app.py

import gradio as gr
import torch
import torch.nn as nn
import numpy as np
from PIL import Image
import math
from einops import rearrange


def to_2tuple(x):
    """Convert input to tuple of length 2."""
    if isinstance(x, (tuple, list)):
        return tuple(x)
    return (x, x)


def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
    """Truncated normal initialization."""
    def norm_cdf(x):
        return (1. + math.erf(x / math.sqrt(2.))) / 2.

    with torch.no_grad():
        l = norm_cdf((a - mean) / std)
        u = norm_cdf((b - mean) / std)
        tensor.uniform_(2 * l - 1, 2 * u - 1)
        tensor.erfinv_()
        tensor.mul_(std * math.sqrt(2.))
        tensor.add_(mean)
        tensor.clamp_(min=a, max=b)
        return tensor


def drop_path(x, drop_prob: float = 0., training: bool = False):
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0], ) + (1, ) * (x.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()
    output = x.div(keep_prob) * random_tensor
    return output


class DropPath(nn.Module):
    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class ChannelAttention(nn.Module):
    def __init__(self, num_feat, squeeze_factor=16):
        super(ChannelAttention, self).__init__()
        self.attention = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0),
            nn.ReLU(inplace=True),
            nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0),
            nn.Sigmoid())

    def forward(self, x):
        y = self.attention(x)
        return x * y


class CAB(nn.Module):
    def __init__(self, num_feat, compress_ratio=3, squeeze_factor=30):
        super(CAB, self).__init__()
        self.cab = nn.Sequential(
            nn.Conv2d(num_feat, num_feat // compress_ratio, 3, 1, 1),
            nn.GELU(),
            nn.Conv2d(num_feat // compress_ratio, num_feat, 3, 1, 1),
            ChannelAttention(num_feat, squeeze_factor)
        )

    def forward(self, x):
        return self.cab(x)


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

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


def window_partition(x, window_size):
    b, h, w, c = x.shape
    x = x.view(b, h // window_size, window_size, w // window_size, window_size, c)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c)
    return windows


def window_reverse(windows, window_size, h, w):
    b = int(windows.shape[0] / (h * w / window_size / window_size))
    x = windows.view(b, h // window_size, w // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1)
    return x


class WindowAttention(nn.Module):
    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
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))

        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, rpi, mask=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]

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

        relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view(
            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
        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


class HAB(nn.Module):
    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 compress_ratio=3, squeeze_factor=30, conv_scale=0.01, mlp_ratio=4.,
                 qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, 'shift_size must in 0-window_size'

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

        self.conv_scale = conv_scale
        self.conv_block = CAB(num_feat=dim, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor)

        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 forward(self, x, x_size, rpi_sa, attn_mask):
        h, w = x_size
        b, _, c = x.shape

        shortcut = x
        x = self.norm1(x)
        x = x.view(b, h, w, c)

        # Conv_X
        conv_x = self.conv_block(x.permute(0, 3, 1, 2))
        conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(b, h * w, c)

        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
            attn_mask = attn_mask
        else:
            shifted_x = x
            attn_mask = None

        # partition windows
        x_windows = window_partition(shifted_x, self.window_size)
        x_windows = x_windows.view(-1, self.window_size * self.window_size, c)

        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, rpi=rpi_sa, mask=attn_mask)

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c)
        shifted_x = window_reverse(attn_windows, self.window_size, h, w)

        # reverse cyclic shift
        if self.shift_size > 0:
            attn_x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
        else:
            attn_x = shifted_x
        attn_x = attn_x.view(b, h * w, c)

        # FFN
        x = shortcut + self.drop_path(attn_x) + conv_x * self.conv_scale
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x


class OCAB(nn.Module):
    def __init__(self, dim, input_resolution, window_size, overlap_ratio, num_heads,
                 qkv_bias=True, qk_scale=None, mlp_ratio=2, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.window_size = window_size
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5
        self.overlap_win_size = int(window_size * overlap_ratio) + window_size

        self.norm1 = norm_layer(dim)
        self.qkv = nn.Linear(dim, dim * 3,  bias=qkv_bias)
        self.unfold = nn.Unfold(kernel_size=(self.overlap_win_size, self.overlap_win_size),
                               stride=window_size, padding=(self.overlap_win_size-window_size)//2)

        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((window_size + self.overlap_win_size - 1) * (window_size + self.overlap_win_size - 1), num_heads))

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

        self.proj = nn.Linear(dim,dim)

        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=nn.GELU)

    def forward(self, x, x_size, rpi):
        h, w = x_size
        b, _, c = x.shape

        shortcut = x
        x = self.norm1(x)
        x = x.view(b, h, w, c)

        qkv = self.qkv(x).reshape(b, h, w, 3, c).permute(3, 0, 4, 1, 2)
        q = qkv[0].permute(0, 2, 3, 1)
        kv = torch.cat((qkv[1], qkv[2]), dim=1)

        # partition windows
        q_windows = window_partition(q, self.window_size)
        q_windows = q_windows.view(-1, self.window_size * self.window_size, c)

        kv_windows = self.unfold(kv)
        kv_windows = rearrange(kv_windows, 'b (nc ch owh oww) nw -> nc (b nw) (owh oww) ch',
                              nc=2, ch=c, owh=self.overlap_win_size, oww=self.overlap_win_size).contiguous()
        k_windows, v_windows = kv_windows[0], kv_windows[1]

        b_, nq, _ = q_windows.shape
        _, n, _ = k_windows.shape
        d = self.dim // self.num_heads
        q = q_windows.reshape(b_, nq, self.num_heads, d).permute(0, 2, 1, 3)
        k = k_windows.reshape(b_, n, self.num_heads, d).permute(0, 2, 1, 3)
        v = v_windows.reshape(b_, n, self.num_heads, d).permute(0, 2, 1, 3)

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

        relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view(
            self.window_size * self.window_size, self.overlap_win_size * self.overlap_win_size, -1)
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
        attn = attn + relative_position_bias.unsqueeze(0)

        attn = self.softmax(attn)
        attn_windows = (attn @ v).transpose(1, 2).reshape(b_, nq, self.dim)

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, self.dim)
        x = window_reverse(attn_windows, self.window_size, h, w)
        x = x.view(b, h * w, self.dim)

        x = self.proj(x) + shortcut
        x = x + self.mlp(self.norm2(x))
        return x


class AttenBlocks(nn.Module):
    def __init__(self, dim, input_resolution, depth, num_heads, window_size, compress_ratio,
                 squeeze_factor, conv_scale, overlap_ratio, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop=0., attn_drop=0., drop_path=0., norm_layer=nn.LayerNorm, downsample=None,
                 use_checkpoint=False):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            HAB(dim=dim, input_resolution=input_resolution, num_heads=num_heads, window_size=window_size,
                shift_size=0 if (i % 2 == 0) else window_size // 2, compress_ratio=compress_ratio,
                squeeze_factor=squeeze_factor, conv_scale=conv_scale, mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop,
                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                norm_layer=norm_layer) for i in range(depth)
        ])

        # OCAB
        self.overlap_attn = OCAB(dim=dim, input_resolution=input_resolution, window_size=window_size,
                                overlap_ratio=overlap_ratio, num_heads=num_heads, qkv_bias=qkv_bias,
                                qk_scale=qk_scale, mlp_ratio=mlp_ratio, norm_layer=norm_layer)

        # 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, params):
        for blk in self.blocks:
            x = blk(x, x_size, params['rpi_sa'], params['attn_mask'])

        x = self.overlap_attn(x, x_size, params['rpi_oca'])

        if self.downsample is not None:
            x = self.downsample(x)
        return x


class RHAG(nn.Module):
    def __init__(self, dim, input_resolution, depth, num_heads, window_size, compress_ratio,
                 squeeze_factor, conv_scale, overlap_ratio, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop=0., attn_drop=0., drop_path=0., norm_layer=nn.LayerNorm, downsample=None,
                 use_checkpoint=False, img_size=224, patch_size=4, resi_connection='1conv'):
        super(RHAG, self).__init__()

        self.dim = dim
        self.input_resolution = input_resolution

        self.residual_group = AttenBlocks(
            dim=dim, input_resolution=input_resolution, depth=depth, num_heads=num_heads,
            window_size=window_size, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor,
            conv_scale=conv_scale, overlap_ratio=overlap_ratio, mlp_ratio=mlp_ratio,
            qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop,
            drop_path=drop_path, norm_layer=norm_layer, downsample=downsample,
            use_checkpoint=use_checkpoint)

        if resi_connection == '1conv':
            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
        elif resi_connection == 'identity':
            self.conv = nn.Identity()

        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, params):
        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size, params), x_size))) + x


class PatchEmbed(nn.Module):
    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)
        if self.norm is not None:
            x = self.norm(x)
        return x


class PatchUnEmbed(nn.Module):
    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):
        x = x.transpose(1, 2).contiguous().view(x.shape[0], self.embed_dim, x_size[0], x_size[1])
        return x


class Upsample(nn.Sequential):
    def __init__(self, scale, num_feat):
        m = []
        if (scale & (scale - 1)) == 0:
            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 HAT(nn.Module):
    def __init__(self, img_size=64, patch_size=1, in_chans=3, embed_dim=96, depths=(6, 6, 6, 6),
                 num_heads=(6, 6, 6, 6), window_size=7, compress_ratio=3, squeeze_factor=30,
                 conv_scale=0.01, overlap_ratio=0.5, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm,
                 ape=False, patch_norm=True, use_checkpoint=False, upscale=2, img_range=1.,
                 upsampler='', resi_connection='1conv', **kwargs):
        super(HAT, self).__init__()

        self.window_size = window_size
        self.shift_size = window_size // 2
        self.overlap_ratio = overlap_ratio

        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

        # relative position index
        relative_position_index_SA = self.calculate_rpi_sa()
        relative_position_index_OCA = self.calculate_rpi_oca()
        self.register_buffer('relative_position_index_SA', relative_position_index_SA)
        self.register_buffer('relative_position_index_OCA', relative_position_index_OCA)

        # shallow feature extraction
        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)

        # 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))]

        # build Residual Hybrid Attention Groups (RHAG)
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = RHAG(
                dim=embed_dim,
                input_resolution=(patches_resolution[0], patches_resolution[1]),
                depth=depths[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size,
                compress_ratio=compress_ratio,
                squeeze_factor=squeeze_factor,
                conv_scale=conv_scale,
                overlap_ratio=overlap_ratio,
                mlp_ratio=self.mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                norm_layer=norm_layer,
                downsample=None,
                use_checkpoint=use_checkpoint,
                img_size=img_size,
                patch_size=patch_size,
                resi_connection=resi_connection)
            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 == 'identity':
            self.conv_after_body = nn.Identity()

        # high quality image reconstruction
        if self.upsampler == 'pixelshuffle':
            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)

        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)

    def calculate_rpi_sa(self):
        coords_h = torch.arange(self.window_size)
        coords_w = torch.arange(self.window_size)
        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.window_size - 1
        relative_coords[:, :, 1] += self.window_size - 1
        relative_coords[:, :, 0] *= 2 * self.window_size - 1
        relative_position_index = relative_coords.sum(-1)
        return relative_position_index

    def calculate_rpi_oca(self):
        window_size_ori = self.window_size
        window_size_ext = self.window_size + int(self.overlap_ratio * self.window_size)

        coords_h = torch.arange(window_size_ori)
        coords_w = torch.arange(window_size_ori)
        coords_ori = torch.stack(torch.meshgrid([coords_h, coords_w]))
        coords_ori_flatten = torch.flatten(coords_ori, 1)

        coords_h = torch.arange(window_size_ext)
        coords_w = torch.arange(window_size_ext)
        coords_ext = torch.stack(torch.meshgrid([coords_h, coords_w]))
        coords_ext_flatten = torch.flatten(coords_ext, 1)

        relative_coords = coords_ext_flatten[:, None, :] - coords_ori_flatten[:, :, None]
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()
        relative_coords[:, :, 0] += window_size_ori - window_size_ext + 1
        relative_coords[:, :, 1] += window_size_ori - window_size_ext + 1
        relative_coords[:, :, 0] *= window_size_ori + window_size_ext - 1
        relative_position_index = relative_coords.sum(-1)
        return relative_position_index

    def calculate_mask(self, x_size):
        h, w = x_size
        img_mask = torch.zeros((1, h, w, 1))
        h_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None))
        w_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None))
        cnt = 0
        for h in h_slices:
            for w in w_slices:
                img_mask[:, h, w, :] = cnt
                cnt += 1

        mask_windows = window_partition(img_mask, self.window_size)
        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        return attn_mask

    @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])

        attn_mask = self.calculate_mask(x_size).to(x.device)
        params = {'attn_mask': attn_mask, 'rpi_sa': self.relative_position_index_SA, 'rpi_oca': self.relative_position_index_OCA}

        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, params)

        x = self.norm(x)
        x = self.patch_unembed(x, x_size)
        return x

    def forward(self, x):
        self.mean = self.mean.type_as(x)
        x = (x - self.mean) * self.img_range

        if self.upsampler == 'pixelshuffle':
            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))

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


# Load the model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = HAT(
    upscale=4,
    in_chans=3,
    img_size=128,
    window_size=16,
    compress_ratio=3,
    squeeze_factor=30,
    conv_scale=0.01,
    overlap_ratio=0.5,
    img_range=1.,
    depths=[6, 6, 6, 6, 6, 6],
    embed_dim=180,
    num_heads=[6, 6, 6, 6, 6, 6],
    mlp_ratio=2,
    upsampler='pixelshuffle',
    resi_connection='1conv'
)

# Load the fine-tuned weights
checkpoint = torch.load('net_g_20000.pth', map_location=device)
if 'params_ema' in checkpoint:
    model.load_state_dict(checkpoint['params_ema'])
elif 'params' in checkpoint:
    model.load_state_dict(checkpoint['params'])
else:
    model.load_state_dict(checkpoint)

model.to(device)
model.eval()


def upscale_image(image):
    # Convert PIL image to tensor
    img_np = np.array(image).astype(np.float32) / 255.0
    img_tensor = torch.from_numpy(img_np).permute(2, 0, 1).unsqueeze(0).to(device)

    # Ensure the image dimensions are multiples of window_size
    h, w = img_tensor.shape[2], img_tensor.shape[3]

    # Pad if necessary
    pad_h = (16 - h % 16) % 16
    pad_w = (16 - w % 16) % 16

    if pad_h > 0 or pad_w > 0:
        img_tensor = torch.nn.functional.pad(img_tensor, (0, pad_w, 0, pad_h), mode='reflect')

    with torch.no_grad():
        output = model(img_tensor)

    # Remove padding if it was added
    if pad_h > 0 or pad_w > 0:
        output = output[:, :, :h*4, :w*4]

    # Convert back to PIL image
    output_np = output.squeeze(0).permute(1, 2, 0).cpu().numpy()
    output_np = np.clip(output_np * 255.0, 0, 255).astype(np.uint8)

    return Image.fromarray(output_np)


# Gradio interface using Blocks for better layout control
def upscale_and_resize_for_display(image):
    # Get the super-resolution output
    upscaled = upscale_image(image)

    # Resize the upscaled image to match input size for display comparison
    display_upscaled = upscaled.resize(image.size, Image.LANCZOS)

    return display_upscaled

# Custom CSS to ensure images display at exactly the same size
css = """
.image-container img {
    max-width: 100% !important;
    height: auto !important;
    object-fit: contain !important;
}
"""

with gr.Blocks(css=css, title="HAT Super-Resolution for Satellite Images") as iface:
    gr.Markdown("# HAT Super-Resolution for Satellite Images")
    gr.Markdown("Upload a satellite image to enhance its resolution by 4x using a fine-tuned HAT model. The output is resized to match the input size for easy comparison of quality improvements.")

    with gr.Row():
        with gr.Column():
            input_image = gr.Image(type="pil", label="Input Satellite Image", elem_classes="image-container")

        with gr.Column():
            output_image = gr.Image(type="pil", label="Enhanced Output (4x Super-Resolution)", elem_classes="image-container")

    submit_btn = gr.Button("Enhance Image", variant="primary")

    submit_btn.click(
        fn=upscale_and_resize_for_display,
        inputs=input_image,
        outputs=output_image
    )

if __name__ == "__main__":
    iface.launch()