# ----------------------------------------------------------------------------------- # Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/2209.11345 # Written by Conde and Choi et al. # ----------------------------------------------------------------------------------- import os import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as checkpoint import PIL import requests from timm.models.layers import DropPath, to_2tuple, trunc_normal_ class Mlp(nn.Module): def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0, ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x def window_partition(x, window_size): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape 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): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ 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): r"""Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. Args: dim (int): Number of input channels. window_size (tuple[int]): The height and width of the window. num_heads (int): Number of attention heads. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 proj_drop (float, optional): Dropout ratio of output. Default: 0.0 pretrained_window_size (tuple[int]): The height and width of the window in pre-training. """ def __init__( self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0.0, proj_drop=0.0, pretrained_window_size=[0, 0], ): super().__init__() self.dim = dim self.window_size = window_size # Wh, Ww self.pretrained_window_size = pretrained_window_size self.num_heads = num_heads self.logit_scale = nn.Parameter( torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True ) # mlp to generate continuous relative position bias self.cpb_mlp = nn.Sequential( nn.Linear(2, 512, bias=True), nn.ReLU(inplace=True), nn.Linear(512, num_heads, bias=False), ) # get relative_coords_table relative_coords_h = torch.arange( -(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32 ) relative_coords_w = torch.arange( -(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32 ) relative_coords_table = ( torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w])) .permute(1, 2, 0) .contiguous() .unsqueeze(0) ) # 1, 2*Wh-1, 2*Ww-1, 2 if pretrained_window_size[0] > 0: relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1 relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1 else: relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1 relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1 relative_coords_table *= 8 # normalize to -8, 8 relative_coords_table = ( torch.sign(relative_coords_table) * torch.log2(torch.abs(relative_coords_table) + 1.0) / np.log2(8) ) self.register_buffer("relative_coords_table", relative_coords_table) # get pair-wise relative position index for each token inside the window coords_h = torch.arange(self.window_size[0]) coords_w = torch.arange(self.window_size[1]) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww relative_coords = ( coords_flatten[:, :, None] - coords_flatten[:, None, :] ) # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute( 1, 2, 0 ).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 1] += self.window_size[1] - 1 relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww self.register_buffer("relative_position_index", relative_position_index) self.qkv = nn.Linear(dim, dim * 3, bias=False) if qkv_bias: self.q_bias = nn.Parameter(torch.zeros(dim)) self.v_bias = nn.Parameter(torch.zeros(dim)) else: self.q_bias = None self.v_bias = None self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.softmax = nn.Softmax(dim=-1) def forward(self, x, mask=None): """ Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None """ B_, N, C = x.shape qkv_bias = None if self.q_bias is not None: qkv_bias = torch.cat( ( self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias, ) ) qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) q, k, v = ( qkv[0], qkv[1], qkv[2], ) # make torchscript happy (cannot use tensor as tuple) # cosine attention attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) logit_scale = torch.clamp( self.logit_scale, max=torch.log(torch.tensor(1.0 / 0.01)).to(self.logit_scale.device), ).exp() attn = attn * logit_scale relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view( -1, self.num_heads ) relative_position_bias = relative_position_bias_table[ self.relative_position_index.view(-1) ].view( self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1, ) # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.permute( 2, 0, 1 ).contiguous() # nH, Wh*Ww, Wh*Ww relative_position_bias = 16 * torch.sigmoid(relative_position_bias) attn = attn + relative_position_bias.unsqueeze(0) if mask is not None: nW = mask.shape[0] attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( 1 ).unsqueeze(0) attn = attn.view(-1, self.num_heads, N, N) attn = self.softmax(attn) else: attn = self.softmax(attn) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B_, N, C) x = self.proj(x) x = self.proj_drop(x) return x def extra_repr(self) -> str: return ( f"dim={self.dim}, window_size={self.window_size}, " f"pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}" ) def flops(self, N): # calculate flops for 1 window with token length of N flops = 0 # qkv = self.qkv(x) flops += N * self.dim * 3 * self.dim # attn = (q @ k.transpose(-2, -1)) flops += self.num_heads * N * (self.dim // self.num_heads) * N # x = (attn @ v) flops += self.num_heads * N * N * (self.dim // self.num_heads) # x = self.proj(x) flops += N * self.dim * self.dim return flops class SwinTransformerBlock(nn.Module): r"""Swin Transformer Block. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resulotion. num_heads (int): Number of attention heads. window_size (int): Window size. shift_size (int): Shift size for SW-MSA. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. 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 pretrained_window_size (int): Window size in pre-training. """ def __init__( self, dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4.0, qkv_bias=True, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0, ): 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: # if window size is larger than input resolution, we don't partition windows 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, attn_drop=attn_drop, proj_drop=drop, pretrained_window_size=to_2tuple(pretrained_window_size), ) self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp( in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, ) if self.shift_size > 0: attn_mask = self.calculate_mask(self.input_resolution) else: attn_mask = None self.register_buffer("attn_mask", attn_mask) def calculate_mask(self, x_size): # calculate attention mask for SW-MSA H, W = x_size img_mask = torch.zeros((1, H, W, 1)) # 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 ) # nW, window_size, window_size, 1 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 def forward(self, x, x_size): H, W = x_size B, L, C = x.shape # assert L == H * W, "input feature has wrong size" shortcut = x x = x.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) ) else: shifted_x = x # partition windows x_windows = window_partition( shifted_x, self.window_size ) # nW*B, window_size, window_size, C x_windows = x_windows.view( -1, self.window_size * self.window_size, C ) # nW*B, window_size*window_size, C # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size if self.input_resolution == x_size: attn_windows = self.attn( x_windows, mask=self.attn_mask ) # nW*B, window_size*window_size, C else: attn_windows = self.attn( x_windows, mask=self.calculate_mask(x_size).to(x.device) ) # 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) # B H' W' C # reverse cyclic shift if self.shift_size > 0: x = torch.roll( shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) ) else: x = shifted_x x = x.view(B, H * W, C) x = shortcut + self.drop_path(self.norm1(x)) # FFN x = x + self.drop_path(self.norm2(self.mlp(x))) return x def extra_repr(self) -> str: return ( f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" ) def flops(self): flops = 0 H, W = self.input_resolution # norm1 flops += self.dim * H * W # W-MSA/SW-MSA nW = H * W / self.window_size / self.window_size flops += nW * self.attn.flops(self.window_size * self.window_size) # mlp flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio # norm2 flops += self.dim * H * W return flops class PatchMerging(nn.Module): r"""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(2 * 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.reduction(x) x = self.norm(x) return x def extra_repr(self) -> str: return f"input_resolution={self.input_resolution}, dim={self.dim}" def flops(self): H, W = self.input_resolution flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim flops += H * W * self.dim // 2 return flops class BasicLayer(nn.Module): """A basic Swin 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 attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. 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. pretrained_window_size (int): Local window size in pre-training. """ def __init__( self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4.0, qkv_bias=True, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, pretrained_window_size=0, ): super().__init__() self.dim = dim self.input_resolution = input_resolution self.depth = depth self.use_checkpoint = use_checkpoint # build blocks self.blocks = nn.ModuleList( [ SwinTransformerBlock( 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, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop, attn_drop=attn_drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer, pretrained_window_size=pretrained_window_size, ) 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): for blk in self.blocks: if self.use_checkpoint: x = checkpoint.checkpoint(blk, x, x_size) else: x = blk(x, x_size) 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}" def flops(self): flops = 0 for blk in self.blocks: flops += blk.flops() if self.downsample is not None: flops += self.downsample.flops() return flops def _init_respostnorm(self): for blk in self.blocks: nn.init.constant_(blk.norm1.bias, 0) nn.init.constant_(blk.norm1.weight, 0) nn.init.constant_(blk.norm2.bias, 0) nn.init.constant_(blk.norm2.weight, 0) 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 self.proj = nn.Conv2d( in_chans, embed_dim, kernel_size=patch_size, stride=patch_size ) if norm_layer is not None: self.norm = norm_layer(embed_dim) else: self.norm = None def forward(self, x): B, C, H, W = x.shape # FIXME look at relaxing size constraints # assert H == self.img_size[0] and W == self.img_size[1], # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C if self.norm is not None: x = self.norm(x) return x def flops(self): Ho, Wo = self.patches_resolution flops = ( Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) ) if self.norm is not None: flops += Ho * Wo * self.embed_dim return flops class RSTB(nn.Module): """Residual Swin Transformer Block (RSTB). Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. 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. """ def __init__( self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4.0, qkv_bias=True, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, img_size=224, patch_size=4, resi_connection="1conv", ): super(RSTB, 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, window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, 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 == "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=dim, embed_dim=dim, norm_layer=None, ) self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=dim, 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 ) def flops(self): flops = 0 flops += self.residual_group.flops() H, W = self.input_resolution flops += H * W * self.dim * self.dim * 9 flops += self.patch_embed.flops() flops += self.patch_unembed.flops() return flops 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 def flops(self): flops = 0 return flops 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 Upsample_hf(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_hf, self).__init__(*m) class UpsampleOneStep(nn.Sequential): """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) Used in lightweight SR to save parameters. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): self.num_feat = num_feat self.input_resolution = input_resolution m = [] m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) m.append(nn.PixelShuffle(scale)) super(UpsampleOneStep, self).__init__(*m) def flops(self): H, W = self.input_resolution flops = H * W * self.num_feat * 3 * 9 return flops class Swin2SR(nn.Module): r"""Swin2SR A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`. 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 Swin Transformer layer. num_heads (tuple(int)): Number of attention heads in different layers. window_size (int): Window size. Default: 7 mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True drop_rate (float): Dropout rate. Default: 0 attn_drop_rate (float): Attention dropout rate. Default: 0 drop_path_rate (float): Stochastic depth rate. Default: 0.1 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: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction img_range: Image range. 1. or 255. upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None resi_connection: The convolutional block before residual connection. '1conv'/'3conv' """ 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, mlp_ratio=4.0, qkv_bias=True, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, use_checkpoint=False, upscale=2, img_range=1.0, upsampler="", resi_connection="1conv", **kwargs, ): super(Swin2SR, 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.window_size = window_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=0.02) 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 Swin Transformer blocks (RSTB) self.layers = nn.ModuleList() for i_layer in range(self.num_layers): layer = RSTB( 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, mlp_ratio=self.mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_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, ) self.layers.append(layer) if self.upsampler == "pixelshuffle_hf": self.layers_hf = nn.ModuleList() for i_layer in range(self.num_layers): layer = RSTB( 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, mlp_ratio=self.mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_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, ) self.layers_hf.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 == "pixelshuffle_aux": self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) self.conv_before_upsample = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.conv_after_aux = nn.Sequential( nn.Conv2d(3, 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 == "pixelshuffle_hf": 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.upsample_hf = Upsample_hf(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.conv_first_hf = nn.Sequential( nn.Conv2d(num_feat, embed_dim, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) self.conv_before_upsample_hf = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.conv_last_hf = 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=0.02) 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 check_image_size(self, x): _, _, h, w = x.size() mod_pad_h = (self.window_size - h % self.window_size) % self.window_size mod_pad_w = (self.window_size - w % self.window_size) % self.window_size x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") return x 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 forward_features_hf(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_hf: x = layer(x, x_size) x = self.norm(x) # B L C x = self.patch_unembed(x, x_size) return x def forward(self, x): H, W = x.shape[2:] x = self.check_image_size(x) 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 == "pixelshuffle_aux": bicubic = F.interpolate( x, size=(H * self.upscale, W * self.upscale), mode="bicubic", align_corners=False, ) bicubic = self.conv_bicubic(bicubic) x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) aux = self.conv_aux(x) # b, 3, LR_H, LR_W x = self.conv_after_aux(aux) x = ( self.upsample(x)[:, :, : H * self.upscale, : W * self.upscale] + bicubic[:, :, : H * self.upscale, : W * self.upscale] ) x = self.conv_last(x) aux = aux / self.img_range + self.mean elif self.upsampler == "pixelshuffle_hf": # for classical SR with HF x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x_before = self.conv_before_upsample(x) x_out = self.conv_last(self.upsample(x_before)) x_hf = self.conv_first_hf(x_before) x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf x_hf = self.conv_before_upsample_hf(x_hf) x_hf = self.conv_last_hf(self.upsample_hf(x_hf)) x = x_out + x_hf x_hf = x_hf / self.img_range + self.mean 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 if self.upsampler == "pixelshuffle_aux": return x[:, :, : H * self.upscale, : W * self.upscale], aux elif self.upsampler == "pixelshuffle_hf": x_out = x_out / self.img_range + self.mean return ( x_out[:, :, : H * self.upscale, : W * self.upscale], x[:, :, : H * self.upscale, : W * self.upscale], x_hf[:, :, : H * self.upscale, : W * self.upscale], ) else: return x[:, :, : H * self.upscale, : W * self.upscale] def flops(self): flops = 0 H, W = self.patches_resolution flops += H * W * 3 * self.embed_dim * 9 flops += self.patch_embed.flops() for i, layer in enumerate(self.layers): flops += layer.flops() flops += H * W * 3 * self.embed_dim * self.embed_dim flops += self.upsample.flops() return flops MODEL_PATH = "model_zoo/swin2sr/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth" PARAM_KEY_G = "params_ema" SCALE = 4 WINDOW_SIZE = 8 def download_model_weights() -> None: os.makedirs(os.path.dirname(MODEL_PATH), exist_ok=True) url = "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/{}".format( os.path.basename(MODEL_PATH) ) r = requests.get(url, allow_redirects=True) with open(MODEL_PATH, "wb") as f: f.write(r.content) def load_model() -> torch.nn.Module: if not os.path.exists(MODEL_PATH): download_model_weights() model = Swin2SR( upscale=SCALE, in_chans=3, img_size=64, window_size=8, img_range=1.0, depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6], mlp_ratio=2, upsampler="nearest+conv", resi_connection="1conv", ) pretrained_model = torch.load(MODEL_PATH) model.load_state_dict( pretrained_model[PARAM_KEY_G] if PARAM_KEY_G in pretrained_model.keys() else pretrained_model, strict=True, ) return model def preprocesss_image(image: PIL.Image.Image) -> torch.FloatTensor: image = np.array(image).astype("float32") / 255.0 image = np.transpose(image, (2, 0, 1)) # HWC -> CHW img_lq = torch.from_numpy(image).float().unsqueeze(0) _, _, h_old, w_old = img_lq.size() h_pad = (h_old // WINDOW_SIZE + 1) * WINDOW_SIZE - h_old w_pad = (w_old // WINDOW_SIZE + 1) * WINDOW_SIZE - w_old img_lq = torch.cat([img_lq, torch.flip(img_lq, [2])], 2)[:, :, : h_old + h_pad, :] img_lq = torch.cat([img_lq, torch.flip(img_lq, [3])], 3)[:, :, :, : w_old + w_pad] return img_lq def postprocess_image(output: torch.Tensor) -> PIL.Image.Image: output = output.data.float().cpu().clamp_(0, 1).numpy() output = (output * 255).round().astype("uint8") output = output.transpose(1, 2, 0) return PIL.Image.fromarray(output)