Swin-Transformer通用视觉骨干网络主体结构代码解释
Swin-Transformer由MSRA视觉计算组的team于2021年发表的工作，在多个视觉任务以及多个数据集上均取得了十分优秀的结果。在这里，我贴出我对于Swin-Transformer主体结构的一些代码的解释和tensor的shape的改变，由于时间的原因，可能会出现许多纰漏，希望大家多多指教
paper：https://arxiv.org/pdf/2111.09883v1.pdf
code：GitHub - microsoft/Swin-Transformer: This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
#-------------------------------#
# 此为对于MLP模块的定义
#-------------------------------#
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super(Mlp,self).__init__()
#---------------------------------------#
# 在这里使用Dropout的作用在于
# 降低因为Linear层的使用所造成的过拟合现象的发生
#---------------------------------------#
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
#--------------------------------------------------#
# 对于window_partition的定义
# 在这里x是一个tensor
# window size由自己定义
# view()的作用相当于numpy中的reshape,重新定义矩阵的形状
#--------------------------------------------------#
def window_partition(x, window_size):
"""
Args:
x: (B, H, W, C)
window_size (int): window size
Returns:
windows: (num_windows**2*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)
#-------------------------------------------------------------------------------------------#
# 1. shape = B, H // window_size, W // window_size, window_size, window_size, C
# 2. shape = num_windows**2*B, window_size, window_size, C(返回值window的shape)
# 3. view(-1, window_size, window_size, C)的含义为 后三维度已经确定,第一维由整体矩阵根据后三个维度得到
# 4. H // window_size = W // window_size即为 num_windows
#-------------------------------------------------------------------------------------------#
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
return windows
#-----------------------------------#
# 此为对于window_reverse函数的定义
#-----------------------------------#
def window_reverse(windows, window_size, H, W):
"""
Args:
windows: (num_windows**2*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
#--------------------------------#
# 对于WindowAttention这个类的定义
# 该类支持滑动的以及未滑动的窗口图像
#--------------------------------#
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(WindowAttention,self).__init__()
head_dim = dim // num_heads
self.dim = dim
self.window_size = window_size # Wh,Ww
self.num_heads = num_heads
self.scale = qk_scale or head_dim ** -0.5
#----------------------------------------------------------------#
# define a parameter table of relative position bias
# return: 2*Wh-1 * 2*Ww-1, nH
# nn.Parameter作用为定义这些参数是可以学习的参数
# torch.zeros()：其形状由变量参数size定义,返回一个由标量值0填充的张量
#----------------------------------------------------------------#
self.relative_position_bias_table = nn.Parameter(torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))
#----------------------------------------------------------------------------#
# get pair-wise relative position index for each token inside the window
# torch.arange()所创建的张量是int类型
# torch.meshgrid()的作用在于将两个类型相同的张量生成一个tensor矩阵
# 这个矩阵的行数为第一个input_tensor的维度,列数为第二个input_tensor的维度
# 之后又进行了stack操作,又增加了一个维度,所以此时的shape为2, Wh, Ww
# 之后通过flatten将后两个维度压缩成一个维度，此时的shape为2, Wh*Ww
#----------------------------------------------------------------------------#
coords_h = torch.arange(self.window_size[0])
coords_w = torch.arange(self.window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
coords_flatten = torch.flatten(coords, 1)
#----------------------------------------------#
# [:, :, None]其中的None代表增加一个维度,具体的值为1
# relative_coords的shape为2, Wh*Ww, Wh*Ww
# 之后对其进行转置,此时的shape为Wh*Ww, Wh*Ww, 2
#----------------------------------------------#
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
relative_coords = relative_coords.permute(1, 2, 0).contiguous()
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
#---------------------------------------------#
# relative_position_index的shape为Wh*Ww, Wh*Ww
# 之后将它作为一个模型的常数
#----------------------------------------- ---#
relative_position_index = relative_coords.sum(-1)
self.register_buffer("relative_position_index", relative_position_index)
#--------------------------------------#
# 定义qkv以及proj,在他们之后均有一个Dropout
# 以降低过拟合发生的风险
#--------------------------------------#
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)
#---------------------------------#
# 利用正太分布来生成一个点
# 之后又定义了一个softmax分类器
#---------------------------------#
trunc_normal_(self.relative_position_bias_table, std=.02)
self.softmax = nn.Softmax(dim=-1)
#------------------------------------------------------#
# 该类前向传播函数的定义
# input-x即为shape：num_windows**2*B,N(windows_size), C
#------------------------------------------------------#
def forward(self, x, mask=None):
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
#--------------------------------------------------#
# 首先给qkv添加几个维度,之后再进行转置
# shape= 3,B_,self.num_heads,N,C // self.num_heads
# 之后便可以得知：q=3, k=B_, v=self.num_heads
# 之后再求解q的值,最后进行注意力的计算
#--------------------------------------------------#
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]
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
#-----------------------------------------------#
# relative_position_bias的shape为Wh*Ww,Wh*Ww,nH
# 之后又进行转置,此时的shape为nH, Wh*Ww, Wh*Ww
# 之后又再第一维增加了一个维度
#-----------------------------------------------#
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(self.window_size[0] * self.window_size[1],self.window_size[0] * self.window_size[1], -1)
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
attn = attn + relative_position_bias.unsqueeze(0)
#----------------------------#
# 在这里我们的mask的值为None
# 所以直接pass through softmax
# 之后过dropout
#----------------------------#
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)
#------------------------#
# 进行注意力加权运算
# 之后过投影层
# 最后过投影层的dropout
#------------------------#
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
#------------------------------#
# extra_repr以及flops函数的定义
#------------------------------#
def extra_repr(self) -> str:
return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
def flops(self, N):
# calculate flops for 1 window with token length of N
flops = 0
# qkv = self.qkv(x)
flops += N * self.dim * 3 * self.dim
# attn = (q @ k.transpose(-2, -1))
flops += self.num_heads * N * (self.dim // self.num_heads) * N
# x = (attn @ v)
flops += self.num_heads * N * N * (self.dim // self.num_heads)
# x = self.proj(x)
flops += N * self.dim * self.dim
return flops
class SwinTransformerBlock(nn.Module):
r""" Swin Transformer Block.
Args:
dim (int): Number of input channels. 输入图像的通道数
input_resolution (tuple[int]): Input resulotion. 输入图像的分辨率
num_heads (int): Number of attention heads. 多头注意力中注意力头的数目
window_size (int): Window size.
shift_size (int): Shift size for SW-MSA. 一次滑动操作所滑动的尺寸
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,
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(SwinTransformerBlock,self).__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
#--------------------------------#
# 若input的尺寸小于窗口的大小
#--------------------------------#
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"
#-------------------------------------#
# 定义使用LayerNorm并且定义了窗口注意力机制
# nn.Identity()相当于pass
#-------------------------------------#
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.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)
if self.shift_size > 0:
# calculate attention mask for SW-MSA
# slice用于对数组元素进行截取，返回值为截取元素组成的一个新数组
H, W = self.input_resolution
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
#---------------------------------------------#
# 根据h_slices以及w_slices求取cnt的值并进行相关操作
#---------------------------------------------#
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))
else:
attn_mask = None
self.register_buffer("attn_mask", attn_mask)
def forward(self, x):
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
shortcut = x
x = self.norm1(x)
x = x.view(B, H, W, C)
# cyclic shift
if self.shift_size > 0:
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
else:
shifted_x = x
# partition windows
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA
attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
# merge windows
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
# reverse cyclic shift
if self.shift_size > 0:
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
else:
x = shifted_x
x = x.view(B, H * W, C)
# FFN
x = shortcut + self.drop_path(x)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
def extra_repr(self) -> str:
return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
def flops(self):
flops = 0
H, W = self.input_resolution
# norm1
flops += self.dim * H * W
# W-MSA/SW-MSA
nW = H * W / self.window_size / self.window_size
flops += nW * self.attn.flops(self.window_size * self.window_size)
# mlp
flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
# norm2
flops += self.dim * H * W
return flops
#--------------------------#
# PatchMerging这个类的定义
#--------------------------#
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(PatchMerging,self).__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的shape：B,H*W,C
#-------------------#
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
x = x.view(B, H, W, C)
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
#-------------------------------#
# 在最后一个维度将四个tensor进行拼接
# tensor的shape为 B H/2 W/2 4*C
# 之后进行view操作即为reshape
# 此时张量的shape为B H/2*W/2 4*C
# 之后过LayerNorm
# 最后通过全连接层来降低通道数
# 此时的shape为B H/2*W/2 2*C
#-------------------------------#
x = torch.cat([x0, x1, x2, x3], -1)
x = x.view(B, -1, 4 * C)
x = self.norm(x)
x = self.reduction(x)
return x
#-------------------------------#
# 定义extra_repr以及flops这两个函数
#-------------------------------#
def extra_repr(self) -> str:
return f"input_resolution={self.input_resolution}, dim={self.dim}"
def flops(self):
H, W = self.input_resolution
flops = H * W * self.dim
flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
return flops
#----------------------------------------------------#
# SwinTransformer整体结构中共有四个stage
# 四个stage中的layer的数目分别为2 2 6 2
# 这个类即为对于一个stage中所用到的layer的定义
#----------------------------------------------------#
class BasicLayer(nn.Module):
r""" A basic Swin Transformer layer for one stage.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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. 是否使用checkpointing来节省内存，默认值为False
"""
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
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(BasicLayer,self).__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
self.use_checkpoint = use_checkpoint
#-----------------------------------#
# build blocks
# 根据depth的值来确定一个layer中所用到的
# SwinTransformerBlock的块数
#-----------------------------------#
self.blocks = nn.ModuleList([
SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
num_heads=num_heads, window_size=window_size,
shift_size=0 if (i % 2 == 0) else window_size // 2,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, 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)])
#-----------------------------------------------------#
# patch merging layer
# 在这里我们定义的downsample的值为None所以无downsample操作
#-----------------------------------------------------#
if downsample is not None:
self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
else:
self.downsample = None
def forward(self, x):
for blk in self.blocks:
if self.use_checkpoint:
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
if self.downsample is not None:
x = self.downsample(x)
return x
def extra_repr(self) -> str:
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
def flops(self):
flops = 0
for blk in self.blocks:
flops += blk.flops()
if self.downsample is not None:
flops += self.downsample.flops()
return flops
#------------------------------#
# PatchEmbed这个类的定义
#------------------------------#
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(PatchEmbed,self).__init__()
#--------------------------------#
# to_2tuple()的作用在于生成一个元组
# 并且该元组中有两个值相同的元素
#--------------------------------#
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
self.img_size = img_size
self.patch_size = patch_size
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
if norm_layer is not None:
self.norm = norm_layer(embed_dim)
else:
self.norm = None
def forward(self, x):
B, C, H, W = x.shape
# FIXME look at relaxing size constraints
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
#-----------------------------------------------#
# 1.先进行一次卷积,此时shape=batch_size, C, Ph, Pw
# 2.将后两个维度进行flatten，使其成为Ph*Pw
# 3.进行转置,此时的shape为batch_size,Ph*Pw C
#-----------------------------------------------#
x = self.proj(x).flatten(2).transpose(1, 2)
if self.norm is not None:
x = self.norm(x)
return x
def flops(self):
Ho, Wo = self.patches_resolution
flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
if self.norm is not None:
flops += Ho * Wo * self.embed_dim
return flops
#----------------------------------------------#
# The build of SwinTransformer
# 若在一个类中想要调用另外一个类,则我们并不
# 需要定义self方法,我们使用该类的类名直接进行调用即可
#----------------------------------------------#
class SwinTransformer(nn.Module):
r""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
https://arxiv.org/pdf/2103.14030
Args:
img_size (int | tuple(int)): Input image size. Default 224
patch_size (int | tuple(int)): Patch size. Default: 4
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for classification head. Default: 1000 -> ImageNet
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
"""
def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
window_size=7, 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, **kwargs):
super(SwinTransformer,self).__init__()
self.num_classes = num_classes
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.ape = ape
self.patch_norm = patch_norm
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
self.mlp_ratio = mlp_ratio
#------------------------------------------------#
# split image into non-overlapping patches
# 将输入的图片split成多个patch
#------------------------------------------------#
self.patch_embed = PatchEmbed(
img_size = img_size, patch_size=patch_size, in_chans=in_chans, 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
#---------------------------------------#
# absolute position embedding
# 类中定义的ape=False,所以我们直接pass即可
#---------------------------------------#
if self.ape:
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
trunc_normal_(self.absolute_pos_embed, std=.02)
#----------------------------#
# 定义一个Dropout层
#----------------------------#
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 layers
# 首先定义一个空的ModuleList
# 之后根据layer的数目将BasicLayer添加至ModuleList中
#--------------------------------------------------#
self.layers = nn.ModuleList()
for i_layer in range(self.num_layers):
layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
input_resolution=(patches_resolution[0] // (2 ** i_layer),patches_resolution[1] // (2 ** i_layer)),
depth = depths[i_layer],
num_heads = num_heads[i_layer],
window_size = window_size,
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])],
norm_layer = norm_layer,
downsample = PatchMerging if (i_layer < self.num_layers - 1) else None,
use_checkpoint = use_checkpoint)
self.layers.append(layer)
#-------------------------------------#
# 定义LayerNorm层
# 定义自适应二维平均池化层
# 定义head,使用Linear来实现
# 之后进行了一个网络权重初始化的定义
#-------------------------------------#
self.norm = norm_layer(self.num_features)
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
#-------------------------------#
# 网络的权重初始化
#-------------------------------#
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
return {'absolute_pos_embed'}
@torch.jit.ignore
def no_weight_decay_keywords(self):
return {'relative_position_bias_table'}
#--------------------------------------------------------------------------------------#
# forward_features函数的定义
# 首先将输入的图片打成多个小的patch
# 之后过Dropout来降低发生过拟合的风险,因为Linear层的存在所以说会定义比较多层的Dropout
# 过SwinTransformer的四个stage,过LayerNorm,再过二维平均池化层,最后将后面的两个维度进行flatten
#--------------------------------------------------------------------------------------#
def forward_features(self, x):
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)
#-----------------------------#
# shape的变化
# B L C -> B C 1 -> B C
#-----------------------------#
x = self.norm(x)
x = self.avgpool(x.transpose(1, 2))
x = torch.flatten(x, 1)
return x
#---------------------------------#
# 主要为运用Linear层将特征图像的
# 通道数转变为ImageNet上所规定的类别数
#---------------------------------#
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def flops(self):
flops = 0
flops += self.patch_embed.flops()
for i, layer in enumerate(self.layers):
flops += layer.flops()
flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
flops += self.num_features * self.num_classes
return flops