论文介绍
题目：CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention
论文地址：https://arxiv.org/pdf/2108.00154
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创新点
跨尺度嵌入层 (Cross-scale Embedding Layer, CEL)：
设计了一种新的嵌入方式，将不同尺度的特征融合在一起，使得每一层嵌入包含跨尺度的信息。这种设计解决了现有视觉Transformer无法有效建立不同尺度特征间交互的问题。
通过使用多种不同大小的卷积核提取特征并拼接，提供了更细粒度的跨尺度特性，尤其适合处理含有多尺度目标的视觉任务。
长短距离注意力机制 (Long Short Distance Attention, LSDA)：
将传统的自注意力模块分为短距离注意力 (SDA) 和长距离注意力 (LDA) 两部分，分别处理相邻和远距离特征的依赖。
在降低计算成本的同时，保持了细粒度和粗粒度特征的完整性，从而实现了跨尺度的注意力交互。
动态位置偏置 (Dynamic Position Bias, DPB)：
提出了一种动态位置偏置模块，替代了传统的固定位置偏置，使得模型能够适应不同大小的输入图像和分组规模。
在实现位置偏置灵活性的同时，保留了相对位置表示的优势。
通用性和性能提升：
提出了一种统一的Transformer架构 (CrossFormer)，不仅适用于图像分类，还能处理目标检测、实例分割和语义分割等任务。在多个视觉任务上，CrossFormer均显著优于现有的视觉Transformer架构，尤其在密集预测任务（如目标检测和分割）上表现出更大的优势。
方法
整体架构
CrossFormer模型采用了基于金字塔的多阶段Transformer架构，包括跨尺度嵌入层（CEL）、长短距离注意力机制（LSDA）和动态位置偏置（DPB）。CEL通过多尺度卷积核提取和融合特征，LSDA交替处理局部和全局依赖，DPB增强位置表示的灵活性。模型从输入图像逐步提取跨尺度特征，分辨率逐阶段降低，特征维度逐步增加，最终通过分类头输出结果。这种设计兼具多尺度特征提取、高效计算和任务通用性。
跨尺度嵌入层 (Cross-scale Embedding Layer, CEL)：
每个阶段开始时，采用跨尺度嵌入层（CEL）生成输入嵌入。
CEL通过不同尺寸的卷积核对输入进行多尺度采样，并将这些特征拼接为一个跨尺度嵌入向量。
在Stage-1中，CEL从图像输入提取跨尺度特征，而在后续阶段，CEL从前一阶段的输出中提取特征。
CrossFormer块 (CrossFormer Block)：
长短距离注意力机制 (LSDA)：分为短距离注意力（SDA）和长距离注意力（LDA），交替出现，用于捕获局部和全局特征依赖。
动态位置偏置 (Dynamic Position Bias, DPB)：为嵌入提供相对位置表示，适用于不同尺寸的输入。
每个阶段包含若干CrossFormer块（论文中的图1(b)详细展示了内部结构）。
CrossFormer块由以下两个关键组件构成：
分类头 (Classification Head)：
在最后一个阶段之后，采用全局平均池化和一个全连接层，用于分类任务。
金字塔结构的细节
分辨率和特征维度逐步降低，特征表示逐步加深：
Stage-1: 分辨率最高，特征维度最低，用于提取细粒度特征。
随着阶段的深入，分辨率逐步减小，特征维度逐步增加，以形成金字塔结构。
CEL的卷积核和步长：
Stage-1使用多尺度卷积核（如 4×4, 8×8, 16×16, 32×32），步长为4。
Stage-2/3/4使用更小的卷积核（如 2×2 和 4×4），步长为2，以减小分辨率。
即插即用模块作用
CrossFormer 作为一个即插即用模块：
跨尺度特征建模：
通过跨尺度嵌入层（CEL）融合不同尺度的特征，帮助模型在不同分辨率和粒度的特征之间建立更强的依赖关系。
在图像包含多种尺度对象的任务中，可以显著提高对场景的理解能力。
高效的全局和局部特征捕获：
通过长短距离注意力机制（LSDA），既能够高效捕获局部特征的细节，又能建立全局上下文依赖，适合需要平衡局部和全局特征的任务。
动态适配输入尺寸：
动态位置偏置（DPB）提供灵活的位置表示能力，使模型适用于不同输入大小，减少需要调整模型结构的复杂性。
提升多任务性能：
在目标检测、分割和分类任务上均展现出性能提升，尤其是在密集预测任务（如检测和分割）上效果显著。
消融实验结果
内容：
比较了不同卷积核组合（即不同的跨尺度嵌入策略）对模型性能的影响。
包括单尺度嵌入（如仅使用 4×4 或 8×8 卷积核）与多尺度嵌入（如组合使用 4×4, 8×8, 16×16, 32×32 卷积核）。
结果：
使用多尺度嵌入时，模型准确率显著提高（例如，单一 4×4 卷积核的准确率为 81.5%，而多尺度嵌入的准确率达到 82.5%）。
说明跨尺度特征提取对模型性能至关重要。
内容：
比较了仅使用CEL、仅使用LSDA，以及同时使用两者对模型性能的影响。
与现有的PVT（使用简单降采样）和Swin（局部注意力）进行对比。
结果：
同时使用CEL和LSDA时，模型的准确率最高（82.5%），显著优于仅使用CEL（81.5%）或仅使用LSDA（81.9%）。
说明CEL和LSDA对模型性能的提升是互补的，二者结合可以充分发挥跨尺度特征的优势。
即插即用模块
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
# 论文地址：https://arxiv.org/pdf/2108.00154
# 论文：CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention (ICLR 2022 Acceptance).
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
class DynamicPosBias(nn.Module):
def __init__(self, dim, num_heads, residual):
super().__init__()
self.residual = residual
self.num_heads = num_heads
self.pos_dim = dim // 4
self.pos_proj = nn.Linear(2, self.pos_dim)
self.pos1 = nn.Sequential(
nn.LayerNorm(self.pos_dim),
nn.ReLU(inplace=True),
nn.Linear(self.pos_dim, self.pos_dim),
)
self.pos2 = nn.Sequential(
nn.LayerNorm(self.pos_dim),
nn.ReLU(inplace=True),
nn.Linear(self.pos_dim, self.pos_dim)
)
self.pos3 = nn.Sequential(
nn.LayerNorm(self.pos_dim),
nn.ReLU(inplace=True),
nn.Linear(self.pos_dim, self.num_heads)
)
def forward(self, biases):
if self.residual:
pos = self.pos_proj(biases) # 2Wh-1 * 2Ww-1, heads
pos = pos + self.pos1(pos)
pos = pos + self.pos2(pos)
pos = self.pos3(pos)
else:
pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases))))
return pos
def flops(self, N):
flops = N * 2 * self.pos_dim
flops += N * self.pos_dim * self.pos_dim
flops += N * self.pos_dim * self.pos_dim
flops += N * self.pos_dim * self.num_heads
return flops
class Attention(nn.Module):
r""" Multi-head self attention module with dynamic position bias.
Args:
dim (int): Number of input channels.
group_size (tuple[int]): The height and width of the group.
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, group_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.,
position_bias=True):
super().__init__()
self.dim = dim
self.group_size = group_size # Wh, Ww
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.position_bias = position_bias
if position_bias:
self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False)
# generate mother-set
position_bias_h = torch.arange(1 - self.group_size[0], self.group_size[0])
position_bias_w = torch.arange(1 - self.group_size[1], self.group_size[1])
biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w])) # 2, 2Wh-1, 2W2-1
biases = biases.flatten(1).transpose(0, 1).float()
self.register_buffer("biases", biases)
# get pair-wise relative position index for each token inside the group
coords_h = torch.arange(self.group_size[0])
coords_w = torch.arange(self.group_size[1])
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.group_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.group_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.group_size[1] - 1
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("relative_position_index", relative_position_index)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x, mask=None):
"""
Args:
x: input features with shape of (num_groups*B, N, C)
mask: (0/-inf) mask with shape of (num_groups, 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))
if self.position_bias:
pos = self.pos(self.biases) # 2Wh-1 * 2Ww-1, heads
# select position bias
relative_position_bias = pos[self.relative_position_index.view(-1)].view(
self.group_size[0] * self.group_size[1], self.group_size[0] * self.group_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
def extra_repr(self) -> str:
return f'dim={self.dim}, group_size={self.group_size}, num_heads={self.num_heads}'
def flops(self, N):
# calculate flops for 1 group 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
if self.position_bias:
flops += self.pos.flops(N)
return flops
class CrossFormerBlock(nn.Module):
r""" CrossFormer Block.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resulotion.
num_heads (int): Number of attention heads.
group_size (int): Group size.
lsda_flag (int): use SDA or LDA, 0 for SDA and 1 for LDA.
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, group_size=7, lsda_flag=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, num_patch_size=1):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
self.group_size = group_size
self.lsda_flag = lsda_flag
self.mlp_ratio = mlp_ratio
self.num_patch_size = num_patch_size
if min(self.input_resolution) <= self.group_size:
# if group size is larger than input resolution, we don't partition groups
self.lsda_flag = 0
self.group_size = min(self.input_resolution)
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, group_size=to_2tuple(self.group_size), num_heads=num_heads,
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop,
position_bias=True)
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)
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 %d, %d, %d" % (L, H, W)
shortcut = x
x = self.norm1(x)
x = x.view(B, H, W, C)
# group embeddings
G = self.group_size
if self.lsda_flag == 0: # 0 for SDA
x = x.reshape(B, H // G, G, W // G, G, C).permute(0, 1, 3, 2, 4, 5)
else: # 1 for LDA
x = x.reshape(B, G, H // G, G, W // G, C).permute(0, 2, 4, 1, 3, 5)
x = x.reshape(B * H * W // G**2, G**2, C)
# multi-head self-attention
x = self.attn(x, mask=self.attn_mask) # nW*B, G*G, C
# ungroup embeddings
x = x.reshape(B, H // G, W // G, G, G, C)
if self.lsda_flag == 0:
x = x.permute(0, 1, 3, 2, 4, 5).reshape(B, H, W, C)
else:
x = x.permute(0, 3, 1, 4, 2, 5).reshape(B, H, W, C)
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"group_size={self.group_size}, lsda_flag={self.lsda_flag}, mlp_ratio={self.mlp_ratio}"
def flops(self):
flops = 0
H, W = self.input_resolution
# norm1
flops += self.dim * H * W
# LSDA
nW = H * W / self.group_size / self.group_size
flops += nW * self.attn.flops(self.group_size * self.group_size)
# mlp
flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
# norm2
flops += self.dim * H * W
return flops
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, patch_size=[2], num_input_patch_size=1):
super().__init__()
self.input_resolution = input_resolution
self.dim = dim
self.reductions = nn.ModuleList()
self.patch_size = patch_size
self.norm = norm_layer(dim)
for i, ps in enumerate(patch_size):
if i == len(patch_size) - 1:
out_dim = 2 * dim // 2 ** i
else:
out_dim = 2 * dim // 2 ** (i + 1)
stride = 2
padding = (ps - stride) // 2
self.reductions.append(nn.Conv2d(dim, out_dim, kernel_size=ps,
stride=stride, padding=padding))
def forward(self, x):
"""
x: 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 = self.norm(x)
x = x.view(B, H, W, C).permute(0, 3, 1, 2)
xs = []
for i in range(len(self.reductions)):
tmp_x = self.reductions[i](x).flatten(2).transpose(1, 2)
xs.append(tmp_x)
x = torch.cat(xs, dim=2)
return x
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
for i, ps in enumerate(self.patch_size):
if i == len(self.patch_size) - 1:
out_dim = 2 * self.dim // 2 ** i
else:
out_dim = 2 * self.dim // 2 ** (i + 1)
flops += (H // 2) * (W // 2) * ps * ps * out_dim * self.dim
return flops
class Stage(nn.Module):
""" CrossFormer blocks 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.
group_size (int): variable G in the paper, one group has GxG embeddings
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, group_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,
patch_size_end=[4], num_patch_size=None):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.ModuleList()
for i in range(depth):
lsda_flag = 0 if (i % 2 == 0) else 1
self.blocks.append(CrossFormerBlock(dim=dim, input_resolution=input_resolution,
num_heads=num_heads, group_size=group_size,
lsda_flag=lsda_flag,
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,
num_patch_size=num_patch_size))
# patch merging layer
if downsample is not None:
self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer,
patch_size=patch_size_end, num_input_patch_size=num_patch_size)
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
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[0] // patch_size[0]]
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.projs = nn.ModuleList()
for i, ps in enumerate(patch_size):
if i == len(patch_size) - 1:
dim = embed_dim // 2 ** i
else:
dim = embed_dim // 2 ** (i + 1)
stride = patch_size[0]
padding = (ps - patch_size[0]) // 2
self.projs.append(nn.Conv2d(in_chans, dim, kernel_size=ps, stride=stride, padding=padding))
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]})."
xs = []
for i in range(len(self.projs)):
tx = self.projs[i](x).flatten(2).transpose(1, 2)
xs.append(tx) # B Ph*Pw C
x = torch.cat(xs, dim=2)
if self.norm is not None:
x = self.norm(x)
return x
def flops(self):
Ho, Wo = self.patches_resolution
flops = 0
for i, ps in enumerate(self.patch_size):
if i == len(self.patch_size) - 1:
dim = self.embed_dim // 2 ** i
else:
dim = self.embed_dim // 2 ** (i + 1)
flops += Ho * Wo * dim * self.in_chans * (self.patch_size[i] * self.patch_size[i])
if self.norm is not None:
flops += Ho * Wo * self.embed_dim
return flops
class CrossFormer(nn.Module):
r""" CrossFormer
A PyTorch impl of : `CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention` -
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
embed_dim (int): Patch embedding dimension. Default: 96
depths (tuple(int)): Depth of each stage.
num_heads (tuple(int)): Number of attention heads in different layers.
group_size (int): Group 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],
group_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, merge_size=[[2], [2], [2]], **kwargs):
super().__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
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
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 layers
self.layers = nn.ModuleList()
num_patch_sizes = [len(patch_size)] + [len(m) for m in merge_size]
for i_layer in range(self.num_layers):
patch_size_end = merge_size[i_layer] if i_layer < self.num_layers - 1 else None
num_patch_size = num_patch_sizes[i_layer]
layer = Stage(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],
group_size=group_size[i_layer],
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,
patch_size_end=patch_size_end,
num_patch_size=num_patch_size)
self.layers.append(layer)
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)
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'}
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)
x = self.norm(x) # B L C
x = self.avgpool(x.transpose(1, 2)) # B C 1
x = torch.flatten(x, 1)
return x
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
if __name__ == '__main__':
input=torch.randn(1,3,224,224)
model = CrossFormer(img_size=224,
patch_size=[4, 8, 16, 32],
in_chans= 3,
num_classes=1000,
embed_dim=48,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
group_size=[7, 7, 7, 7],
mlp_ratio=4.,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
drop_path_rate=0.1,
ape=False,
patch_norm=True,
use_checkpoint=False,
merge_size=[[2, 4], [2,4], [2, 4]]
)
output=model(input) print(output.shape)
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