''' DAT network from https://github.com/zhengchen1999/DAT (https://openaccess.thecvf.com/content/ICCV2023/papers/Chen_Dual_Aggregation_Transformer_for_Image_Super-Resolution_ICCV_2023_paper.pdf) ''' import torch import torch.nn as nn import torch.utils.checkpoint as checkpoint from torch import Tensor from torch.nn import functional as F from timm.models.layers import DropPath, trunc_normal_ from einops.layers.torch import Rearrange from einops import rearrange import math import numpy as np def img2windows(img, H_sp, W_sp): """ Input: Image (B, C, H, W) Output: Window Partition (B', N, C) """ B, C, H, W = img.shape img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp) img_perm = img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp* W_sp, C) return img_perm def windows2img(img_splits_hw, H_sp, W_sp, H, W): """ Input: Window Partition (B', N, C) Output: Image (B, H, W, C) """ B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp)) img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1) img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return img class SpatialGate(nn.Module): """ Spatial-Gate. Args: dim (int): Half of input channels. """ def __init__(self, dim): super().__init__() self.norm = nn.LayerNorm(dim) self.conv = nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim) # DW Conv def forward(self, x, H, W): # Split x1, x2 = x.chunk(2, dim = -1) B, N, C = x.shape x2 = self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C//2, H, W)).flatten(2).transpose(-1, -2).contiguous() return x1 * x2 class SGFN(nn.Module): """ Spatial-Gate Feed-Forward Network. Args: in_features (int): Number of input channels. hidden_features (int | None): Number of hidden channels. Default: None out_features (int | None): Number of output channels. Default: None act_layer (nn.Module): Activation layer. Default: nn.GELU drop (float): Dropout rate. Default: 0.0 """ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.sg = SpatialGate(hidden_features//2) self.fc2 = nn.Linear(hidden_features//2, out_features) self.drop = nn.Dropout(drop) def forward(self, x, H, W): """ Input: x: (B, H*W, C), H, W Output: x: (B, H*W, C) """ x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.sg(x, H, W) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class DynamicPosBias(nn.Module): # The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py """ Dynamic Relative Position Bias. Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. residual (bool): If True, use residual strage to connect conv. """ def __init__(self, dim, num_heads, residual): super().__init__() self.residual = residual self.num_heads = num_heads self.pos_dim = dim // 4 self.pos_proj = nn.Linear(2, self.pos_dim) self.pos1 = nn.Sequential( nn.LayerNorm(self.pos_dim), nn.ReLU(inplace=True), nn.Linear(self.pos_dim, self.pos_dim), ) self.pos2 = nn.Sequential( nn.LayerNorm(self.pos_dim), nn.ReLU(inplace=True), nn.Linear(self.pos_dim, self.pos_dim) ) self.pos3 = nn.Sequential( nn.LayerNorm(self.pos_dim), nn.ReLU(inplace=True), nn.Linear(self.pos_dim, self.num_heads) ) def forward(self, biases): if self.residual: pos = self.pos_proj(biases) # 2Gh-1 * 2Gw-1, heads pos = pos + self.pos1(pos) pos = pos + self.pos2(pos) pos = self.pos3(pos) else: pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases)))) return pos class Spatial_Attention(nn.Module): """ Spatial Window Self-Attention. It supports rectangle window (containing square window). Args: dim (int): Number of input channels. idx (int): The indentix of window. (0/1) split_size (tuple(int)): Height and Width of spatial window. dim_out (int | None): The dimension of the attention output. Default: None num_heads (int): Number of attention heads. Default: 6 attn_drop (float): Dropout ratio of attention weight. Default: 0.0 proj_drop (float): Dropout ratio of output. Default: 0.0 qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set position_bias (bool): The dynamic relative position bias. Default: True """ def __init__(self, dim, idx, split_size=[8,8], dim_out=None, num_heads=6, attn_drop=0., proj_drop=0., qk_scale=None, position_bias=True): super().__init__() self.dim = dim self.dim_out = dim_out or dim self.split_size = split_size self.num_heads = num_heads self.idx = idx self.position_bias = position_bias head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 if idx == 0: H_sp, W_sp = self.split_size[0], self.split_size[1] elif idx == 1: W_sp, H_sp = self.split_size[0], self.split_size[1] else: print ("ERROR MODE", idx) exit(0) self.H_sp = H_sp self.W_sp = W_sp if self.position_bias: self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False) # generate mother-set position_bias_h = torch.arange(1 - self.H_sp, self.H_sp) position_bias_w = torch.arange(1 - self.W_sp, self.W_sp) biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w])) biases = biases.flatten(1).transpose(0, 1).contiguous().float() self.register_buffer('rpe_biases', biases) # get pair-wise relative position index for each token inside the window coords_h = torch.arange(self.H_sp) coords_w = torch.arange(self.W_sp) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) coords_flatten = torch.flatten(coords, 1) relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] relative_coords = relative_coords.permute(1, 2, 0).contiguous() relative_coords[:, :, 0] += self.H_sp - 1 relative_coords[:, :, 1] += self.W_sp - 1 relative_coords[:, :, 0] *= 2 * self.W_sp - 1 relative_position_index = relative_coords.sum(-1) self.register_buffer('relative_position_index', relative_position_index) self.attn_drop = nn.Dropout(attn_drop) def im2win(self, x, H, W): B, N, C = x.shape x = x.transpose(-2,-1).contiguous().view(B, C, H, W) x = img2windows(x, self.H_sp, self.W_sp) x = x.reshape(-1, self.H_sp* self.W_sp, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3).contiguous() return x def forward(self, qkv, H, W, mask=None): """ Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size Output: x (B, H, W, C) """ q,k,v = qkv[0], qkv[1], qkv[2] B, L, C = q.shape assert L == H * W, "flatten img_tokens has wrong size" # partition the q,k,v, image to window q = self.im2win(q, H, W) k = self.im2win(k, H, W) v = self.im2win(v, H, W) q = q * self.scale attn = (q @ k.transpose(-2, -1)) # B head N C @ B head C N --> B head N N # calculate drpe if self.position_bias: pos = self.pos(self.rpe_biases) # select position bias relative_position_bias = pos[self.relative_position_index.view(-1)].view( self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1) relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() attn = attn + relative_position_bias.unsqueeze(0) N = attn.shape[3] # use mask for shift window if mask is not None: nW = mask.shape[0] attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) attn = attn.view(-1, self.num_heads, N, N) attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype) attn = self.attn_drop(attn) x = (attn @ v) x = x.transpose(1, 2).reshape(-1, self.H_sp* self.W_sp, C) # B head N N @ B head N C # merge the window, window to image x = windows2img(x, self.H_sp, self.W_sp, H, W) # B H' W' C return x class Adaptive_Spatial_Attention(nn.Module): # The implementation builds on CAT code https://github.com/Zhengchen1999/CAT """ Adaptive Spatial Self-Attention Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. Default: 6 split_size (tuple(int)): Height and Width of spatial window. shift_size (tuple(int)): Shift size for spatial window. qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. drop (float): Dropout rate. Default: 0.0 attn_drop (float): Attention dropout rate. Default: 0.0 rg_idx (int): The indentix of Residual Group (RG) b_idx (int): The indentix of Block in each RG """ def __init__(self, dim, num_heads, reso=64, split_size=[8,8], shift_size=[1,2], qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., rg_idx=0, b_idx=0): super().__init__() self.dim = dim self.num_heads = num_heads self.split_size = split_size self.shift_size = shift_size self.b_idx = b_idx self.rg_idx = rg_idx self.patches_resolution = reso self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) assert 0 <= self.shift_size[0] < self.split_size[0], "shift_size must in 0-split_size0" assert 0 <= self.shift_size[1] < self.split_size[1], "shift_size must in 0-split_size1" self.branch_num = 2 self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(drop) self.attns = nn.ModuleList([ Spatial_Attention( dim//2, idx = i, split_size=split_size, num_heads=num_heads//2, dim_out=dim//2, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, position_bias=True) for i in range(self.branch_num)]) if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or (self.rg_idx % 2 != 0 and self.b_idx % 4 == 0): attn_mask = self.calculate_mask(self.patches_resolution, self.patches_resolution) self.register_buffer("attn_mask_0", attn_mask[0]) self.register_buffer("attn_mask_1", attn_mask[1]) else: attn_mask = None self.register_buffer("attn_mask_0", None) self.register_buffer("attn_mask_1", None) self.dwconv = nn.Sequential( nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1,groups=dim), nn.BatchNorm2d(dim), nn.GELU() ) self.channel_interaction = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(dim, dim // 8, kernel_size=1), nn.BatchNorm2d(dim // 8), nn.GELU(), nn.Conv2d(dim // 8, dim, kernel_size=1), ) self.spatial_interaction = nn.Sequential( nn.Conv2d(dim, dim // 16, kernel_size=1), nn.BatchNorm2d(dim // 16), nn.GELU(), nn.Conv2d(dim // 16, 1, kernel_size=1) ) def calculate_mask(self, H, W): # The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py # calculate attention mask for shift window img_mask_0 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=0 img_mask_1 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=1 h_slices_0 = (slice(0, -self.split_size[0]), slice(-self.split_size[0], -self.shift_size[0]), slice(-self.shift_size[0], None)) w_slices_0 = (slice(0, -self.split_size[1]), slice(-self.split_size[1], -self.shift_size[1]), slice(-self.shift_size[1], None)) h_slices_1 = (slice(0, -self.split_size[1]), slice(-self.split_size[1], -self.shift_size[1]), slice(-self.shift_size[1], None)) w_slices_1 = (slice(0, -self.split_size[0]), slice(-self.split_size[0], -self.shift_size[0]), slice(-self.shift_size[0], None)) cnt = 0 for h in h_slices_0: for w in w_slices_0: img_mask_0[:, h, w, :] = cnt cnt += 1 cnt = 0 for h in h_slices_1: for w in w_slices_1: img_mask_1[:, h, w, :] = cnt cnt += 1 # calculate mask for window-0 img_mask_0 = img_mask_0.view(1, H // self.split_size[0], self.split_size[0], W // self.split_size[1], self.split_size[1], 1) img_mask_0 = img_mask_0.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, self.split_size[0], self.split_size[1], 1) # nW, sw[0], sw[1], 1 mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1]) attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2) attn_mask_0 = attn_mask_0.masked_fill(attn_mask_0 != 0, float(-100.0)).masked_fill(attn_mask_0 == 0, float(0.0)) # calculate mask for window-1 img_mask_1 = img_mask_1.view(1, H // self.split_size[1], self.split_size[1], W // self.split_size[0], self.split_size[0], 1) img_mask_1 = img_mask_1.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, self.split_size[1], self.split_size[0], 1) # nW, sw[1], sw[0], 1 mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0]) attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2) attn_mask_1 = attn_mask_1.masked_fill(attn_mask_1 != 0, float(-100.0)).masked_fill(attn_mask_1 == 0, float(0.0)) return attn_mask_0, attn_mask_1 def forward(self, x, H, W): """ Input: x: (B, H*W, C), H, W Output: x: (B, H*W, C) """ B, L, C = x.shape assert L == H * W, "flatten img_tokens has wrong size" qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3) # 3, B, HW, C # V without partition v = qkv[2].transpose(-2,-1).contiguous().view(B, C, H, W) # image padding max_split_size = max(self.split_size[0], self.split_size[1]) pad_l = pad_t = 0 pad_r = (max_split_size - W % max_split_size) % max_split_size pad_b = (max_split_size - H % max_split_size) % max_split_size qkv = qkv.reshape(3*B, H, W, C).permute(0, 3, 1, 2) # 3B C H W qkv = F.pad(qkv, (pad_l, pad_r, pad_t, pad_b)).reshape(3, B, C, -1).transpose(-2, -1) # l r t b _H = pad_b + H _W = pad_r + W _L = _H * _W # window-0 and window-1 on split channels [C/2, C/2]; for square windows (e.g., 8x8), window-0 and window-1 can be merged # shift in block: (0, 4, 8, ...), (2, 6, 10, ...), (0, 4, 8, ...), (2, 6, 10, ...), ... if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or (self.rg_idx % 2 != 0 and self.b_idx % 4 == 0): qkv = qkv.view(3, B, _H, _W, C) qkv_0 = torch.roll(qkv[:,:,:,:,:C//2], shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(2, 3)) qkv_0 = qkv_0.view(3, B, _L, C//2) qkv_1 = torch.roll(qkv[:,:,:,:,C//2:], shifts=(-self.shift_size[1], -self.shift_size[0]), dims=(2, 3)) qkv_1 = qkv_1.view(3, B, _L, C//2) if self.patches_resolution != _H or self.patches_resolution != _W: mask_tmp = self.calculate_mask(_H, _W) x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device)) x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device)) else: x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0) x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1) x1 = torch.roll(x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2)) x2 = torch.roll(x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2)) x1 = x1[:, :H, :W, :].reshape(B, L, C//2) x2 = x2[:, :H, :W, :].reshape(B, L, C//2) # attention output attened_x = torch.cat([x1,x2], dim=2) else: x1 = self.attns[0](qkv[:,:,:,:C//2], _H, _W)[:, :H, :W, :].reshape(B, L, C//2) x2 = self.attns[1](qkv[:,:,:,C//2:], _H, _W)[:, :H, :W, :].reshape(B, L, C//2) # attention output attened_x = torch.cat([x1,x2], dim=2) # convolution output conv_x = self.dwconv(v) # Adaptive Interaction Module (AIM) # C-Map (before sigmoid) channel_map = self.channel_interaction(conv_x).permute(0, 2, 3, 1).contiguous().view(B, 1, C) # S-Map (before sigmoid) attention_reshape = attened_x.transpose(-2,-1).contiguous().view(B, C, H, W) spatial_map = self.spatial_interaction(attention_reshape) # C-I attened_x = attened_x * torch.sigmoid(channel_map) # S-I conv_x = torch.sigmoid(spatial_map) * conv_x conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, L, C) x = attened_x + conv_x x = self.proj(x) x = self.proj_drop(x) return x class Adaptive_Channel_Attention(nn.Module): # The implementation builds on XCiT code https://github.com/facebookresearch/xcit """ Adaptive Channel Self-Attention Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. Default: 6 qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. attn_drop (float): Attention dropout rate. Default: 0.0 drop_path (float): Stochastic depth rate. Default: 0.0 """ def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1)) 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) self.dwconv = nn.Sequential( nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1,groups=dim), nn.BatchNorm2d(dim), nn.GELU() ) self.channel_interaction = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(dim, dim // 8, kernel_size=1), nn.BatchNorm2d(dim // 8), nn.GELU(), nn.Conv2d(dim // 8, dim, kernel_size=1), ) self.spatial_interaction = nn.Sequential( nn.Conv2d(dim, dim // 16, kernel_size=1), nn.BatchNorm2d(dim // 16), nn.GELU(), nn.Conv2d(dim // 16, 1, kernel_size=1) ) def forward(self, x, H, W): """ Input: x: (B, H*W, C), H, W Output: x: (B, H*W, C) """ B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) qkv = qkv.permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] q = q.transpose(-2, -1) k = k.transpose(-2, -1) v = v.transpose(-2, -1) v_ = v.reshape(B, C, N).contiguous().view(B, C, H, W) q = torch.nn.functional.normalize(q, dim=-1) k = torch.nn.functional.normalize(k, dim=-1) attn = (q @ k.transpose(-2, -1)) * self.temperature attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) # attention output attened_x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C) # convolution output conv_x = self.dwconv(v_) # Adaptive Interaction Module (AIM) # C-Map (before sigmoid) attention_reshape = attened_x.transpose(-2,-1).contiguous().view(B, C, H, W) channel_map = self.channel_interaction(attention_reshape) # S-Map (before sigmoid) spatial_map = self.spatial_interaction(conv_x).permute(0, 2, 3, 1).contiguous().view(B, N, 1) # S-I attened_x = attened_x * torch.sigmoid(spatial_map) # C-I conv_x = conv_x * torch.sigmoid(channel_map) conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, N, C) x = attened_x + conv_x x = self.proj(x) x = self.proj_drop(x) return x class DATB(nn.Module): def __init__(self, dim, num_heads, reso=64, split_size=[2,4],shift_size=[1,2], expansion_factor=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, rg_idx=0, b_idx=0): super().__init__() self.norm1 = norm_layer(dim) if b_idx % 2 == 0: # DSTB self.attn = Adaptive_Spatial_Attention( dim, num_heads=num_heads, reso=reso, split_size=split_size, shift_size=shift_size, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, rg_idx=rg_idx, b_idx=b_idx ) else: # DCTB self.attn = Adaptive_Channel_Attention( dim, 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() ffn_hidden_dim = int(dim * expansion_factor) self.ffn = SGFN(in_features=dim, hidden_features=ffn_hidden_dim, out_features=dim, act_layer=act_layer) self.norm2 = norm_layer(dim) def forward(self, x, x_size): """ Input: x: (B, H*W, C), x_size: (H, W) Output: x: (B, H*W, C) """ H , W = x_size x = x + self.drop_path(self.attn(self.norm1(x), H, W)) x = x + self.drop_path(self.ffn(self.norm2(x), H, W)) return x class ResidualGroup(nn.Module): """ ResidualGroup Args: dim (int): Number of input channels. reso (int): Input resolution. num_heads (int): Number of attention heads. split_size (tuple(int)): Height and Width of spatial window. expansion_factor (float): Ratio of ffn hidden dim to embedding dim. qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None drop (float): Dropout rate. Default: 0 attn_drop(float): Attention dropout rate. Default: 0 drop_paths (float | None): Stochastic depth rate. act_layer (nn.Module): Activation layer. Default: nn.GELU norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm depth (int): Number of dual aggregation Transformer blocks in residual group. use_chk (bool): Whether to use checkpointing to save memory. resi_connection: The convolutional block before residual connection. '1conv'/'3conv' """ def __init__( self, dim, reso, num_heads, split_size=[2,4], expansion_factor=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_paths=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm, depth=2, use_chk=False, resi_connection='1conv', rg_idx=0): super().__init__() self.use_chk = use_chk self.reso = reso self.blocks = nn.ModuleList([ DATB( dim=dim, num_heads=num_heads, reso = reso, split_size = split_size, shift_size = [split_size[0]//2, split_size[1]//2], expansion_factor=expansion_factor, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_paths[i], act_layer=act_layer, norm_layer=norm_layer, rg_idx = rg_idx, b_idx = i, )for i in range(depth)]) if resi_connection == '1conv': self.conv = nn.Conv2d(dim, dim, 3, 1, 1) elif resi_connection == '3conv': 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)) def forward(self, x, x_size): """ Input: x: (B, H*W, C), x_size: (H, W) Output: x: (B, H*W, C) """ H, W = x_size res = x for blk in self.blocks: if self.use_chk: x = checkpoint.checkpoint(blk, x, x_size) else: x = blk(x, x_size) x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) x = self.conv(x) x = rearrange(x, "b c h w -> b (h w) c") x = res + x return x class Upsample(nn.Sequential): """Upsample module. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat): m = [] if (scale & (scale - 1)) == 0: # scale = 2^n for _ in range(int(math.log(scale, 2))): m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(2)) elif scale == 3: m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(3)) else: raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') super(Upsample, self).__init__(*m) class UpsampleOneStep(nn.Sequential): """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) Used in lightweight SR to save parameters. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): self.num_feat = num_feat self.input_resolution = input_resolution m = [] m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) m.append(nn.PixelShuffle(scale)) super(UpsampleOneStep, self).__init__(*m) def flops(self): h, w = self.input_resolution flops = h * w * self.num_feat * 3 * 9 return flops class DAT(nn.Module): """ Dual Aggregation Transformer Args: img_size (int): Input image size. Default: 64 in_chans (int): Number of input image channels. Default: 3 embed_dim (int): Patch embedding dimension. Default: 180 depths (tuple(int)): Depth of each residual group (number of DATB in each RG). split_size (tuple(int)): Height and Width of spatial window. num_heads (tuple(int)): Number of attention heads in different residual groups. expansion_factor (float): Ratio of ffn 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 | None): 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 act_layer (nn.Module): Activation layer. Default: nn.GELU norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm use_chk (bool): Whether to use checkpointing to save memory. upscale: Upscale factor. 2/3/4 for image SR img_range: Image range. 1. or 255. resi_connection: The convolutional block before residual connection. '1conv'/'3conv' """ def __init__(self, img_size=64, in_chans=3, embed_dim=180, split_size=[2,4], depth=[2,2,2,2], num_heads=[2,2,2,2], expansion_factor=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_chk=False, upscale=2, img_range=1., resi_connection='1conv', upsampler='pixelshuffle', **kwargs): super().__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 # ------------------------- 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(depth) self.use_chk = use_chk self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models heads=num_heads self.before_RG = nn.Sequential( Rearrange('b c h w -> b (h w) c'), nn.LayerNorm(embed_dim) ) curr_dim = embed_dim dpr = [x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth))] # stochastic depth decay rule self.layers = nn.ModuleList() for i in range(self.num_layers): layer = ResidualGroup( dim=embed_dim, num_heads=heads[i], reso=img_size, split_size=split_size, expansion_factor=expansion_factor, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_paths=dpr[sum(depth[:i]):sum(depth[:i + 1])], act_layer=act_layer, norm_layer=norm_layer, depth=depth[i], use_chk=use_chk, resi_connection=resi_connection, rg_idx=i) self.layers.append(layer) self.norm = norm_layer(curr_dim) # 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, Reconstruction ------------------------- # if self.upsampler == 'pixelshuffle': # for classical SR self.conv_before_upsample = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.upsample = Upsample(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) elif self.upsampler == 'pixelshuffledirect': # for lightweight SR (to save parameters) self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, (img_size, img_size)) 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.BatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d)): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) def forward_features(self, x): _, _, H, W = x.shape x_size = [H, W] x = self.before_RG(x) for layer in self.layers: x = layer(x, x_size) x = self.norm(x) x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) return x def forward(self, x): """ Input: x: (B, C, H, W) """ self.mean = self.mean.type_as(x) x = (x - self.mean) * self.img_range if self.upsampler == 'pixelshuffle': # for image 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) x = x / self.img_range + self.mean return x if __name__ == '__main__': upscale = 1 height = 64 width = 64 model = DAT(upscale=4, in_chans=3, img_size=64, img_range=1., depth=[18], embed_dim=60, num_heads=[6], expansion_factor=2, resi_connection='3conv', split_size=[8,32], upsampler='pixelshuffledirect', ).cuda().eval() print(height, width) x = torch.randn((1, 3, height, width)).cuda() x = model(x) print(x.shape)