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| # ----------------------------------------------------------------------------------- | |
| # SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 | |
| # Originally Written by Ze Liu, Modified by Jingyun Liang. | |
| # ----------------------------------------------------------------------------------- | |
| 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_ | |
| 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): | |
| """ | |
| 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 | |
| qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set | |
| attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 | |
| proj_drop (float, optional): Dropout ratio of output. Default: 0.0 | |
| """ | |
| def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): | |
| super().__init__() | |
| self.dim = dim | |
| self.window_size = window_size # Wh, Ww | |
| self.num_heads = num_heads | |
| head_dim = dim // num_heads | |
| self.scale = qk_scale or head_dim ** -0.5 | |
| # define a parameter table of relative position bias | |
| self.relative_position_bias_table = nn.Parameter( | |
| torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH | |
| # get pair-wise relative position index for each token inside the window | |
| coords_h = torch.arange(self.window_size[0]) | |
| coords_w = torch.arange(self.window_size[1]) | |
| coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww | |
| coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww | |
| relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww | |
| relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 | |
| relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 | |
| relative_coords[:, :, 1] += self.window_size[1] - 1 | |
| relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 | |
| relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww | |
| self.register_buffer("relative_position_index", relative_position_index) | |
| self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) | |
| self.attn_drop = nn.Dropout(attn_drop) | |
| self.proj = nn.Linear(dim, dim) | |
| self.proj_drop = nn.Dropout(proj_drop) | |
| trunc_normal_(self.relative_position_bias_table, std=.02) | |
| self.softmax = nn.Softmax(dim=-1) | |
| def forward(self, x, mask=None): | |
| """ | |
| Args: | |
| x: input features with shape of (num_windows*B, N, C) | |
| mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None | |
| """ | |
| B_, N, C = x.shape | |
| qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) | |
| q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) | |
| q = q * self.scale | |
| attn = (q @ k.transpose(-2, -1)) | |
| relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( | |
| self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH | |
| relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww | |
| attn = attn + relative_position_bias.unsqueeze(0) | |
| if mask is not None: | |
| nW = mask.shape[0] | |
| attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) | |
| attn = attn.view(-1, self.num_heads, N, N) | |
| attn = self.softmax(attn) | |
| else: | |
| attn = self.softmax(attn) | |
| attn = self.attn_drop(attn) | |
| x = (attn @ v).transpose(1, 2).reshape(B_, N, C) | |
| x = self.proj(x) | |
| x = self.proj_drop(x) | |
| return x | |
| def extra_repr(self) -> str: | |
| return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' | |
| 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 | |
| qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. | |
| 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 | |
| """ | |
| def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, | |
| 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: | |
| # 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, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) | |
| self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() | |
| self.norm2 = norm_layer(dim) | |
| mlp_hidden_dim = int(dim * mlp_ratio) | |
| self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) | |
| 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 = self.norm1(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) | |
| # FFN | |
| x = shortcut + self.drop_path(x) | |
| x = x + self.drop_path(self.mlp(self.norm2(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(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}" | |
| def flops(self): | |
| H, W = self.input_resolution | |
| flops = H * W * self.dim | |
| flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim | |
| 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 | |
| qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. | |
| 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. | |
| """ | |
| def __init__(self, dim, input_resolution, depth, num_heads, window_size, | |
| 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([ | |
| 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, 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)]) | |
| # 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 | |
| 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 | |
| qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. | |
| 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., 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(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, 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 == '3conv': | |
| # to save parameters and memory | |
| self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.GELU(), | |
| nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), | |
| nn.GELU(), | |
| 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 | |
| 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 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 | |
| def flops(self): | |
| flops = 0 | |
| H, W = self.img_size | |
| if self.norm is not None: | |
| flops += H * W * self.embed_dim | |
| 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 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 Generator(nn.Module): | |
| r""" SwinIR | |
| A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. | |
| 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 | |
| qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None | |
| drop_rate (float): Dropout rate. Default: 0 | |
| attn_drop_rate (float): Attention dropout rate. Default: 0 | |
| drop_path_rate (float): Stochastic depth rate. Default: 0.1 | |
| 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., 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(Generator, 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=.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, qk_scale=qk_scale, | |
| 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) | |
| 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.GELU(), | |
| nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), | |
| nn.GELU(), | |
| 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.GELU()) | |
| 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.GELU()) | |
| 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.GELU() | |
| 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) | |
| def no_weight_decay(self): | |
| return {'absolute_pos_embed'} | |
| 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(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 == '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] | |
| 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 | |
| class Discriminator(nn.Module): | |
| def __init__(self): | |
| super(Discriminator, self).__init__() | |
| self.net = nn.Sequential( | |
| nn.Conv2d(3, 64, kernel_size=3, padding=1), | |
| nn.GELU(), | |
| nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1), | |
| nn.BatchNorm2d(64), | |
| nn.GELU(), | |
| nn.Conv2d(64, 128, kernel_size=3, padding=1), | |
| nn.BatchNorm2d(128), | |
| nn.GELU(), | |
| nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1), | |
| nn.BatchNorm2d(128), | |
| nn.GELU(), | |
| nn.Conv2d(128, 256, kernel_size=3, padding=1), | |
| nn.BatchNorm2d(256), | |
| nn.GELU(), | |
| nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1), | |
| nn.BatchNorm2d(256), | |
| nn.GELU(), | |
| nn.Conv2d(256, 512, kernel_size=3, padding=1), | |
| nn.BatchNorm2d(512), | |
| nn.GELU(), | |
| nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1), | |
| nn.BatchNorm2d(512), | |
| nn.GELU(), | |
| nn.AdaptiveAvgPool2d(1), | |
| nn.Conv2d(512, 1024, kernel_size=1), | |
| nn.GELU(), | |
| nn.Conv2d(1024, 1, kernel_size=1) | |
| ) | |
| def forward(self, x): | |
| batch_size = x.size(0) | |
| return torch.sigmoid(self.net(x).view(batch_size)) | |
| if __name__ == '__main__': | |
| upscale = 8 | |
| window_size = 8 | |
| height = (96 // upscale // window_size + 1) * window_size | |
| width = (192 // upscale // window_size + 1) * window_size | |
| model = Generator(upscale=upscale, img_size=(height, width), | |
| window_size=window_size, img_range=1., depths=[6, 6, 6, 6], | |
| embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect') | |
| print(model) | |
| print(height, width, model.flops() / 1e9) | |
| x = torch.randn((1, 3, height, width)) | |
| x = model(x) | |
| print(x.shape) | |