目录
- 一、HAttention注意力机制
- 1.1HAttention注意力介绍
- 1.2HAT核心代码
- 二、添加HAT注意力机制
- 2.1STEP1
- 2.2STEP2
- 2.3STEP3
- 2.4STEP4
- 三、yaml文件与运行
- 3.1yaml文件
- 3.2运行成功截图
一、HAttention注意力机制
1.1HAttention注意力介绍
HAT模型 通过结合卷积特征提取与多尺度注意力机制，具备了强大的图像重建能力。它的优势在于能有效整合局部和全局信息，并通过残差连接和通道注意力等方式提高网络的表达能力和重建质量，适用于图像超分辨率和图像重建任务。
下面是HAT的工作流程和主要模块的作用
- 浅层特征提取 (Shallow Feature Extraction)
输入图像首先经过卷积操作提取低级特征。该过程用来捕捉图像的基础信息，如边缘、颜色等，形成初步的特征图。 - 深层特征提取 (Deep Feature Extraction)
浅层特征通过多个RHAG模块进行深度特征提取。RHAG由多个HAB（混合注意力块）和OCAB（重叠交叉注意力块）组成：
HAB：包含 CAB (Channel Attention Block) 和 (S)W-MSA (Shifted Window Multi-Head Self-Attention) 结构。
CAB (通道注意力块) 使用全局池化和通道注意力机制，专注于不同通道之间的依赖关系，以增强特定通道的特征表示。
(S)W-MSA 是一种窗口划分的自注意力机制，通过窗口化操作计算注意力，减少计算开销，同时增强局部与全局信息的交互。
OCAB：通过交叉注意力机制结合局部和全局特征，并通过重叠区域确保信息的连贯性和连续性。
优势：深度特征提取模块通过多个注意力模块结合局部和全局信息，实现对复杂特征的高效捕捉，同时保持较低的计算成本。 - 图像重建 (Image Reconstruction)
深层特征经过多个RHAG模块后，通过上采样操作重建回高分辨率图像。模型将提取到的深层特征与初始输入进行特征融合，生成更高质量的重建图像。 - 模块优势
RHAG (Residual Hybrid Attention Group)：该模块通过残差连接增强网络的梯度流，避免深层网络中的梯度消失问题，同时结合多种注意力机制，提高特征提取的准确性和效率。
HAB (Hybrid Attention Block)：该模块将通道注意力与窗口自注意力相结合，在不同尺度上捕捉图像特征。通道注意力增强了各个特征通道的表示能力，而窗口自注意力通过局部和全局上下文的信息交互来提升整体的特征感知能力。
OCAB (Overlapping Cross-Attention Block)：通过交叉注意力和重叠区域融合，使模型在捕捉局部特征的同时，能够保持对全局特征的感知，避免信息的割裂。
1.2HAT核心代码
import math
import torch
import torch.nn as nn
from basicsr.utils.registry import ARCH_REGISTRY
from basicsr.archs.arch_util import to_2tuple, trunc_normal_
from einops import rearrange
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.):
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim**-0.5
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
trunc_normal_(self.relative_position_bias_table, std=.02)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x, 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.,
qkv_bias=True,
qk_scale=None,
drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
if min(self.input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = 0
self.window_size = min(self.input_resolution)
assert 0 <= self.shift_size < self.window_size, 'shift_size must in 0-window_size'
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim,
window_size=to_2tuple(self.window_size),
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop)
self.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))
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(
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=.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
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.,
qkv_bias=True,
qk_scale=None,
drop=0.,
attn_drop=0.,
drop_path=0.,
norm_layer=nn.LayerNorm,
downsample=None,
use_checkpoint=False):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.ModuleList([
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,
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.,
qkv_bias=True,
qk_scale=None,
drop=0.,
attn_drop=0.,
drop_path=0.,
norm_layer=nn.LayerNorm,
downsample=None,
use_checkpoint=False,
img_size=224,
patch_size=4,
resi_connection='1conv'):
super(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], 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)
@ARCH_REGISTRY.register()
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,
in_chans=3,
img_size=64,
patch_size=1,
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.,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.1,
norm_layer=nn.LayerNorm,
ape=False,
patch_norm=True,
use_checkpoint=False,
upscale=2,
img_range=1.,
upsampler='',
resi_connection='1conv',
**kwargs):
super(HAT, 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)
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)
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)
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
二、添加HAT注意力机制
2.1STEP1
首先找到ultralytics/nn文件路径下新建一个Add-module的python文件包【这里注意一定是python文件包，新建后会自动生成_init_.py】，如果已经跟着我的教程建立过一次了可以省略此步骤，随后新建一个HAT.py文件并将上文中提到的注意力机制的代码全部粘贴到此文件中，如下图所示
2.2STEP2
在STEP1中新建的_init_.py文件中导入增加改进模块的代码包如下图所示
2.3STEP3
找到ultralytics/nn文件夹中的task.py文件，在其中按照下图添加
2.4STEP4
定位到ultralytics/nn文件夹中的task.py文件中的def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)函数添加如图代码,【如果不好定位可以直接ctrl+f搜索定位】
三、yaml文件与运行
3.1yaml文件
以下是添加HAT注意力机制在Backbone中的yaml文件，大家可以注释自行调节，效果以自己的数据集结果为准
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs
# YOLO11n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
- [-1, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 2, C3k2, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 2, C3k2, [1024, True]]
- [-1, 1, HAT, []]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 2, C2PSA, [1024]] # 10
# YOLO11n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, C3k2, [512, False]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 14], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 11], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[17, 20, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)
以上添加位置仅供参考，具体添加位置以及模块效果以自己的数据集结果为准
3.2运行成功截图
OK 以上就是添加HAT注意力机制的全部过程了，后续将持续更新尽情期待
![[Python学习日记-37] Python 中的内置函数（下）](https://i-blog.csdnimg.cn/direct/5719e81902ca4c139f7dc70fa945c67e.png)
