TRISTAN AESCHBACH
add entire Fooocus repo
047c786
# pylint: skip-file
# HAT from https://github.com/XPixelGroup/HAT/blob/main/hat/archs/hat_arch.py
import math
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from .timm.helpers import to_2tuple
from .timm.weight_init import trunc_normal_
def drop_path(x, drop_prob: float = 0.0, training: bool = False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py
"""
if drop_prob == 0.0 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/huggingface/pytorch-image-models/blob/main/timm/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) # type: ignore
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=0.0,
):
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=0.0,
proj_drop=0.0,
):
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim**-0.5
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter( # type: ignore
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
) # 2*Wh-1 * 2*Ww-1, nH
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
trunc_normal_(self.relative_position_bias_table, std=0.02)
self.softmax = nn.Softmax(dim=-1)
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)
)
q, k, v = (
qkv[0],
qkv[1],
qkv[2],
) # make torchscript happy (cannot use tensor as tuple)
q = q * self.scale
attn = q @ k.transpose(-2, -1)
relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view(
self.window_size[0] * self.window_size[1],
self.window_size[0] * self.window_size[1],
-1,
) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(
2, 0, 1
).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None:
nw = mask.shape[0]
attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze(
1
).unsqueeze(0)
attn = attn.view(-1, self.num_heads, n, n)
attn = self.softmax(attn)
else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(b_, n, c)
x = self.proj(x)
x = self.proj_drop(x)
return x
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=4.0,
qkv_bias=True,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
if min(self.input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = 0
self.window_size = min(self.input_resolution)
assert (
0 <= self.shift_size < self.window_size
), "shift_size must in 0-window_size"
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim,
window_size=to_2tuple(self.window_size),
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop,
)
self.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 > 0.0 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))
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,
):
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.scale = qk_scale or head_dim**-0.5
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,
)
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter( # type: ignore
torch.zeros(
(window_size + self.overlap_win_size - 1)
* (window_size + self.overlap_win_size - 1),
num_heads,
)
) # 2*Wh-1 * 2*Ww-1, nH
trunc_normal_(self.relative_position_bias_table, std=0.02)
self.softmax = nn.Softmax(dim=-1)
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) # 3, b, c, h, w
q = qkv[0].permute(0, 2, 3, 1) # 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
# Do the above rearrangement without the rearrange function
# kv_windows = kv_windows.view(
# 2, b, self.overlap_win_size, self.overlap_win_size, c, -1
# )
# kv_windows = kv_windows.permute(0, 5, 1, 2, 3, 4).contiguous()
# kv_windows = kv_windows.view(
# 2, -1, self.overlap_win_size * self.overlap_win_size, 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
d = self.dim // self.num_heads
q = q_windows.reshape(b_, nq, self.num_heads, d).permute(
0, 2, 1, 3
) # nw*b, nH, nq, d
k = k_windows.reshape(b_, n, self.num_heads, d).permute(
0, 2, 1, 3
) # nw*b, nH, n, d
v = v_windows.reshape(b_, n, self.num_heads, d).permute(
0, 2, 1, 3
) # nw*b, nH, n, d
q = q * self.scale
attn = q @ k.transpose(-2, -1)
relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view(
self.window_size * self.window_size,
self.overlap_win_size * self.overlap_win_size,
-1,
) # ws*ws, wse*wse, nH
relative_position_bias = relative_position_bias.permute(
2, 0, 1
).contiguous() # nH, ws*ws, wse*wse
attn = attn + relative_position_bias.unsqueeze(0)
attn = self.softmax(attn)
attn_windows = (attn @ v).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=4.0,
qkv_bias=True,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
norm_layer=nn.LayerNorm,
downsample=None,
use_checkpoint=False,
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.ModuleList(
[
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,
)
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, # type: ignore
norm_layer=norm_layer,
)
# 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=4.0,
qkv_bias=True,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
norm_layer=nn.LayerNorm,
downsample=None,
use_checkpoint=False,
img_size=224,
patch_size=4,
resi_connection="1conv",
):
super(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,
)
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], # type: ignore
img_size[1] // patch_size[1], # type: ignore
]
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], # type: ignore
img_size[1] // patch_size[1], # type: ignore
]
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 HAT(nn.Module):
r"""Hybrid Attention Transformer
A PyTorch implementation of : `Activating More Pixels in Image Super-Resolution Transformer`.
Some codes are based on SwinIR.
Args:
img_size (int | tuple(int)): Input image size. Default 64
patch_size (int | tuple(int)): Patch size. Default: 1
in_chans (int): Number of input image channels. Default: 3
embed_dim (int): Patch embedding dimension. Default: 96
depths (tuple(int)): Depth of each Swin Transformer layer.
num_heads (tuple(int)): Number of attention heads in different layers.
window_size (int): Window size. Default: 7
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
drop_rate (float): Dropout rate. Default: 0
attn_drop_rate (float): Attention dropout rate. Default: 0
drop_path_rate (float): Stochastic depth rate. Default: 0.1
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
patch_norm (bool): If True, add normalization after patch embedding. Default: True
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
img_range: Image range. 1. or 255.
upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
"""
def __init__(
self,
state_dict,
**kwargs,
):
super(HAT, self).__init__()
# Defaults
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
compress_ratio = 3
squeeze_factor = 30
conv_scale = 0.01
overlap_ratio = 0.5
mlp_ratio = 4.0
qkv_bias = True
qk_scale = None
drop_rate = 0.0
attn_drop_rate = 0.0
drop_path_rate = 0.1
norm_layer = nn.LayerNorm
ape = False
patch_norm = True
use_checkpoint = False
upscale = 2
img_range = 1.0
upsampler = ""
resi_connection = "1conv"
self.state = state_dict
self.model_arch = "HAT"
self.sub_type = "SR"
self.supports_fp16 = False
self.support_bf16 = True
self.min_size_restriction = 16
state_keys = list(state_dict.keys())
num_feat = state_dict["conv_last.weight"].shape[1]
in_chans = state_dict["conv_first.weight"].shape[1]
num_out_ch = state_dict["conv_last.weight"].shape[0]
embed_dim = state_dict["conv_first.weight"].shape[0]
if "conv_before_upsample.0.weight" in state_keys:
if "conv_up1.weight" in state_keys:
upsampler = "nearest+conv"
else:
upsampler = "pixelshuffle"
supports_fp16 = False
elif "upsample.0.weight" in state_keys:
upsampler = "pixelshuffledirect"
else:
upsampler = ""
upscale = 1
if upsampler == "nearest+conv":
upsample_keys = [
x for x in state_keys if "conv_up" in x and "bias" not in x
]
for upsample_key in upsample_keys:
upscale *= 2
elif upsampler == "pixelshuffle":
upsample_keys = [
x
for x in state_keys
if "upsample" in x and "conv" not in x and "bias" not in x
]
for upsample_key in upsample_keys:
shape = self.state[upsample_key].shape[0]
upscale *= math.sqrt(shape // num_feat)
upscale = int(upscale)
elif upsampler == "pixelshuffledirect":
upscale = int(
math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch)
)
max_layer_num = 0
max_block_num = 0
for key in state_keys:
result = re.match(
r"layers.(\d*).residual_group.blocks.(\d*).conv_block.cab.0.weight", key
)
if result:
layer_num, block_num = result.groups()
max_layer_num = max(max_layer_num, int(layer_num))
max_block_num = max(max_block_num, int(block_num))
depths = [max_block_num + 1 for _ in range(max_layer_num + 1)]
if (
"layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
in state_keys
):
num_heads_num = self.state[
"layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
].shape[-1]
num_heads = [num_heads_num for _ in range(max_layer_num + 1)]
else:
num_heads = depths
mlp_ratio = float(
self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0]
/ embed_dim
)
# TODO: could actually count the layers, but this should do
if "layers.0.conv.4.weight" in state_keys:
resi_connection = "3conv"
else:
resi_connection = "1conv"
window_size = int(math.sqrt(self.state["relative_position_index_SA"].shape[0]))
# Not sure if this is needed or used at all anywhere in HAT's config
if "layers.0.residual_group.blocks.1.attn_mask" in state_keys:
img_size = int(
math.sqrt(
self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0]
)
* window_size
)
self.window_size = window_size
self.shift_size = window_size // 2
self.overlap_ratio = overlap_ratio
self.in_nc = in_chans
self.out_nc = num_out_ch
self.num_feat = num_feat
self.embed_dim = embed_dim
self.num_heads = num_heads
self.depths = depths
self.window_size = window_size
self.mlp_ratio = mlp_ratio
self.scale = upscale
self.upsampler = upsampler
self.img_size = img_size
self.img_range = img_range
self.resi_connection = resi_connection
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( # type: ignore[arg-type]
torch.zeros(1, num_patches, embed_dim)
)
trunc_normal_(self.absolute_pos_embed, std=0.02)
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth
dpr = [
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
] # stochastic depth decay rule
# build Residual 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]) # type: ignore
], # no impact on SR results
norm_layer=norm_layer,
downsample=None,
use_checkpoint=use_checkpoint,
img_size=img_size,
patch_size=patch_size,
resi_connection=resi_connection,
)
self.layers.append(layer)
self.norm = norm_layer(self.num_features)
# build the last conv layer in deep feature extraction
if resi_connection == "1conv":
self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
elif resi_connection == "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)
self.load_state_dict(self.state, strict=False)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
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 # type: ignore
def no_weight_decay(self):
return {"absolute_pos_embed"}
@torch.jit.ignore # type: 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])
# Calculate attention mask and relative position index in advance to speed up inference.
# The original code is very time-cosuming 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):
H, W = x.shape[2:]
self.mean = self.mean.type_as(x)
x = (x - self.mean) * self.img_range
x = self.check_image_size(x)
if self.upsampler == "pixelshuffle":
# for classical SR
x = self.conv_first(x)
x = self.conv_after_body(self.forward_features(x)) + x
x = self.conv_before_upsample(x)
x = self.conv_last(self.upsample(x))
x = x / self.img_range + self.mean
return x[:, :, : H * self.upscale, : W * self.upscale]