XiangZ
/

hit-sr

@@ -1,947 +1,945 @@
-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_
-import numpy as np
-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
-class dwconv(nn.Module):
-    def __init__(self,hidden_features):
-        super(dwconv, self).__init__()
-        self.depthwise_conv = nn.Sequential(
-            nn.Conv2d(hidden_features, hidden_features, kernel_size=5, stride=1, padding=2, dilation=1,
-                      groups=hidden_features), nn.GELU())
-        self.hidden_features = hidden_features
-    def forward(self,x,x_size):
-        x = x.transpose(1, 2).view(x.shape[0], self.hidden_features, x_size[0], x_size[1]).contiguous()  # b Ph*Pw c
-        x = self.depthwise_conv(x)
-        x = x.flatten(2).transpose(1, 2).contiguous()
-        return x
-class ConvFFN(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.dwconv = dwconv(hidden_features=hidden_features)
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-    def forward(self, x,x_size):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = x + self.dwconv(x,x_size)
-        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 (tuple): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
-    return windows
-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
-        self.qv = DFE(dim, dim)
-        self.proj = nn.Linear(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
-        head_dim = dim // (2*num_heads)
-        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)
-        # 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
-        # generate mother-set
-        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 = window_partition(qv, self.window_size)
-        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 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 = ConvFFN(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-        # 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)
-        _, 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, x_size)))
-        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_SRF(nn.Module, PyTorchModelHubMixin):
-    """ HiT-SRF 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_SRF, 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_SRF(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_
+import numpy as np
+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
+class dwconv(nn.Module):
+    def __init__(self,hidden_features):
+        super(dwconv, self).__init__()
+        self.depthwise_conv = nn.Sequential(
+            nn.Conv2d(hidden_features, hidden_features, kernel_size=5, stride=1, padding=2, dilation=1,
+                      groups=hidden_features), nn.GELU())
+        self.hidden_features = hidden_features
+    def forward(self,x,x_size):
+        x = x.transpose(1, 2).view(x.shape[0], self.hidden_features, x_size[0], x_size[1]).contiguous()  # b Ph*Pw c
+        x = self.depthwise_conv(x)
+        x = x.flatten(2).transpose(1, 2).contiguous()
+        return x
+class ConvFFN(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.dwconv = dwconv(hidden_features=hidden_features)
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+    def forward(self, x,x_size):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = x + self.dwconv(x,x_size)
+        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 (tuple): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
+    return windows
+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
+        self.qv = DFE(dim, dim)
+        self.proj = nn.Linear(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
+        head_dim = dim // (2*num_heads)
+        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)
+        # 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
+        # generate mother-set
+        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 = window_partition(qv, self.window_size)
+        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 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 = ConvFFN(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+        # 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)
+        _, 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, x_size)))
+        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_SRF(nn.Module, PyTorchModelHubMixin):
+    """ HiT-SRF 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_SRF, 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_SRF(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)