import math import torch import torch.nn as nn from torch.utils.checkpoint import checkpoint import torch.nn.functional as F import collections.abc from itertools import repeat from functools import partial from typing import Any, Optional, Tuple from einops import rearrange # From PyTorch def _ntuple(n): def parse(x): if isinstance(x, collections.abc.Iterable): return x return tuple(repeat(x, n)) return parse to_1tuple = _ntuple(1) to_2tuple = _ntuple(2) to_3tuple = _ntuple(3) to_4tuple = _ntuple(4) to_ntuple = _ntuple def _no_grad_trunc_normal_(tensor, mean, std, a, b): # From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py # Cut & paste from PyTorch official master until it's in a few official releases - RW # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf def norm_cdf(x): # Computes standard normal cumulative distribution function return (1. + math.erf(x / math.sqrt(2.))) / 2. if (mean < a - 2 * std) or (mean > b + 2 * std): warnings.warn( 'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. ' 'The distribution of values may be incorrect.', stacklevel=2) with torch.no_grad(): # Values are generated by using a truncated uniform distribution and # then using the inverse CDF for the normal distribution. # Get upper and lower cdf values low = norm_cdf((a - mean) / std) up = norm_cdf((b - mean) / std) # Uniformly fill tensor with values from [low, up], then translate to # [2l-1, 2u-1]. tensor.uniform_(2 * low - 1, 2 * up - 1) # Use inverse cdf transform for normal distribution to get truncated # standard normal tensor.erfinv_() # Transform to proper mean, std tensor.mul_(std * math.sqrt(2.)) tensor.add_(mean) # Clamp to ensure it's in the proper range tensor.clamp_(min=a, max=b) return tensor def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): r"""Fills the input Tensor with values drawn from a truncated normal distribution. From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \leq \text{mean} \leq b`. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value Examples: >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w) """ return _no_grad_trunc_normal_(tensor, mean, std, a, b) def init_t_xy(end_x: int, end_y: int, zero_center=False): t = torch.arange(end_x * end_y, dtype=torch.float32) t_x = (t % end_x).float() t_y = torch.div(t, end_x, rounding_mode='floor').float() return t_x, t_y def init_random_2d_freqs(head_dim: int, num_heads: int, theta: float = 10.0, rotate: bool = True): freqs_x = [] freqs_y = [] theta = theta mag = 1 / (theta ** (torch.arange(0, head_dim, 4)[: (head_dim // 4)].float() / head_dim)) for i in range(num_heads): angles = torch.rand(1) * 2 * torch.pi if rotate else torch.zeros(1) fx = torch.cat([mag * torch.cos(angles), mag * torch.cos(torch.pi/2 + angles)], dim=-1) fy = torch.cat([mag * torch.sin(angles), mag * torch.sin(torch.pi/2 + angles)], dim=-1) freqs_x.append(fx) freqs_y.append(fy) freqs_x = torch.stack(freqs_x, dim=0) freqs_y = torch.stack(freqs_y, dim=0) freqs = torch.stack([freqs_x, freqs_y], dim=0) return freqs def compute_cis(freqs, t_x, t_y): N = t_x.shape[0] # No float 16 for this range with torch.cuda.amp.autocast(enabled=False): freqs_x = (t_x.unsqueeze(-1) @ freqs[0].unsqueeze(-2)) freqs_y = (t_y.unsqueeze(-1) @ freqs[1].unsqueeze(-2)) freqs_cis = torch.polar(torch.ones_like(freqs_x), freqs_x + freqs_y) return freqs_cis def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): ndim = x.ndim assert 0 <= 1 < ndim # assert freqs_cis.shape == (x.shape[-2], x.shape[-1]) # print(f"freqs_cis shape is {freqs_cis.shape}, x shape is {x.shape}") if freqs_cis.shape == (x.shape[-2], x.shape[-1]): shape = [d if i >= ndim-2 else 1 for i, d in enumerate(x.shape)] elif freqs_cis.shape == (x.shape[-3], x.shape[-2], x.shape[-1]): shape = [d if i >= ndim-3 else 1 for i, d in enumerate(x.shape)] return freqs_cis.view(*shape) def apply_rotary_emb( xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor, ) -> Tuple[torch.Tensor, torch.Tensor]: # print(f"xq shape is {xq.shape}, xq.shape[:-1] is {xq.shape[:-1]}") xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # print(f"xq_ shape is {xq_.shape}") xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) freqs_cis = reshape_for_broadcast(freqs_cis, xq_) xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device) def apply_rotary_emb_single(x, freqs_cis): x_ = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2)) seq_len = x_.shape[2] freqs_cis = freqs_cis[:, :seq_len, :] freqs_cis = freqs_cis.unsqueeze(0).expand_as(x_) x_out = torch.view_as_real(x_ * freqs_cis).flatten(3) return x_out.type_as(x).to(x.device) def drop_path(x, drop_prob: float = 0., training: bool = False): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py """ if drop_prob == 0. or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0], ) + (1, ) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() # binarize output = x.div(keep_prob) * random_tensor return output class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py """ def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, x): return drop_path(x, self.drop_prob, self.training) class ChannelAttention(nn.Module): """Channel attention used in RCAN. Args: num_feat (int): Channel number of intermediate features. squeeze_factor (int): Channel squeeze factor. Default: 16. """ def __init__(self, num_feat, squeeze_factor=16): super(ChannelAttention, self).__init__() self.attention = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0), nn.ReLU(inplace=True), nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), nn.Sigmoid()) def forward(self, x): y = self.attention(x) return x * y class CAB(nn.Module): def __init__(self, num_feat, compress_ratio=3, squeeze_factor=30): super(CAB, self).__init__() self.cab = nn.Sequential( nn.Conv2d(num_feat, num_feat // compress_ratio, 3, 1, 1), nn.GELU(), nn.Conv2d(num_feat // compress_ratio, num_feat, 3, 1, 1), ChannelAttention(num_feat, squeeze_factor) ) def forward(self, x): return self.cab(x) class Mlp(nn.Module): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x def window_partition(x, window_size): """ 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., rope_mixed = True, rope_theta=10.0): super().__init__() self.dim = dim self.window_size = window_size # Wh, Ww self.num_heads = num_heads head_dim = dim // num_heads self.rope_mixed = rope_mixed t_x, t_y = init_t_xy(end_x=self.window_size[1], end_y=self.window_size[0]) self.register_buffer('rope_t_x', t_x) self.register_buffer('rope_t_y', t_y) freqs = init_random_2d_freqs( head_dim=self.dim // self.num_heads, num_heads=self.num_heads, theta=rope_theta, rotate=self.rope_mixed ) if self.rope_mixed: self.rope_freqs = nn.Parameter(freqs, requires_grad=True) else: self.register_buffer('rope_freqs', freqs) freqs_cis = compute_cis(self.rope_freqs, self.rope_t_x, self.rope_t_y) self.rope_freqs_cis = freqs_cis 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) def forward(self, x, rpi, 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).contiguous() q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) ###### Apply rotary position embedding if self.rope_mixed: freqs_cis = compute_cis(self.rope_freqs, self.rope_t_x, self.rope_t_y) else: freqs_cis = self.rope_freqs_cis.to(x.device) q, k = apply_rotary_emb(q, k, freqs_cis) ######### attn = F.scaled_dot_product_attention(q, k, v) attn = attn.transpose(1, 2).reshape(b_, n, c) x = self.proj(attn) x = self.proj_drop(x) return x class HAB(nn.Module): r""" Hybrid Attention Block. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. 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, compress_ratio=3, squeeze_factor=30, conv_scale=0.01, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, rope_mixed = True, rope_theta=10.0): super().__init__() self.dim = dim self.input_resolution = input_resolution self.num_heads = num_heads self.window_size = window_size self.shift_size = shift_size self.mlp_ratio = mlp_ratio if min(self.input_resolution) <= self.window_size: # if window size is larger than input resolution, we don't partition windows self.shift_size = 0 self.window_size = min(self.input_resolution) assert 0 <= self.shift_size < self.window_size, 'shift_size must in 0-window_size' self.norm1 = norm_layer(dim) self.attn = WindowAttention( dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, rope_mixed = rope_mixed, rope_theta=rope_theta) self.conv_scale = conv_scale self.conv_block = CAB(num_feat=dim, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor) self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def forward(self, x, x_size, rpi_sa, attn_mask): h, w = x_size b, _, c = x.shape # assert seq_len == h * w, "input feature has wrong size" shortcut = x x = self.norm1(x) x = x.view(b, h, w, c) # Conv_X conv_x = self.conv_block(x.permute(0, 3, 1, 2).contiguous()) conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(b, h * w, c) # cyclic shift if self.shift_size > 0: shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) attn_mask = attn_mask else: shifted_x = x attn_mask = None # partition windows x_windows = window_partition(shifted_x, self.window_size) # 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 attn_windows = self.attn(x_windows, rpi=rpi_sa, mask=attn_mask) # merge windows attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c) shifted_x = window_reverse(attn_windows, self.window_size, h, w) # b h' w' c # reverse cyclic shift if self.shift_size > 0: attn_x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) else: attn_x = shifted_x attn_x = attn_x.view(b, h * w, c) # FFN x = shortcut + self.drop_path(attn_x) + conv_x * self.conv_scale x = x + self.drop_path(self.mlp(self.norm2(x))) return x class 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, seq_len, c = x.shape assert seq_len == 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 class OCAB(nn.Module): # overlapping cross-attention block def __init__(self, dim, input_resolution, window_size, overlap_ratio, num_heads, qkv_bias=True, qk_scale=None, mlp_ratio=2, norm_layer=nn.LayerNorm, rope_mixed = True, rope_theta = 10.0 ): super().__init__() self.dim = dim self.input_resolution = input_resolution self.window_size = window_size self.num_heads = num_heads head_dim = dim // num_heads self.rope_mixed = rope_mixed self.overlap_win_size = int(window_size * overlap_ratio) + window_size self.norm1 = norm_layer(dim) self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.unfold = nn.Unfold(kernel_size=(self.overlap_win_size, self.overlap_win_size), stride=window_size, padding=(self.overlap_win_size-window_size)//2) t_x, t_y = init_t_xy(end_x=max(self.window_size, self.overlap_win_size), end_y=max(self.window_size, self.overlap_win_size)) self.register_buffer('rope_t_x', t_x) self.register_buffer('rope_t_y', t_y) freqs = init_random_2d_freqs( head_dim=self.dim // self.num_heads, num_heads=self.num_heads, theta=rope_theta, rotate=self.rope_mixed ) if self.rope_mixed: self.rope_freqs = nn.Parameter(freqs, requires_grad=True) else: self.register_buffer('rope_freqs', freqs) freqs_cis = compute_cis(self.rope_freqs, self.rope_t_x, self.rope_t_y) self.rope_freqs_cis = freqs_cis self.proj = nn.Linear(dim,dim) self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=nn.GELU) def forward(self, x, x_size, rpi): h, w = x_size b, _, c = x.shape shortcut = x x = self.norm1(x) x = x.view(b, h, w, c) qkv = self.qkv(x).reshape(b, h, w, 3, c).permute(3, 0, 4, 1, 2).contiguous() # 3, b, c, h, w q = qkv[0].permute(0, 2, 3, 1).contiguous() # b, h, w, c kv = torch.cat((qkv[1], qkv[2]), dim=1) # b, 2*c, h, w # partition windows q_windows = window_partition(q, self.window_size) # nw*b, window_size, window_size, c q_windows = q_windows.view(-1, self.window_size * self.window_size, c) # nw*b, window_size*window_size, c kv_windows = self.unfold(kv) # b, c*w*w, nw kv_windows = rearrange(kv_windows, 'b (nc ch owh oww) nw -> nc (b nw) (owh oww) ch', nc=2, ch=c, owh=self.overlap_win_size, oww=self.overlap_win_size).contiguous() # 2, nw*b, ow*ow, c k_windows, v_windows = kv_windows[0], kv_windows[1] # nw*b, ow*ow, c b_, nq, _ = q_windows.shape _, n, _ = k_windows.shape # print(f"nq is {nq}, n is {n}") d = self.dim // self.num_heads q = q_windows.reshape(b_, nq, self.num_heads, d).permute(0, 2, 1, 3).contiguous() # nw*b, nH, nq, d k = k_windows.reshape(b_, n, self.num_heads, d).permute(0, 2, 1, 3).contiguous() # nw*b, nH, n, d v = v_windows.reshape(b_, n, self.num_heads, d).permute(0, 2, 1, 3).contiguous() # nw*b, nH, n, d ###### Apply rotary position embedding if self.rope_mixed: freqs_cis = compute_cis(self.rope_freqs, self.rope_t_x, self.rope_t_y) else: freqs_cis = self.rope_freqs_cis.to(x.device) q = apply_rotary_emb_single(q, freqs_cis) k = apply_rotary_emb_single(k, freqs_cis) ######### attn = F.scaled_dot_product_attention(q, k, v) attn_windows = attn.transpose(1, 2).reshape(b_, nq, self.dim) # merge windows attn_windows = attn_windows.view(-1, self.window_size, self.window_size, self.dim) x = window_reverse(attn_windows, self.window_size, h, w) # b h w c x = x.view(b, h * w, self.dim) x = self.proj(x) + shortcut x = x + self.mlp(self.norm2(x)) return x class AttenBlocks(nn.Module): """ A series of attention blocks for one RHAG. 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, compress_ratio, squeeze_factor, conv_scale, overlap_ratio, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, rope_mixed = True, rope_theta=10.0): super().__init__() self.dim = dim self.input_resolution = input_resolution self.depth = depth self.use_checkpoint = use_checkpoint # build blocks self.blocks = nn.ModuleList([ HAB( dim=dim, input_resolution=input_resolution, num_heads=num_heads, window_size=window_size, shift_size=0 if (i % 2 == 0) else window_size // 2, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor, conv_scale=conv_scale, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer, rope_mixed = rope_mixed, rope_theta=rope_theta) for i in range(depth) ]) # OCAB self.overlap_attn = OCAB( dim=dim, input_resolution=input_resolution, window_size=window_size, overlap_ratio=overlap_ratio, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, mlp_ratio=mlp_ratio, norm_layer=norm_layer, rope_mixed = rope_mixed, rope_theta = rope_theta) # patch merging layer if downsample is not None: self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) else: self.downsample = None def forward(self, x, x_size, params): for blk in self.blocks: x = blk(x, x_size, params['rpi_sa'], params['attn_mask']) x = self.overlap_attn(x, x_size, params['rpi_oca']) if self.downsample is not None: x = self.downsample(x) return x class RHAG(nn.Module): """Residual Hybrid Attention Group (RHAG). 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, compress_ratio, squeeze_factor, conv_scale, overlap_ratio, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, img_size=224, patch_size=4, resi_connection='1conv', rope_mixed = True, rope_theta=10.0): super(RHAG, self).__init__() self.dim = dim self.input_resolution = input_resolution self.residual_group = AttenBlocks( dim=dim, input_resolution=input_resolution, depth=depth, num_heads=num_heads, window_size=window_size, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor, conv_scale=conv_scale, overlap_ratio=overlap_ratio, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_path, norm_layer=norm_layer, downsample=downsample, use_checkpoint=use_checkpoint, rope_mixed = rope_mixed, rope_theta=rope_theta) if resi_connection == '1conv': self.conv = nn.Conv2d(dim, dim, 3, 1, 1) elif resi_connection == 'identity': self.conv = nn.Identity() self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None) self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None) def forward(self, x, x_size, params): return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size, params), x_size))) + x class PatchEmbed(nn.Module): 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 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): x = x.transpose(1, 2).contiguous().view(x.shape[0], self.embed_dim, x_size[0], x_size[1]) # b Ph*Pw c 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 HATNOUP_ROPE_AMP(nn.Module): def __init__(self, img_size=64, patch_size=1, in_chans=3, embed_dim=192, depths=(6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6), num_heads=(6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6), window_size=16, compress_ratio=3, squeeze_factor=32, conv_scale=0.01, overlap_ratio=0.5, mlp_ratio=2, 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=4, img_range=1., upsampler='pixelshuffle', resi_connection='1conv', rope_mixed = True, rope_theta=10.0, **kwargs): super(HATNOUP_ROPE_AMP, self).__init__() self.window_size = window_size self.shift_size = window_size // 2 self.overlap_ratio = overlap_ratio num_in_ch = in_chans num_out_ch = in_chans num_feat = 64 self.img_range = img_range if in_chans == 3: rgb_mean = (0.4488, 0.4371, 0.4040) self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) else: self.mean = torch.zeros(1, 1, 1, 1) self.upscale = upscale self.upsampler = upsampler # relative position index relative_position_index_SA = self.calculate_rpi_sa() relative_position_index_OCA = self.calculate_rpi_oca() self.register_buffer('relative_position_index_SA', relative_position_index_SA) self.register_buffer('relative_position_index_OCA', relative_position_index_OCA) # ------------------------- 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 Hybrid Attention Groups (RHAG) self.layers = nn.ModuleList() for i_layer in range(self.num_layers): layer = RHAG( dim=embed_dim, input_resolution=(patches_resolution[0], patches_resolution[1]), depth=depths[i_layer], num_heads=num_heads[i_layer], window_size=window_size, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor, conv_scale=conv_scale, overlap_ratio=overlap_ratio, mlp_ratio=self.mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # 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, rope_mixed = rope_mixed, rope_theta=rope_theta) self.layers.append(layer) self.norm = norm_layer(self.num_features) self.use_checkpoint = use_checkpoint # build the last conv layer in deep feature extraction if resi_connection == '1conv': self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) elif resi_connection == 'identity': self.conv_after_body = nn.Identity() # ------------------------- 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) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) def calculate_rpi_sa(self): # calculate relative position index for SA coords_h = torch.arange(self.window_size) coords_w = torch.arange(self.window_size) 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 - 1 # shift to start from 0 relative_coords[:, :, 1] += self.window_size - 1 relative_coords[:, :, 0] *= 2 * self.window_size - 1 relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww return relative_position_index def calculate_rpi_oca(self): # calculate relative position index for OCA window_size_ori = self.window_size window_size_ext = self.window_size + int(self.overlap_ratio * self.window_size) coords_h = torch.arange(window_size_ori) coords_w = torch.arange(window_size_ori) coords_ori = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, ws, ws coords_ori_flatten = torch.flatten(coords_ori, 1) # 2, ws*ws coords_h = torch.arange(window_size_ext) coords_w = torch.arange(window_size_ext) coords_ext = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, wse, wse coords_ext_flatten = torch.flatten(coords_ext, 1) # 2, wse*wse relative_coords = coords_ext_flatten[:, None, :] - coords_ori_flatten[:, :, None] # 2, ws*ws, wse*wse relative_coords = relative_coords.permute(1, 2, 0).contiguous() # ws*ws, wse*wse, 2 relative_coords[:, :, 0] += window_size_ori - window_size_ext + 1 # shift to start from 0 relative_coords[:, :, 1] += window_size_ori - window_size_ext + 1 relative_coords[:, :, 0] *= window_size_ori + window_size_ext - 1 relative_position_index = relative_coords.sum(-1) return relative_position_index def calculate_mask(self, x_size): # 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 @torch.jit.ignore def no_weight_decay(self): return {'absolute_pos_embed'} @torch.jit.ignore def no_weight_decay_keywords(self): return {'relative_position_bias_table'} def forward_features(self, x): x_size = (x.shape[2], x.shape[3]) # Calculate attention mask and relative position index in advance to speed up inference. # The original code is very time-consuming for large window size. attn_mask = self.calculate_mask(x_size).to(x.device) params = {'attn_mask': attn_mask, 'rpi_sa': self.relative_position_index_SA, 'rpi_oca': self.relative_position_index_OCA} x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) for layer in self.layers: x = layer(x, x_size, params) x = self.norm(x) # b seq_len c x = self.patch_unembed(x, x_size) return x def forward(self, x): # self.mean = self.mean.type_as(x) # x = (x - self.mean) * self.img_range if self.upsampler == 'pixelshuffle': # for classical SR x = self.conv_first(x) if self.use_checkpoint: x = self.conv_after_body(checkpoint(self.forward_features, x)) + x else: x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) # x = self.conv_last(self.upsample(x)) # x = x / self.img_range + self.mean return x if __name__ == '__main__': srcs = torch.randn(8, 3, 64, 64).cuda() encoder = HATNOUP_ROPE_AMP(upscale=4, in_chans=3, img_size=64, window_size=16, compress_ratio=3, squeeze_factor=32, conv_scale=0.01, overlap_ratio=0.5, img_range=1., depths=(6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6), embed_dim=192, num_heads=(6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6), mlp_ratio=2, upsampler='pixelshuffle', resi_connection='1conv', use_checkpoint=False).cuda() feature = encoder(srcs) pass