# # Source code: https://github.com/IMPLabUniPr/swin2-mose # # ---------------------------------------------------------------------------- # https://arxiv.org/abs/2404.18924 # ---------------------------------------------------------------------------- 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 from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from swin2_mose.libs import window_reverse, Mlp, window_partition from swin2_mose.moe import MoE 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., proj_drop=0., pretrained_window_size=[0, 0], use_lepe=False, use_cpb_bias=True, use_rpe_bias=False): 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) self.use_cpb_bias = use_cpb_bias if self.use_cpb_bias: print('positional encoder: CPB') # 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.use_rpe_bias = use_rpe_bias if self.use_rpe_bias: print('positional encoder: RPE') # 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 rpe_relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww self.register_buffer("rpe_relative_position_index", rpe_relative_position_index) trunc_normal_(self.relative_position_bias_table, std=.02) 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) self.use_lepe = use_lepe if self.use_lepe: print('positional encoder: LEPE') self.get_v = nn.Conv2d( dim, dim, kernel_size=3, stride=1, padding=1, groups=dim) 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) if self.use_lepe: lepe = self.lepe_pos(v) # 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. / 0.01)).to(self.logit_scale.device)).exp() attn = attn * logit_scale if self.use_cpb_bias: 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 self.use_rpe_bias: relative_position_bias = self.relative_position_bias_table[self.rpe_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) if self.use_lepe: x = x + lepe x = x.transpose(1, 2).reshape(B_, N, C) x = self.proj(x) x = self.proj_drop(x) return x def lepe_pos(self, v): B, NH, HW, NW = v.shape C = NH * NW H = W = int(math.sqrt(HW)) v = v.transpose(-2, -1).contiguous().view(B, C, H, W) lepe = self.get_v(v) lepe = lepe.reshape(-1, self.num_heads, NW, HW) lepe = lepe.permute(0, 1, 3, 2).contiguous() return lepe 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., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0, use_lepe=False, use_cpb_bias=True, MoE_config=None, use_rpe_bias=False): 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), use_lepe=use_lepe, use_cpb_bias=use_cpb_bias, use_rpe_bias=use_rpe_bias) 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) if MoE_config is None: print('-->>> MLP') self.mlp = Mlp( in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) else: print('-->>> MOE') print(MoE_config) self.mlp = MoE( input_size=dim, output_size=dim, hidden_size=mlp_hidden_dim, **MoE_config) 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 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 loss_moe = None res = self.mlp(x) if not torch.is_tensor(res): res, loss_moe = res x = x + self.drop_path(self.norm2(res)) return x, loss_moe 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., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, pretrained_window_size=0, use_lepe=False, use_cpb_bias=True, MoE_config=None, use_rpe_bias=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, 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, use_lepe=use_lepe, use_cpb_bias=use_cpb_bias, MoE_config=MoE_config, use_rpe_bias=use_rpe_bias) 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): loss_moe_all = 0 for blk in self.blocks: if self.use_checkpoint: x = checkpoint.checkpoint(blk, x, x_size) else: x = blk(x, x_size) if not torch.is_tensor(x): x, loss_moe = x loss_moe_all += loss_moe or 0 if self.downsample is not None: x = self.downsample(x) return x, loss_moe_all 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., qkv_bias=True, 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', use_lepe=False, use_cpb_bias=True, MoE_config=None, use_rpe_bias=False): 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, use_lepe=use_lepe, use_cpb_bias=use_cpb_bias, MoE_config=MoE_config, use_rpe_bias=use_rpe_bias ) 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): loss_moe = None res = self.residual_group(x, x_size) if not torch.is_tensor(res): res, loss_moe = res res = self.patch_embed(self.conv(self.patch_unembed(res, x_size))) return res + x, loss_moe 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 Swin2MoSE(nn.Module): r""" Swin2-MoSE 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., qkv_bias=True, 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', use_lepe=False, use_cpb_bias=True, MoE_config=None, use_rpe_bias=False, **kwargs): super(Swin2MoSE, 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, 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, use_lepe=use_lepe, use_cpb_bias=use_cpb_bias, MoE_config=MoE_config, use_rpe_bias=use_rpe_bias, ) 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, use_lepe=use_lepe, use_cpb_bias=use_cpb_bias, MoE_config=MoE_config, use_rpe_bias=use_rpe_bias ) 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=.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) loss_moe_all = 0 for layer in self.layers: x = layer(x, x_size) if not torch.is_tensor(x): x, loss_moe = x loss_moe_all += loss_moe or 0 x = self.norm(x) # B L C x = self.patch_unembed(x, x_size) return x, loss_moe_all 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) loss_moe_all = 0 for layer in self.layers_hf: x = layer(x, x_size) if not torch.is_tensor(x): x, loss_moe = x loss_moe_all += loss_moe or 0 x = self.norm(x) # B L C x = self.patch_unembed(x, x_size) return x, loss_moe_all def forward_backbone(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 == 'pixelshuffledirect': # for lightweight SR x = self.conv_first(x) res = self.forward_features(x) if not torch.is_tensor(res): res, loss_moe = res x = self.conv_after_body(res) + x else: raise Exception('not implemented yet') x = x / self.img_range + self.mean 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 loss_moe = 0 if self.upsampler == 'pixelshuffle': # for classical SR x = self.conv_first(x) res = self.forward_features(x) if not torch.is_tensor(res): res, loss_moe = res x = self.conv_after_body(res) + 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) res = self.forward_features(x) if not torch.is_tensor(res): res, loss_moe = res x = self.conv_after_body(res) + 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) res = self.forward_features(x) if not torch.is_tensor(res): res, loss_moe = res x = self.conv_after_body(res) + 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) res_hf = self.forward_features_hf(x_hf) if not torch.is_tensor(res_hf): res_hf, loss_moe_hf = res_hf loss_moe += loss_moe_hf x_hf = self.conv_after_body_hf(res_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) res = self.forward_features(x) if not torch.is_tensor(res): res, loss_moe = res x = self.conv_after_body(res) + x x = self.upsample(x) elif self.upsampler == 'nearest+conv': # for real-world SR x = self.conv_first(x) res = self.forward_features(x) if not torch.is_tensor(res): res, loss_moe = res x = self.conv_after_body(res) + 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.forward_features(x_first) if not torch.is_tensor(res): res, loss_moe = res res = self.conv_after_body(res) + 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, loss_moe 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], loss_moe else: return x[:, :, :H*self.upscale, :W*self.upscale], loss_moe 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