        
-        # to alleviate parameter expansion with window sizes
-        self.unigram_embed = nn.Conv2d(2, 1,
-                                       kernel_size=(self.window_size[0], self.window_size[1]), 
-                                       stride=self.window_size, padding=0, groups=1)
-
-        self.ngram_attn = NGramWindowAttention(dim=dim//2, num_heads=ngram_num_heads, window_size=(ngram, ngram))
-        self.avg_pool = nn.AvgPool2d(ngram)
-        self.merge = nn.Conv2d(dim, dim, 1, 1, 0)
-        
-    def seq_refl_win_pad(self, x, back=False):
-        if self.ngram == 1: return x
-        x = TF.pad(x, (0,0,self.ngram-1,self.ngram-1)) if not back else TF.pad(x, (self.ngram-1,self.ngram-1,0,0))
-        if self.padding_mode == 'zero_pad':
-            return x
-        if not back:
-            (start_h, start_w), (end_h, end_w) = to_2tuple(-2*self.ngram+1), to_2tuple(-self.ngram)
-            # pad lower
-            x[:,:,-(self.ngram-1):,:] = x[:,:,start_h:end_h,:]
-            # pad right
-            x[:,:,:,-(self.ngram-1):] = x[:,:,:,start_w:end_w]
-        else:
-            (start_h, start_w), (end_h, end_w) = to_2tuple(self.ngram), to_2tuple(2*self.ngram-1)
-            # pad upper
-            x[:,:,:self.ngram-1,:] = x[:,:,start_h:end_h,:]
-            # pad left
-            x[:,:,:,:self.ngram-1] = x[:,:,:,start_w:end_w]
-            
-        return x
-    
-    def sliding_window_attention(self, unigram):
-        slide = unigram.unfold(3, self.ngram, 1).unfold(2, self.ngram, 1)
-        slide = rearrange(slide, 'b c h w ww hh -> b (h hh) (w ww) c') # [B, 2(wh+ngram-2), 2(ww+ngram-2), D/2]
-        slide, num_windows = window_partition(slide, self.ngram) # [B*wh*ww, ngram, ngram, D/2], (wh, ww)
-        slide = slide.view(-1, self.ngram*self.ngram, self.dim//2) # [B*wh*ww, ngram*ngram, D/2]
-        
-        context = self.ngram_attn(slide).view(-1, self.ngram, self.ngram, self.dim//2) # [B*wh*ww, ngram, ngram, D/2]
-
-        context = window_unpartition(context, num_windows) # [B, wh*ngram, ww*ngram, D/2]
-        context = rearrange(context, 'b h w d -> b d h w') # [B, D/2, wh*ngram, ww*ngram]
-        context = self.avg_pool(context) # [B, D/2, wh, ww]
-        return context
-        
-    def forward(self, x):
-        B, ph, pw, D = x.size()
-        x = rearrange(x, 'b ph pw d -> b d ph pw') # [B, D, ph, pw]
-        x = x.contiguous().view(B*(D//2),2,ph,pw)
-        unigram = self.unigram_embed(x).view(B, D//2, ph//self.window_size[0], pw//self.window_size[1])
-
-        unigram_forward_pad = self.seq_refl_win_pad(unigram, False) # [B, D/2, wh+ngram-1, ww+ngram-1]
-        unigram_backward_pad = self.seq_refl_win_pad(unigram, True) # [B, D/2, wh+ngram-1, ww+ngram-1]
-        
-        context_forward = self.sliding_window_attention(unigram_forward_pad) # [B, D/2, wh, ww]
-        context_backward = self.sliding_window_attention(unigram_backward_pad) # [B, D/2, wh, ww]
-        
-        context_bidirect = torch.cat([context_forward, context_backward], dim=1) # [B, D, wh, ww]
-        context_bidirect = self.merge(context_bidirect) # [B, D, wh, ww]
-        context_bidirect = rearrange(context_bidirect, 'b d h w -> b h w d') # [B, wh, ww, D]
-        
-        return context_bidirect.unsqueeze(-2).unsqueeze(-2).contiguous() # [B, wh, ww, 1, 1, D]
-
-class NGramWindowPartition(nn.Module):
-    """
-    Args:
-        dim (int): Number of input channels.
-        window_size (int): The height and width of the window.
-        ngram (int): How much windows to see as context.
-        ngram_num_heads (int):
-        shift_size (int, optional): Shift size for SW-MSA.  Default: 0
-    """
-    def __init__(self, dim, window_size, ngram, ngram_num_heads, shift_size=0):
-        super(NGramWindowPartition, self).__init__()
-        self.window_size = window_size[0]
-        self.ngram = ngram
-        self.shift_size = shift_size
-        
-        self.ngram_context = NGramContext(dim, window_size, ngram, ngram_num_heads, padding_mode='seq_refl_win_pad')
-    
-    def forward(self, x):
-        B, ph, pw, D = x.size()
-        wh, ww = ph//self.window_size, pw//self.window_size # number of windows (height, width)
-        _assert(0 not in [wh, ww], "feature map size should be larger than window size!")
-        
-        context = self.ngram_context(x) # [B, wh, ww, 1, 1, D]
-        
-        windows = rearrange(x, 'b (h wh) (w ww) c -> b h w wh ww c', 
-                            wh=self.window_size, ww=self.window_size).contiguous() # [B, wh, ww, WH, WW, D]. semi window partitioning
-        windows+=context # [B, wh, ww, WH, WW, D]. inject context
-        
-        # Cyclic Shift
-        if self.shift_size>0:
-            x = rearrange(windows, 'b h w wh ww c -> b (h wh) (w ww) c').contiguous() # [B, ph, pw, D]. re-patchfying
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) # [B, ph, pw, D]. cyclic shift
-            windows = rearrange(shifted_x, 'b (h wh) (w ww) c -> b h w wh ww c', 
-                                wh=self.window_size, ww=self.window_size).contiguous() # [B, wh, ww, WH, WW, D]. re-semi window partitioning
-        windows = rearrange(windows, 'b h w wh ww c -> (b h w) wh ww c').contiguous() # [B*wh*ww, WH, WW, D]. window partitioning
-        
-        return windows
-
-
-class HierarchicalTransformerBlock(nn.Module):
-    """ Hierarchical Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of heads for spatial self-correlation.
-        base_win_size (tuple[int]): The height and width of the base window.
-        window_size (tuple[int]): The height and width of the window.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        drop (float, optional): Dropout rate. Default: 0.0
-        value_drop (float, optional): Dropout ratio of value. 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, base_win_size, window_size,
-                 mlp_ratio=4., drop=0., value_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.mlp_ratio = mlp_ratio
-
-        # check window size
-        if (window_size[0] > base_win_size[0]) and (window_size[1] > base_win_size[1]):
-            assert window_size[0] % base_win_size[0] == 0, "please ensure the window size is smaller than or divisible by the base window size"
-            assert window_size[1] % base_win_size[1] == 0, "please ensure the window size is smaller than or divisible by the base window size"
-
-
-        self.norm1 = norm_layer(dim)
-        self.correlation = SCC(
-            dim, base_win_size=base_win_size, window_size=self.window_size, num_heads=num_heads,
-            value_drop=value_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)
-
-    def check_image_size(self, x, win_size):
-        x = x.permute(0,3,1,2).contiguous()
-        _, _, h, w = x.size()
-        mod_pad_h = (win_size[0] - h % win_size[0]) % win_size[0]
-        mod_pad_w = (win_size[1] - w % win_size[1]) % win_size[1]
-
-        if mod_pad_h >= h or mod_pad_w >= w:
-            pad_h, pad_w = h-1, w-1
-            x = F.pad(x, (0, pad_w, 0, pad_h), 'reflect')
-        else:
-            pad_h, pad_w = 0, 0
-        
-        mod_pad_h = mod_pad_h - pad_h
-        mod_pad_w = mod_pad_w - pad_w
-        
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
-        x = x.permute(0,2,3,1).contiguous()
-        return x
-
-    def forward(self, x, x_size, win_size):
-        H, W = x_size
-        B, L, C = x.shape
-
-        shortcut = x
-        x = x.view(B, H, W, C)
-        
-        # padding
-        x = self.check_image_size(x, (win_size[0]*2, win_size[1]*2))
-        _, H_pad, W_pad, _ = x.shape # shape after padding
-
-        x = self.correlation(x) 
-
-        # unpad
-        x = x[:, :H, :W, :].contiguous()
-
-        # norm
-        x = x.view(B, H * W, C)
-        x = self.norm1(x)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.norm2(self.mlp(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}, mlp_ratio={self.mlp_ratio}"
-
-
-class PatchMerging(nn.Module):
-    """ 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}"
-
-
-class BasicLayer(nn.Module):
-    """ A basic Hierarchical 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 heads for spatial self-correlation.
-        base_win_size (tuple[int]): The height and width of the base window.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        drop (float, optional): Dropout rate. Default: 0.0
-        value_drop (float, optional): Dropout ratio of value. 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.
-        hier_win_ratios (list): hierarchical window ratios for a transformer block. Default: [0.5,1,2,4,6,8].
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, base_win_size,
-                 mlp_ratio=4., drop=0., value_drop=0.,drop_path=0., norm_layer=nn.LayerNorm,
-                   downsample=None, use_checkpoint=False, hier_win_ratios=[0.5,1,2,4,6,8]):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        self.win_hs = [int(base_win_size[0] * ratio) for ratio in hier_win_ratios]
-        self.win_ws = [int(base_win_size[1] * ratio) for ratio in hier_win_ratios]
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            HierarchicalTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, 
-                                 base_win_size=base_win_size,
-                                 window_size=(self.win_hs[i], self.win_ws[i]),
-                                 mlp_ratio=mlp_ratio,
-                                 drop=drop, value_drop=value_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, x_size):
-
-        i = 0
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size, (self.win_hs[i], self.win_ws[i]))
-            else:
-                x = blk(x, x_size, (self.win_hs[i], self.win_ws[i]))
-            i = i + 1
-
-        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}"
-
-
-class RHTB(nn.Module):
-    """Residual Hierarchical Transformer Block (RHTB).
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of heads for spatial self-correlation.
-        base_win_size (tuple[int]): The height and width of the base window.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        drop (float, optional): Dropout rate. Default: 0.0
-        value_drop (float, optional): Dropout ratio of value. 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.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-        hier_win_ratios (list): hierarchical window ratios for a transformer block. Default: [0.5,1,2,4,6,8].
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, base_win_size,
-                 mlp_ratio=4., drop=0., value_drop=0., drop_path=0., norm_layer=nn.LayerNorm, 
-                 downsample=None, use_checkpoint=False, img_size=224, patch_size=4, 
-                 resi_connection='1conv', hier_win_ratios=[0.5,1,2,4,6,8]):
-        super(RHTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(dim=dim,
-                                         input_resolution=input_resolution,
-                                         depth=depth,
-                                         num_heads=num_heads,
-                                         base_win_size=base_win_size,
-                                         mlp_ratio=mlp_ratio,
-                                         drop=drop, value_drop=value_drop,
-                                         drop_path=drop_path,
-                                         norm_layer=norm_layer,
-                                         downsample=downsample,
-                                         use_checkpoint=use_checkpoint,
-                                         hier_win_ratios=hier_win_ratios)
-
-        if resi_connection == '1conv':
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            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))
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-    def forward(self, x, x_size):
-        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + 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
-
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-
-class PatchUnEmbed(nn.Module):
-    r""" Image to Patch Unembedding
-
-    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
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        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)
-
-
-class HiT_SNG(nn.Module, PyTorchModelHubMixin):
-    """ HiT-SNG network.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Transformer block.
-        num_heads (tuple(int)): Number of heads for spatial self-correlation in different layers.
-        base_win_size (tuple[int]): The height and width of the base window.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        drop_rate (float): Dropout rate. Default: 0
-        value_drop_rate (float): Dropout ratio of value. Default: 0.0
-        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
-        upscale (int): Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range (float): Image range. 1. or 255.
-        upsampler (str): The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection (str): The convolutional block before residual connection. '1conv'/'3conv'
-        hier_win_ratios (list): hierarchical window ratios for a transformer block. Default: [0.5,1,2,4,6,8].
-    """
-
-    def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=60, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
-                 base_win_size=[8,8], mlp_ratio=2.,
-                 drop_rate=0., value_drop_rate=0., drop_path_rate=0.,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, upscale=4, img_range=1., upsampler='pixelshuffledirect', resi_connection='1conv',
-                 hier_win_ratios=[0.5,1,2,4,6,8],
-                 **kwargs):
-        super(HiT_SNG, self).__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
-        self.base_win_size = base_win_size
-
-        #####################################################################################################
-        ################################### 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(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, 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
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # 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 Residual Hierarchical Transformer blocks (RHTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RHTB(dim=embed_dim,
-                         input_resolution=(patches_resolution[0],
-                                           patches_resolution[1]),
-                         depth=depths[i_layer],
-                         num_heads=num_heads[i_layer],
-                         base_win_size=base_win_size,
-                         mlp_ratio=self.mlp_ratio,
-                         drop=drop_rate, value_drop=value_drop_rate,
-                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                         norm_layer=norm_layer,
-                         downsample=None,
-                         use_checkpoint=use_checkpoint,
-                         img_size=img_size,
-                         patch_size=patch_size,
-                         resi_connection=resi_connection,
-                         hier_win_ratios=hier_win_ratios
-                         )
-            self.layers.append(layer)
-        self.norm = norm_layer(self.num_features)
-
-        # 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, high quality image 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,
-                                            (patches_resolution[0], patches_resolution[1]))
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR (less artifacts)
-            assert self.upscale == 4, 'only support x4 now.'
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        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 forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-    
-    def infer_image(self, image_path, cuda=True):
-
-        io_backend_opt = {'type':'disk'}
-        self.file_client = FileClient(io_backend_opt.pop('type'), **io_backend_opt)
-
-        # load lq image
-        lq_path = image_path
-        img_bytes = self.file_client.get(lq_path, 'lq')
-        img_lq = imfrombytes(img_bytes, float32=True)
-
-        # BGR to RGB, HWC to CHW, numpy to tensor
-        x = img2tensor(img_lq, bgr2rgb=True, float32=True)[None,...]
-
-        if cuda:
-            x= x.cuda()
-
-        out = self(x)
-
-        if cuda:
-            out = out.cpu()
-
-        out = tensor2img(out)
-
-        return out
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == 'pixelshuffle':
-            # for classical 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)
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-
-        return x[:, :, :H*self.upscale, :W*self.upscale]
-
-
-if __name__ == '__main__':
-    upscale = 4
-    base_win_size = [8, 8]
-    height = (1024 // upscale // base_win_size[0] + 1) * base_win_size[0]
-    width = (720 // upscale // base_win_size[1] + 1) * base_win_size[1]
-    
-    ## HiT-SIR
-    model = HiT_SNG(upscale=4, img_size=(height, width),
-                   base_win_size=base_win_size, img_range=1., depths=[6, 6, 6, 6],
-                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
-
-    params_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
-    print("params: ", params_num)
-
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_, _assert
+from torchvision.transforms import functional as TF
+from timm.models.fx_features import register_notrace_function
+
+import numpy as np
+from einops import rearrange
+from huggingface_hub import PyTorchModelHubMixin
+from utils import FileClient, imfrombytes, img2tensor, tensor2img
+
+class DFE(nn.Module):
+    """ Dual Feature Extraction 
+    Args:
+        in_features (int): Number of input channels.
+        out_features (int): Number of output channels.
+    """
+    def __init__(self, in_features, out_features):
+        super().__init__()
+
+        self.out_features = out_features
+
+        self.conv = nn.Sequential(nn.Conv2d(in_features, in_features // 5, 1, 1, 0),
+                        nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                        nn.Conv2d(in_features // 5, in_features // 5, 3, 1, 1),
+                        nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                        nn.Conv2d(in_features // 5, out_features, 1, 1, 0))
+        
+        self.linear = nn.Conv2d(in_features, out_features,1,1,0)
+
+    def forward(self, x, x_size):
+        
+        B, L, C = x.shape
+        H, W = x_size
+        x = x.permute(0, 2, 1).contiguous().view(B, C, H, W)
+        x = self.conv(x) * self.linear(x)
+        x = x.view(B, -1, H*W).permute(0,2,1).contiguous()
+
+        return x
+
+class Mlp(nn.Module):
+    """ MLP-based 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.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
+    wh, ww = H//window_size, W//window_size
+    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, (wh, ww)
+
+@register_notrace_function  # reason: int argument is a Proxy
+def window_unpartition(windows, num_windows):
+    """
+    Args:
+        windows: [B*wh*ww, WH, WW, D]
+        num_windows (tuple[int]): The height and width of the window.
+    Returns:
+        x: [B, ph, pw, D]
+    """
+    x = rearrange(windows, '(p h w) wh ww c -> p (h wh) (w ww) c', h=num_windows[0], w=num_windows[1])
+    return x.contiguous()
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (tuple): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] * (window_size[0] * window_size[1]) / (H * W))
+    x = windows.view(B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    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 heads for spatial self-correlation.
+        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 SCC(nn.Module):
+    """ Spatial-Channel Correlation.
+    Args:
+        dim (int): Number of input channels.
+        base_win_size (tuple[int]): The height and width of the base window.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of heads for spatial self-correlation.
+        value_drop (float, optional): Dropout ratio of value. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self, dim, base_win_size, window_size, num_heads, value_drop=0., proj_drop=0.):
+
+        super().__init__()
+        # parameters
+        self.dim = dim
+        self.window_size = window_size 
+        self.num_heads = num_heads
+
+        # feature projection
+        head_dim = dim // (2*num_heads)
+        if dim % (2*num_heads) > 0:
+            head_dim = head_dim + 1
+        self.attn_dim = head_dim * 2 * num_heads
+        self.qv = DFE(dim, self.attn_dim)
+        self.proj = nn.Linear(self.attn_dim, dim)
+
+        # dropout
+        self.value_drop = nn.Dropout(value_drop)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        # base window size
+        min_h = min(self.window_size[0], base_win_size[0])
+        min_w = min(self.window_size[1], base_win_size[1])
+        self.base_win_size = (min_h, min_w)
+
+        # normalization factor and spatial linear layer for S-SC
+        self.scale = head_dim
+        self.spatial_linear = nn.Linear(self.window_size[0]*self.window_size[1] // (self.base_win_size[0]*self.base_win_size[1]), 1)
+
+        # NGram window partition without shifting
+        self.ngram_window_partition = NGramWindowPartition(dim, window_size, 2, num_heads, shift_size=0)
+
+        # define a parameter table of relative position bias
+        self.H_sp, self.W_sp = self.window_size
+        self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False)
+        
+    def spatial_linear_projection(self, x):
+        B, num_h, L, C = x.shape
+        H, W = self.window_size
+        map_H, map_W = self.base_win_size
+
+        x = x.view(B, num_h, map_H, H//map_H, map_W, W//map_W, C).permute(0,1,2,4,6,3,5).contiguous().view(B, num_h, map_H*map_W, C, -1)
+        x = self.spatial_linear(x).view(B, num_h, map_H*map_W, C)
+        return x
+    
+    def spatial_self_correlation(self, q, v):
+        
+        B, num_head, L, C = q.shape
+
+        # spatial projection
+        v = self.spatial_linear_projection(v)
+
+        # compute correlation map
+        corr_map = (q @ v.transpose(-2,-1)) / self.scale
+
+        # add relative position bias
+        position_bias_h = torch.arange(1 - self.H_sp, self.H_sp, device=v.device)
+        position_bias_w = torch.arange(1 - self.W_sp, self.W_sp, device=v.device)
+        biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w]))
+        rpe_biases = biases.flatten(1).transpose(0, 1).contiguous().float()
+        pos = self.pos(rpe_biases)
+
+        # select position bias
+        coords_h = torch.arange(self.H_sp, device=v.device)
+        coords_w = torch.arange(self.W_sp, device=v.device)
+        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)
+        relative_position_bias = pos[relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.base_win_size[0], self.window_size[0]//self.base_win_size[0], self.base_win_size[1], self.window_size[1]//self.base_win_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(0,1,3,5,2,4).contiguous().view(
+            self.window_size[0] * self.window_size[1], self.base_win_size[0]*self.base_win_size[1], self.num_heads, -1).mean(-1)
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() 
+        corr_map = corr_map + relative_position_bias.unsqueeze(0)
+
+        # transformation
+        v_drop = self.value_drop(v)
+        x = (corr_map @ v_drop).permute(0,2,1,3).contiguous().view(B, L, -1) 
+
+        return x
+    
+    def channel_self_correlation(self, q, v):
+        
+        B, num_head, L, C = q.shape
+
+        # apply single head strategy
+        q = q.permute(0,2,1,3).contiguous().view(B, L, num_head*C)
+        v = v.permute(0,2,1,3).contiguous().view(B, L, num_head*C)
+
+        # compute correlation map
+        corr_map = (q.transpose(-2,-1) @ v) / L
+        
+        # transformation
+        v_drop = self.value_drop(v)
+        x = (corr_map @ v_drop.transpose(-2,-1)).permute(0,2,1).contiguous().view(B, L, -1)
+
+        return x
+
+    def forward(self, x):
+        """
+        Args:
+            x: input features with shape of (B, H, W, C)
+        """
+        xB,xH,xW,xC = x.shape
+        qv = self.qv(x.view(xB,-1,xC), (xH,xW)).view(xB, xH, xW, xC)
+
+        # window partition
+        qv = self.ngram_window_partition(qv)
+        qv = qv.view(-1, self.window_size[0]*self.window_size[1], xC)
+
+        # qv splitting
+        B, L, C = qv.shape
+        qv = qv.view(B, L, 2, self.num_heads, C // (2*self.num_heads)).permute(2,0,3,1,4).contiguous()
+        q, v = qv[0], qv[1] # B, num_heads, L, C//num_heads
+
+        # spatial self-correlation (S-SC)
+        x_spatial = self.spatial_self_correlation(q, v)
+        x_spatial = x_spatial.view(-1, self.window_size[0], self.window_size[1], C//2)
+        x_spatial = window_reverse(x_spatial, (self.window_size[0],self.window_size[1]), xH, xW)  # xB xH xW xC
+
+        # channel self-correlation (C-SC)
+        x_channel = self.channel_self_correlation(q, v)
+        x_channel = x_channel.view(-1, self.window_size[0], self.window_size[1], C//2)
+        x_channel = window_reverse(x_channel, (self.window_size[0], self.window_size[1]), xH, xW) # xB xH xW xC
+
+        # spatial-channel information fusion
+        x = torch.cat([x_spatial, x_channel], -1)
+        x = self.proj_drop(self.proj(x))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+class NGramWindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias for NGram attention.
+    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}'
+
+class NGramContext(nn.Module):
+    '''
+    Args:
+        dim (int): Number of input channels.
+        window_size (int or tuple[int]): The height and width of the window.
+        ngram (int): How much windows(or patches) to see.
+        ngram_num_heads (int):
+        padding_mode (str, optional): How to pad.  Default: seq_refl_win_pad
+                                                   Options: ['seq_refl_win_pad', 'zero_pad']
+    '''
+    def __init__(self, dim, window_size, ngram, ngram_num_heads, padding_mode='seq_refl_win_pad'):
+        super(NGramContext, self).__init__()
+        _assert(padding_mode in ['seq_refl_win_pad', 'zero_pad'], "padding mode should be 'seq_refl_win_pad' or 'zero_pad'!!")
+        
+        self.dim = dim
+        self.window_size = to_2tuple(window_size)
+        self.ngram = ngram
+        self.padding_mode = padding_mode
+        
+        # to alleviate parameter expansion with window sizes
+        self.unigram_embed = nn.Conv2d(2, 1,
+                                       kernel_size=(self.window_size[0], self.window_size[1]), 
+                                       stride=self.window_size, padding=0, groups=1)
+
+        self.ngram_attn = NGramWindowAttention(dim=dim//2, num_heads=ngram_num_heads, window_size=(ngram, ngram))
+        self.avg_pool = nn.AvgPool2d(ngram)
+        self.merge = nn.Conv2d(dim, dim, 1, 1, 0)
+        
+    def seq_refl_win_pad(self, x, back=False):
+        if self.ngram == 1: return x
+        x = TF.pad(x, (0,0,self.ngram-1,self.ngram-1)) if not back else TF.pad(x, (self.ngram-1,self.ngram-1,0,0))
+        if self.padding_mode == 'zero_pad':
+            return x
+        if not back:
+            (start_h, start_w), (end_h, end_w) = to_2tuple(-2*self.ngram+1), to_2tuple(-self.ngram)
+            # pad lower
+            x[:,:,-(self.ngram-1):,:] = x[:,:,start_h:end_h,:]
+            # pad right
+            x[:,:,:,-(self.ngram-1):] = x[:,:,:,start_w:end_w]
+        else:
+            (start_h, start_w), (end_h, end_w) = to_2tuple(self.ngram), to_2tuple(2*self.ngram-1)
+            # pad upper
+            x[:,:,:self.ngram-1,:] = x[:,:,start_h:end_h,:]
+            # pad left
+            x[:,:,:,:self.ngram-1] = x[:,:,:,start_w:end_w]
+            
+        return x
+    
+    def sliding_window_attention(self, unigram):
+        slide = unigram.unfold(3, self.ngram, 1).unfold(2, self.ngram, 1)
+        slide = rearrange(slide, 'b c h w ww hh -> b (h hh) (w ww) c') # [B, 2(wh+ngram-2), 2(ww+ngram-2), D/2]
+        slide, num_windows = window_partition(slide, self.ngram) # [B*wh*ww, ngram, ngram, D/2], (wh, ww)
+        slide = slide.view(-1, self.ngram*self.ngram, self.dim//2) # [B*wh*ww, ngram*ngram, D/2]
+        
+        context = self.ngram_attn(slide).view(-1, self.ngram, self.ngram, self.dim//2) # [B*wh*ww, ngram, ngram, D/2]
+
+        context = window_unpartition(context, num_windows) # [B, wh*ngram, ww*ngram, D/2]
+        context = rearrange(context, 'b h w d -> b d h w') # [B, D/2, wh*ngram, ww*ngram]
+        context = self.avg_pool(context) # [B, D/2, wh, ww]
+        return context
+        
+    def forward(self, x):
+        B, ph, pw, D = x.size()
+        x = rearrange(x, 'b ph pw d -> b d ph pw') # [B, D, ph, pw]
+        x = x.contiguous().view(B*(D//2),2,ph,pw)
+        unigram = self.unigram_embed(x).view(B, D//2, ph//self.window_size[0], pw//self.window_size[1])
+
+        unigram_forward_pad = self.seq_refl_win_pad(unigram, False) # [B, D/2, wh+ngram-1, ww+ngram-1]
+        unigram_backward_pad = self.seq_refl_win_pad(unigram, True) # [B, D/2, wh+ngram-1, ww+ngram-1]
+        
+        context_forward = self.sliding_window_attention(unigram_forward_pad) # [B, D/2, wh, ww]
+        context_backward = self.sliding_window_attention(unigram_backward_pad) # [B, D/2, wh, ww]
+        
+        context_bidirect = torch.cat([context_forward, context_backward], dim=1) # [B, D, wh, ww]
+        context_bidirect = self.merge(context_bidirect) # [B, D, wh, ww]
+        context_bidirect = rearrange(context_bidirect, 'b d h w -> b h w d') # [B, wh, ww, D]
+        
+        return context_bidirect.unsqueeze(-2).unsqueeze(-2).contiguous() # [B, wh, ww, 1, 1, D]
+
+class NGramWindowPartition(nn.Module):
+    """
+    Args:
+        dim (int): Number of input channels.
+        window_size (int): The height and width of the window.
+        ngram (int): How much windows to see as context.
+        ngram_num_heads (int):
+        shift_size (int, optional): Shift size for SW-MSA.  Default: 0
+    """
+    def __init__(self, dim, window_size, ngram, ngram_num_heads, shift_size=0):
+        super(NGramWindowPartition, self).__init__()
+        self.window_size = window_size[0]
+        self.ngram = ngram
+        self.shift_size = shift_size
+        
+        self.ngram_context = NGramContext(dim, window_size, ngram, ngram_num_heads, padding_mode='seq_refl_win_pad')
+    
+    def forward(self, x):
+        B, ph, pw, D = x.size()
+        wh, ww = ph//self.window_size, pw//self.window_size # number of windows (height, width)
+        _assert(0 not in [wh, ww], "feature map size should be larger than window size!")
+        
+        context = self.ngram_context(x) # [B, wh, ww, 1, 1, D]
+        
+        windows = rearrange(x, 'b (h wh) (w ww) c -> b h w wh ww c', 
+                            wh=self.window_size, ww=self.window_size).contiguous() # [B, wh, ww, WH, WW, D]. semi window partitioning
+        windows+=context # [B, wh, ww, WH, WW, D]. inject context
+        
+        # Cyclic Shift
+        if self.shift_size>0:
+            x = rearrange(windows, 'b h w wh ww c -> b (h wh) (w ww) c').contiguous() # [B, ph, pw, D]. re-patchfying
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) # [B, ph, pw, D]. cyclic shift
+            windows = rearrange(shifted_x, 'b (h wh) (w ww) c -> b h w wh ww c', 
+                                wh=self.window_size, ww=self.window_size).contiguous() # [B, wh, ww, WH, WW, D]. re-semi window partitioning
+        windows = rearrange(windows, 'b h w wh ww c -> (b h w) wh ww c').contiguous() # [B*wh*ww, WH, WW, D]. window partitioning
+        
+        return windows
+
+
+class HierarchicalTransformerBlock(nn.Module):
+    """ Hierarchical Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of heads for spatial self-correlation.
+        base_win_size (tuple[int]): The height and width of the base window.
+        window_size (tuple[int]): The height and width of the window.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        drop (float, optional): Dropout rate. Default: 0.0
+        value_drop (float, optional): Dropout ratio of value. 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, base_win_size, window_size,
+                 mlp_ratio=4., drop=0., value_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.mlp_ratio = mlp_ratio
+
+        # check window size
+        if (window_size[0] > base_win_size[0]) and (window_size[1] > base_win_size[1]):
+            assert window_size[0] % base_win_size[0] == 0, "please ensure the window size is smaller than or divisible by the base window size"
+            assert window_size[1] % base_win_size[1] == 0, "please ensure the window size is smaller than or divisible by the base window size"
+
+
+        self.norm1 = norm_layer(dim)
+        self.correlation = SCC(
+            dim, base_win_size=base_win_size, window_size=self.window_size, num_heads=num_heads,
+            value_drop=value_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)
+
+    def check_image_size(self, x, win_size):
+        x = x.permute(0,3,1,2).contiguous()
+        _, _, h, w = x.size()
+        mod_pad_h = (win_size[0] - h % win_size[0]) % win_size[0]
+        mod_pad_w = (win_size[1] - w % win_size[1]) % win_size[1]
+
+        if mod_pad_h >= h or mod_pad_w >= w:
+            pad_h, pad_w = h-1, w-1
+            x = F.pad(x, (0, pad_w, 0, pad_h), 'reflect')
+        else:
+            pad_h, pad_w = 0, 0
+        
+        mod_pad_h = mod_pad_h - pad_h
+        mod_pad_w = mod_pad_w - pad_w
+        
+        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+        x = x.permute(0,2,3,1).contiguous()
+        return x
+
+    def forward(self, x, x_size, win_size):
+        H, W = x_size
+        B, L, C = x.shape
+
+        shortcut = x
+        x = x.view(B, H, W, C)
+        
+        # padding
+        x = self.check_image_size(x, (win_size[0]*2, win_size[1]*2))
+        _, H_pad, W_pad, _ = x.shape # shape after padding
+
+        x = self.correlation(x) 
+
+        # unpad
+        x = x[:, :H, :W, :].contiguous()
+
+        # norm
+        x = x.view(B, H * W, C)
+        x = self.norm1(x)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.norm2(self.mlp(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}, mlp_ratio={self.mlp_ratio}"
+
+
+class PatchMerging(nn.Module):
+    """ 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}"
+
+
+class BasicLayer(nn.Module):
+    """ A basic Hierarchical 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 heads for spatial self-correlation.
+        base_win_size (tuple[int]): The height and width of the base window.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        drop (float, optional): Dropout rate. Default: 0.0
+        value_drop (float, optional): Dropout ratio of value. 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.
+        hier_win_ratios (list): hierarchical window ratios for a transformer block. Default: [0.5,1,2,4,6,8].
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, base_win_size,
+                 mlp_ratio=4., drop=0., value_drop=0.,drop_path=0., norm_layer=nn.LayerNorm,
+                   downsample=None, use_checkpoint=False, hier_win_ratios=[0.5,1,2,4,6,8]):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        self.win_hs = [int(base_win_size[0] * ratio) for ratio in hier_win_ratios]
+        self.win_ws = [int(base_win_size[1] * ratio) for ratio in hier_win_ratios]
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            HierarchicalTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, 
+                                 base_win_size=base_win_size,
+                                 window_size=(self.win_hs[i], self.win_ws[i]),
+                                 mlp_ratio=mlp_ratio,
+                                 drop=drop, value_drop=value_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, x_size):
+
+        i = 0
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, x_size, (self.win_hs[i], self.win_ws[i]))
+            else:
+                x = blk(x, x_size, (self.win_hs[i], self.win_ws[i]))
+            i = i + 1
+
+        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}"
+
+
+class RHTB(nn.Module):
+    """Residual Hierarchical Transformer Block (RHTB).
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of heads for spatial self-correlation.
+        base_win_size (tuple[int]): The height and width of the base window.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        drop (float, optional): Dropout rate. Default: 0.0
+        value_drop (float, optional): Dropout ratio of value. 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.
+        img_size: Input image size.
+        patch_size: Patch size.
+        resi_connection: The convolutional block before residual connection.
+        hier_win_ratios (list): hierarchical window ratios for a transformer block. Default: [0.5,1,2,4,6,8].
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, base_win_size,
+                 mlp_ratio=4., drop=0., value_drop=0., drop_path=0., norm_layer=nn.LayerNorm, 
+                 downsample=None, use_checkpoint=False, img_size=224, patch_size=4, 
+                 resi_connection='1conv', hier_win_ratios=[0.5,1,2,4,6,8]):
+        super(RHTB, self).__init__()
+
+        self.dim = dim
+        self.input_resolution = input_resolution
+
+        self.residual_group = BasicLayer(dim=dim,
+                                         input_resolution=input_resolution,
+                                         depth=depth,
+                                         num_heads=num_heads,
+                                         base_win_size=base_win_size,
+                                         mlp_ratio=mlp_ratio,
+                                         drop=drop, value_drop=value_drop,
+                                         drop_path=drop_path,
+                                         norm_layer=norm_layer,
+                                         downsample=downsample,
+                                         use_checkpoint=use_checkpoint,
+                                         hier_win_ratios=hier_win_ratios)
+
+        if resi_connection == '1conv':
+            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            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))
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+            norm_layer=None)
+
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+            norm_layer=None)
+
+    def forward(self, x, x_size):
+        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + 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
+
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+
+class PatchUnEmbed(nn.Module):
+    r""" Image to Patch Unembedding
+
+    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
+
+    def forward(self, x, x_size):
+        B, HW, C = x.shape
+        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
+        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)
+
+
+class HiT_SNG(nn.Module, PyTorchModelHubMixin):
+    """ HiT-SNG network.
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 64
+        patch_size (int | tuple(int)): Patch size. Default: 1
+        in_chans (int): Number of input image channels. Default: 3
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Transformer block.
+        num_heads (tuple(int)): Number of heads for spatial self-correlation in different layers.
+        base_win_size (tuple[int]): The height and width of the base window.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        drop_rate (float): Dropout rate. Default: 0
+        value_drop_rate (float): Dropout ratio of value. Default: 0.0
+        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
+        upscale (int): Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+        img_range (float): Image range. 1. or 255.
+        upsampler (str): The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+        resi_connection (str): The convolutional block before residual connection. '1conv'/'3conv'
+        hier_win_ratios (list): hierarchical window ratios for a transformer block. Default: [0.5,1,2,4,6,8].
+    """
+
+    def __init__(self, img_size=64, patch_size=1, in_chans=3,
+                 embed_dim=60, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 base_win_size=[8,8], mlp_ratio=2.,
+                 drop_rate=0., value_drop_rate=0., drop_path_rate=0.,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, upscale=4, img_range=1., upsampler='pixelshuffledirect', resi_connection='1conv',
+                 hier_win_ratios=[0.5,1,2,4,6,8],
+                 **kwargs):
+        super(HiT_SNG, self).__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
+        self.base_win_size = base_win_size
+
+        #####################################################################################################
+        ################################### 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(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = embed_dim
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, 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
+
+        # merge non-overlapping patches into image
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # 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 Residual Hierarchical Transformer blocks (RHTB)
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = RHTB(dim=embed_dim,
+                         input_resolution=(patches_resolution[0],
+                                           patches_resolution[1]),
+                         depth=depths[i_layer],
+                         num_heads=num_heads[i_layer],
+                         base_win_size=base_win_size,
+                         mlp_ratio=self.mlp_ratio,
+                         drop=drop_rate, value_drop=value_drop_rate,
+                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                         norm_layer=norm_layer,
+                         downsample=None,
+                         use_checkpoint=use_checkpoint,
+                         img_size=img_size,
+                         patch_size=patch_size,
+                         resi_connection=resi_connection,
+                         hier_win_ratios=hier_win_ratios
+                         )
+            self.layers.append(layer)
+        self.norm = norm_layer(self.num_features)
+
+        # 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, high quality image 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,
+                                            (patches_resolution[0], patches_resolution[1]))
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR (less artifacts)
+            assert self.upscale == 4, 'only support x4 now.'
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+        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 forward_features(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x
+    
+    def infer_image(self, image_path, device):
+
+        io_backend_opt = {'type':'disk'}
+        self.file_client = FileClient(io_backend_opt.pop('type'), **io_backend_opt)
+
+        # load lq image
+        lq_path = image_path
+        img_bytes = self.file_client.get(lq_path, 'lq')
+        img_lq = imfrombytes(img_bytes, float32=True)
+
+        # BGR to RGB, HWC to CHW, numpy to tensor
+        x = img2tensor(img_lq, bgr2rgb=True, float32=True)[None,...]
+
+        x= x.to(device)
+
+        out = self(x)
+
+        out = out.cpu()
+
+        out = tensor2img(out)
+
+        return out
+
+    def forward(self, x):
+        H, W = x.shape[2:]
+
+        self.mean = self.mean.type_as(x)
+        x = (x - self.mean) * self.img_range
+
+        if self.upsampler == 'pixelshuffle':
+            # for classical 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)
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.conv_last(self.lrelu(self.conv_hr(x)))
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            x_first = self.conv_first(x)
+            res = self.conv_after_body(self.forward_features(x_first)) + x_first
+            x = x + self.conv_last(res)
+
+        x = x / self.img_range + self.mean
+
+        return x[:, :, :H*self.upscale, :W*self.upscale]
+
+
+if __name__ == '__main__':
+    upscale = 4
+    base_win_size = [8, 8]
+    height = (1024 // upscale // base_win_size[0] + 1) * base_win_size[0]
+    width = (720 // upscale // base_win_size[1] + 1) * base_win_size[1]
+    
+    ## HiT-SIR
+    model = HiT_SNG(upscale=4, img_size=(height, width),
+                   base_win_size=base_win_size, img_range=1., depths=[6, 6, 6, 6],
+                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+
+    params_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    print("params: ", params_num)
+