【RT-DETR有效改进】EfficientFormerV2移动设备优化的视觉网络（附对比试验效果图）
前言
大家好，我是Snu77，这里是RT-DETR有效涨点专栏。
本专栏的内容为根据ultralytics版本的RT-DETR进行改进，内容持续更新，每周更新文章数量3-10篇。
专栏以ResNet18、ResNet50为基础修改版本，同时修改内容也支持ResNet32、ResNet101和PPHGNet版本，其中ResNet为RT-DETR官方版本1：1移植过来的，参数量基本保持一致(误差很小很小)，不同于ultralytics仓库版本的ResNet官方版本，同时ultralytics仓库的一些参数是和RT-DETR相冲的所以我也是会教大家调好一些参数和代码，真正意义上的跑ultralytics的和RT-DETR官方版本的无区别
欢迎大家订阅本专栏，一起学习RT-DETR
一、本文介绍
本文给大家带来的改进机制是特征提取网络EfficientFormerV2，其是一种针对移动设备优化的视觉变换器（Vision Transformer），它通过重新考虑ViTs的设计选择，实现了低延迟和高参数效率，通过修改改网络我们的参数量降低了约百分之五十，GFLOPs也降低了百分之五十，其作为一种高效和轻量化的网络无论从精度还是效果上都非常推荐大家来使用。
专栏链接：RT-DETR剑指论文专栏，持续复现各种顶会内容——论文收割机RT-DETR
目录
一、本文介绍
二、EfficientFormerV2原理
三、EfficientformerV2的核心代码
四、手把手教你添加EfficientformerV2
4.1 修改一
4.2 修改二
4.3 修改三
4.4 修改四
4.5 修改五
4.6 修改六
4.7 修改七
4.8 修改八
4.9 RT-DETR不能打印计算量问题的解决
4.10 可选修改
五、EfficientformerV2的yaml文件
5.1 yaml文件
5.2 运行文件
5.3 成功训练截图
六、全文总结
二、EfficientFormerV2原理
论文地址： 论文官方代码
代码地址： 代码官方代码
EfficientFormerV2是一种针对移动设备优化的视觉变换器（Vision Transformer），它通过重新考虑ViTs的设计选择，实现了低延迟和高参数效率。
关键改进包括：
1. 统一的前馈网络（FFN）：通过在FFN中集成深度卷积来增强局部信息处理能力，减少了显式的局部令牌混合器，简化了网络架构。
2. 搜索空间细化：探索了网络深度和宽度的变化，发现更深更窄的网络能够在提高准确性的同时降低参数数量和延迟。
3. MHSA（多头自注意力）改进：通过在值矩阵中加入局部信息和增加不同注意力头之间的交流来提升性能，而不增加模型大小和延迟。
4. 高分辨率下的注意力机制：研究了在早期阶段应用MHSA的策略，通过简化注意力模块的复杂度来提升效率。
5. 双路径注意力下采样：结合了静态局部下采样和可学习的局部下采样，以及通过残差连接的常规跨步卷积来形成局部-全局方式，进一步提高准确性。
6. 联合优化模型大小和速度：通过精细化的联合搜索算法来优化模型大小和速度，找到适合移动设备部署的最优视觉骨干网络。这张图展示了EfficientFormerV2的网络架构设计，它包括不同的阶段，每个阶段都有不同的组件和功能。
a. 整体架构：从输入层（stem）开始，通过四个阶段逐渐降低分辨率，同时提升特征维度。
b. 统一FFN模块：这一部分结合了池化操作和深度可分离卷积，用于增强局部特征的提取。结合了深度卷积层（DW.Conv3x3-BN），用以加强局部信息的处理。
c. MHSA模块：这是一个多头自注意力机制，其中包含局部性引入和Talking Head机制以提高性能。多头自注意力（MHSA）模块通过引入局部性（Locality）和不同注意力头之间的对话（Talking Head）来提升性能。
d/e. 注意力高分辨率解决方案：在网络的早期层次使用注意力机制，以处理高分辨率的特征图。
f. 双路径注意力下采样：结合了传统的卷积和注意力机制，它结合了卷积和池化操作，实现有效的特征下采样。
三、EfficientformerV2的核心代码
代码的使用方式看章节四。
import os
import torch
import torch.nn as nn
import math
import itertools
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
__all__ = ['efficientformerv2_s0', 'efficientformerv2_s1', 'efficientformerv2_s2', 'efficientformerv2_l']
EfficientFormer_width = {
'L': [40, 80, 192, 384], # 26m 83.3% 6attn
'S2': [32, 64, 144, 288], # 12m 81.6% 4attn dp0.02
'S1': [32, 48, 120, 224], # 6.1m 79.0
'S0': [32, 48, 96, 176], # 75.0 75.7
}
EfficientFormer_depth = {
'L': [5, 5, 15, 10], # 26m 83.3%
'S2': [4, 4, 12, 8], # 12m
'S1': [3, 3, 9, 6], # 79.0
'S0': [2, 2, 6, 4], # 75.7
}
# 26m
expansion_ratios_L = {
'0': [4, 4, 4, 4, 4],
'1': [4, 4, 4, 4, 4],
'2': [4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4],
'3': [4, 4, 4, 3, 3, 3, 3, 4, 4, 4],
}
# 12m
expansion_ratios_S2 = {
'0': [4, 4, 4, 4],
'1': [4, 4, 4, 4],
'2': [4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4],
'3': [4, 4, 3, 3, 3, 3, 4, 4],
}
# 6.1m
expansion_ratios_S1 = {
'0': [4, 4, 4],
'1': [4, 4, 4],
'2': [4, 4, 3, 3, 3, 3, 4, 4, 4],
'3': [4, 4, 3, 3, 4, 4],
}
# 3.5m
expansion_ratios_S0 = {
'0': [4, 4],
'1': [4, 4],
'2': [4, 3, 3, 3, 4, 4],
'3': [4, 3, 3, 4],
}
class Attention4D(torch.nn.Module):
def __init__(self, dim=384, key_dim=32, num_heads=8,
attn_ratio=4,
resolution=7,
act_layer=nn.ReLU,
stride=None):
super().__init__()
self.num_heads = num_heads
self.scale = key_dim ** -0.5
self.key_dim = key_dim
self.nh_kd = nh_kd = key_dim * num_heads
if stride is not None:
self.resolution = math.ceil(resolution / stride)
self.stride_conv = nn.Sequential(nn.Conv2d(dim, dim, kernel_size=3, stride=stride, padding=1, groups=dim),
nn.BatchNorm2d(dim), )
self.upsample = nn.Upsample(scale_factor=stride, mode='bilinear')
else:
self.resolution = resolution
self.stride_conv = None
self.upsample = None
self.N = self.resolution ** 2
self.N2 = self.N
self.d = int(attn_ratio * key_dim)
self.dh = int(attn_ratio * key_dim) * num_heads
self.attn_ratio = attn_ratio
h = self.dh + nh_kd * 2
self.q = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.key_dim, 1),
nn.BatchNorm2d(self.num_heads * self.key_dim), )
self.k = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.key_dim, 1),
nn.BatchNorm2d(self.num_heads * self.key_dim), )
self.v = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.d, 1),
nn.BatchNorm2d(self.num_heads * self.d),
)
self.v_local = nn.Sequential(nn.Conv2d(self.num_heads * self.d, self.num_heads * self.d,
kernel_size=3, stride=1, padding=1, groups=self.num_heads * self.d),
nn.BatchNorm2d(self.num_heads * self.d), )
self.talking_head1 = nn.Conv2d(self.num_heads, self.num_heads, kernel_size=1, stride=1, padding=0)
self.talking_head2 = nn.Conv2d(self.num_heads, self.num_heads, kernel_size=1, stride=1, padding=0)
self.proj = nn.Sequential(act_layer(),
nn.Conv2d(self.dh, dim, 1),
nn.BatchNorm2d(dim), )
points = list(itertools.product(range(self.resolution), range(self.resolution)))
N = len(points)
attention_offsets = {}
idxs = []
for p1 in points:
for p2 in points:
offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
if offset not in attention_offsets:
attention_offsets[offset] = len(attention_offsets)
idxs.append(attention_offsets[offset])
self.attention_biases = torch.nn.Parameter(
torch.zeros(num_heads, len(attention_offsets)))
self.register_buffer('attention_bias_idxs',
torch.LongTensor(idxs).view(N, N))
@torch.no_grad()
def train(self, mode=True):
super().train(mode)
if mode and hasattr(self, 'ab'):
del self.ab
else:
self.ab = self.attention_biases[:, self.attention_bias_idxs]
def forward(self, x): # x (B,N,C)
B, C, H, W = x.shape
if self.stride_conv is not None:
x = self.stride_conv(x)
q = self.q(x).flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 3, 2)
k = self.k(x).flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 2, 3)
v = self.v(x)
v_local = self.v_local(v)
v = v.flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 3, 2)
attn = (
(q @ k) * self.scale
+
(self.attention_biases[:, self.attention_bias_idxs]
if self.training else self.ab)
)
# attn = (q @ k) * self.scale
attn = self.talking_head1(attn)
attn = attn.softmax(dim=-1)
attn = self.talking_head2(attn)
x = (attn @ v)
out = x.transpose(2, 3).reshape(B, self.dh, self.resolution, self.resolution) + v_local
if self.upsample is not None:
out = self.upsample(out)
out = self.proj(out)
return out
def stem(in_chs, out_chs, act_layer=nn.ReLU):
return nn.Sequential(
nn.Conv2d(in_chs, out_chs // 2, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(out_chs // 2),
act_layer(),
nn.Conv2d(out_chs // 2, out_chs, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(out_chs),
act_layer(),
)
class LGQuery(torch.nn.Module):
def __init__(self, in_dim, out_dim, resolution1, resolution2):
super().__init__()
self.resolution1 = resolution1
self.resolution2 = resolution2
self.pool = nn.AvgPool2d(1, 2, 0)
self.local = nn.Sequential(nn.Conv2d(in_dim, in_dim, kernel_size=3, stride=2, padding=1, groups=in_dim),
)
self.proj = nn.Sequential(nn.Conv2d(in_dim, out_dim, 1),
nn.BatchNorm2d(out_dim), )
def forward(self, x):
local_q = self.local(x)
pool_q = self.pool(x)
q = local_q + pool_q
q = self.proj(q)
return q
class Attention4DDownsample(torch.nn.Module):
def __init__(self, dim=384, key_dim=16, num_heads=8,
attn_ratio=4,
resolution=7,
out_dim=None,
act_layer=None,
):
super().__init__()
self.num_heads = num_heads
self.scale = key_dim ** -0.5
self.key_dim = key_dim
self.nh_kd = nh_kd = key_dim * num_heads
self.resolution = resolution
self.d = int(attn_ratio * key_dim)
self.dh = int(attn_ratio * key_dim) * num_heads
self.attn_ratio = attn_ratio
h = self.dh + nh_kd * 2
if out_dim is not None:
self.out_dim = out_dim
else:
self.out_dim = dim
self.resolution2 = math.ceil(self.resolution / 2)
self.q = LGQuery(dim, self.num_heads * self.key_dim, self.resolution, self.resolution2)
self.N = self.resolution ** 2
self.N2 = self.resolution2 ** 2
self.k = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.key_dim, 1),
nn.BatchNorm2d(self.num_heads * self.key_dim), )
self.v = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.d, 1),
nn.BatchNorm2d(self.num_heads * self.d),
)
self.v_local = nn.Sequential(nn.Conv2d(self.num_heads * self.d, self.num_heads * self.d,
kernel_size=3, stride=2, padding=1, groups=self.num_heads * self.d),
nn.BatchNorm2d(self.num_heads * self.d), )
self.proj = nn.Sequential(
act_layer(),
nn.Conv2d(self.dh, self.out_dim, 1),
nn.BatchNorm2d(self.out_dim), )
points = list(itertools.product(range(self.resolution), range(self.resolution)))
points_ = list(itertools.product(
range(self.resolution2), range(self.resolution2)))
N = len(points)
N_ = len(points_)
attention_offsets = {}
idxs = []
for p1 in points_:
for p2 in points:
size = 1
offset = (
abs(p1[0] * math.ceil(self.resolution / self.resolution2) - p2[0] + (size - 1) / 2),
abs(p1[1] * math.ceil(self.resolution / self.resolution2) - p2[1] + (size - 1) / 2))
if offset not in attention_offsets:
attention_offsets[offset] = len(attention_offsets)
idxs.append(attention_offsets[offset])
self.attention_biases = torch.nn.Parameter(
torch.zeros(num_heads, len(attention_offsets)))
self.register_buffer('attention_bias_idxs',
torch.LongTensor(idxs).view(N_, N))
@torch.no_grad()
def train(self, mode=True):
super().train(mode)
if mode and hasattr(self, 'ab'):
del self.ab
else:
self.ab = self.attention_biases[:, self.attention_bias_idxs]
def forward(self, x): # x (B,N,C)
B, C, H, W = x.shape
q = self.q(x).flatten(2).reshape(B, self.num_heads, -1, self.N2).permute(0, 1, 3, 2)
k = self.k(x).flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 2, 3)
v = self.v(x)
v_local = self.v_local(v)
v = v.flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 3, 2)
attn = (
(q @ k) * self.scale
+
(self.attention_biases[:, self.attention_bias_idxs]
if self.training else self.ab)
)
# attn = (q @ k) * self.scale
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(2, 3)
out = x.reshape(B, self.dh, self.resolution2, self.resolution2) + v_local
out = self.proj(out)
return out
class Embedding(nn.Module):
def __init__(self, patch_size=3, stride=2, padding=1,
in_chans=3, embed_dim=768, norm_layer=nn.BatchNorm2d,
light=False, asub=False, resolution=None, act_layer=nn.ReLU, attn_block=Attention4DDownsample):
super().__init__()
self.light = light
self.asub = asub
if self.light:
self.new_proj = nn.Sequential(
nn.Conv2d(in_chans, in_chans, kernel_size=3, stride=2, padding=1, groups=in_chans),
nn.BatchNorm2d(in_chans),
nn.Hardswish(),
nn.Conv2d(in_chans, embed_dim, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(embed_dim),
)
self.skip = nn.Sequential(
nn.Conv2d(in_chans, embed_dim, kernel_size=1, stride=2, padding=0),
nn.BatchNorm2d(embed_dim)
)
elif self.asub:
self.attn = attn_block(dim=in_chans, out_dim=embed_dim,
resolution=resolution, act_layer=act_layer)
patch_size = to_2tuple(patch_size)
stride = to_2tuple(stride)
padding = to_2tuple(padding)
self.conv = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size,
stride=stride, padding=padding)
self.bn = norm_layer(embed_dim) if norm_layer else nn.Identity()
else:
patch_size = to_2tuple(patch_size)
stride = to_2tuple(stride)
padding = to_2tuple(padding)
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size,
stride=stride, padding=padding)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
if self.light:
out = self.new_proj(x) + self.skip(x)
elif self.asub:
out_conv = self.conv(x)
out_conv = self.bn(out_conv)
out = self.attn(x) + out_conv
else:
x = self.proj(x)
out = self.norm(x)
return out
class Mlp(nn.Module):
"""
Implementation of MLP with 1*1 convolutions.
Input: tensor with shape [B, C, H, W]
"""
def __init__(self, in_features, hidden_features=None,
out_features=None, act_layer=nn.GELU, drop=0., mid_conv=False):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.mid_conv = mid_conv
self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
self.act = act_layer()
self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
self.drop = nn.Dropout(drop)
self.apply(self._init_weights)
if self.mid_conv:
self.mid = nn.Conv2d(hidden_features, hidden_features, kernel_size=3, stride=1, padding=1,
groups=hidden_features)
self.mid_norm = nn.BatchNorm2d(hidden_features)
self.norm1 = nn.BatchNorm2d(hidden_features)
self.norm2 = nn.BatchNorm2d(out_features)
def _init_weights(self, m):
if isinstance(m, nn.Conv2d):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.fc1(x)
x = self.norm1(x)
x = self.act(x)
if self.mid_conv:
x_mid = self.mid(x)
x_mid = self.mid_norm(x_mid)
x = self.act(x_mid)
x = self.drop(x)
x = self.fc2(x)
x = self.norm2(x)
x = self.drop(x)
return x
class AttnFFN(nn.Module):
def __init__(self, dim, mlp_ratio=4.,
act_layer=nn.ReLU, norm_layer=nn.LayerNorm,
drop=0., drop_path=0.,
use_layer_scale=True, layer_scale_init_value=1e-5,
resolution=7, stride=None):
super().__init__()
self.token_mixer = Attention4D(dim, resolution=resolution, act_layer=act_layer, stride=stride)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
act_layer=act_layer, drop=drop, mid_conv=True)
self.drop_path = DropPath(drop_path) if drop_path > 0. \
else nn.Identity()
self.use_layer_scale = use_layer_scale
if use_layer_scale:
self.layer_scale_1 = nn.Parameter(
layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)
self.layer_scale_2 = nn.Parameter(
layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)
def forward(self, x):
if self.use_layer_scale:
x = x + self.drop_path(self.layer_scale_1 * self.token_mixer(x))
x = x + self.drop_path(self.layer_scale_2 * self.mlp(x))
else:
x = x + self.drop_path(self.token_mixer(x))
x = x + self.drop_path(self.mlp(x))
return x
class FFN(nn.Module):
def __init__(self, dim, pool_size=3, mlp_ratio=4.,
act_layer=nn.GELU,
drop=0., drop_path=0.,
use_layer_scale=True, layer_scale_init_value=1e-5):
super().__init__()
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
act_layer=act_layer, drop=drop, mid_conv=True)
self.drop_path = DropPath(drop_path) if drop_path > 0. \
else nn.Identity()
self.use_layer_scale = use_layer_scale
if use_layer_scale:
self.layer_scale_2 = nn.Parameter(
layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)
def forward(self, x):
if self.use_layer_scale:
x = x + self.drop_path(self.layer_scale_2 * self.mlp(x))
else:
x = x + self.drop_path(self.mlp(x))
return x
def eformer_block(dim, index, layers,
pool_size=3, mlp_ratio=4.,
act_layer=nn.GELU, norm_layer=nn.LayerNorm,
drop_rate=.0, drop_path_rate=0.,
use_layer_scale=True, layer_scale_init_value=1e-5, vit_num=1, resolution=7, e_ratios=None):
blocks = []
for block_idx in range(layers[index]):
block_dpr = drop_path_rate * (
block_idx + sum(layers[:index])) / (sum(layers) - 1)
mlp_ratio = e_ratios[str(index)][block_idx]
if index >= 2 and block_idx > layers[index] - 1 - vit_num:
if index == 2:
stride = 2
else:
stride = None
blocks.append(AttnFFN(
dim, mlp_ratio=mlp_ratio,
act_layer=act_layer, norm_layer=norm_layer,
drop=drop_rate, drop_path=block_dpr,
use_layer_scale=use_layer_scale,
layer_scale_init_value=layer_scale_init_value,
resolution=resolution,
stride=stride,
))
else:
blocks.append(FFN(
dim, pool_size=pool_size, mlp_ratio=mlp_ratio,
act_layer=act_layer,
drop=drop_rate, drop_path=block_dpr,
use_layer_scale=use_layer_scale,
layer_scale_init_value=layer_scale_init_value,
))
blocks = nn.Sequential(*blocks)
return blocks
class EfficientFormerV2(nn.Module):
def __init__(self, layers, embed_dims=None,
mlp_ratios=4, downsamples=None,
pool_size=3,
norm_layer=nn.BatchNorm2d, act_layer=nn.GELU,
num_classes=1000,
down_patch_size=3, down_stride=2, down_pad=1,
drop_rate=0., drop_path_rate=0.,
use_layer_scale=True, layer_scale_init_value=1e-5,
fork_feat=True,
vit_num=0,
resolution=640,
e_ratios=expansion_ratios_L,
**kwargs):
super().__init__()
if not fork_feat:
self.num_classes = num_classes
self.fork_feat = fork_feat
self.patch_embed = stem(3, embed_dims[0], act_layer=act_layer)
network = []
for i in range(len(layers)):
stage = eformer_block(embed_dims[i], i, layers,
pool_size=pool_size, mlp_ratio=mlp_ratios,
act_layer=act_layer, norm_layer=norm_layer,
drop_rate=drop_rate,
drop_path_rate=drop_path_rate,
use_layer_scale=use_layer_scale,
layer_scale_init_value=layer_scale_init_value,
resolution=math.ceil(resolution / (2 ** (i + 2))),
vit_num=vit_num,
e_ratios=e_ratios)
network.append(stage)
if i >= len(layers) - 1:
break
if downsamples[i] or embed_dims[i] != embed_dims[i + 1]:
# downsampling between two stages
if i >= 2:
asub = True
else:
asub = False
network.append(
Embedding(
patch_size=down_patch_size, stride=down_stride,
padding=down_pad,
in_chans=embed_dims[i], embed_dim=embed_dims[i + 1],
resolution=math.ceil(resolution / (2 ** (i + 2))),
asub=asub,
act_layer=act_layer, norm_layer=norm_layer,
)
)
self.network = nn.ModuleList(network)
if self.fork_feat:
# add a norm layer for each output
self.out_indices = [0, 2, 4, 6]
for i_emb, i_layer in enumerate(self.out_indices):
if i_emb == 0 and os.environ.get('FORK_LAST3', None):
layer = nn.Identity()
else:
layer = norm_layer(embed_dims[i_emb])
layer_name = f'norm{i_layer}'
self.add_module(layer_name, layer)
self.width_list = [i.size(1) for i in self.forward(torch.randn(1, 3, resolution, resolution))]
def forward_tokens(self, x):
outs = []
for idx, block in enumerate(self.network):
x = block(x)
if self.fork_feat and idx in self.out_indices:
norm_layer = getattr(self, f'norm{idx}')
x_out = norm_layer(x)
outs.append(x_out)
return outs
def forward(self, x):
x = self.patch_embed(x)
x = self.forward_tokens(x)
return x
def efficientformerv2_s0(pretrained=False, **kwargs):
model = EfficientFormerV2(
layers=EfficientFormer_depth['S0'],
embed_dims=EfficientFormer_width['S0'],
downsamples=[True, True, True, True, True],
vit_num=2,
drop_path_rate=0.0,
e_ratios=expansion_ratios_S0,
**kwargs)
return model
def efficientformerv2_s1(pretrained=False, **kwargs):
model = EfficientFormerV2(
layers=EfficientFormer_depth['S1'],
embed_dims=EfficientFormer_width['S1'],
downsamples=[True, True, True, True],
vit_num=2,
drop_path_rate=0.0,
e_ratios=expansion_ratios_S1,
**kwargs)
return model
def efficientformerv2_s2(pretrained=False, **kwargs):
model = EfficientFormerV2(
layers=EfficientFormer_depth['S2'],
embed_dims=EfficientFormer_width['S2'],
downsamples=[True, True, True, True],
vit_num=4,
drop_path_rate=0.02,
e_ratios=expansion_ratios_S2,
**kwargs)
return model
def efficientformerv2_l(pretrained=False, **kwargs):
model = EfficientFormerV2(
layers=EfficientFormer_depth['L'],
embed_dims=EfficientFormer_width['L'],
downsamples=[True, True, True, True],
vit_num=6,
drop_path_rate=0.1,
e_ratios=expansion_ratios_L,
**kwargs)
return model
if __name__ == '__main__':
inputs = torch.randn((1, 3, 640, 640))
model = efficientformerv2_l()
res = model(inputs)
for i in res:
print(i.size())
四、手把手教你添加EfficientformerV2
下面教大家如何修改该网络结构，主干网络结构的修改步骤比较复杂，我也会将task.py文件上传到CSDN的文件中，大家如果自己修改不正确，可以尝试用我的task.py文件替换你的，然后只需要修改其中的第1、2、3、5步即可。
⭐修改过程中大家一定要仔细⭐
4.1 修改一
首先我门中到如下“ultralytics/nn”的目录，我们在这个目录下在创建一个新的目录，名字为'Addmodules'（此文件之后就用于存放我们的所有改进机制），之后我们在创建的目录内创建一个新的py文件复制粘贴进去 ，可以根据文章改进机制来起，这里大家根据自己的习惯命名即可。
4.2 修改二
第二步我们在我们创建的目录内创建一个新的py文件名字为'__init__.py'（只需要创建一个即可），然后在其内部导入我们本文的改进机制即可，其余代码均为未发大家没有不用理会！。
4.3 修改三
第三步我门中到如下文件'ultralytics/nn/tasks.py'然后在开头导入我们的所有改进机制（如果你用了我多个改进机制，这一步只需要修改一次即可）。
4.4 修改四
添加如下两行代码！！！
4.5 修改五
找到七百多行大概把具体看图片，按照图片来修改就行，添加红框内的部分，注意没有()只是函数名（此处我的文件里已经添加很多了后期都会发出来，大家没有的不用理会即可）。
elif m in {自行添加对应的模型即可，下面都是一样的}:
m = m(*args)
c2 = m.width_list # 返回通道列表
backbone = True4.6 修改六
用下面的代码替换红框内的内容。
if isinstance(c2, list):
m_ = m
m_.backbone = True
else:
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
t = str(m)[8:-2].replace('__main__.', '') # module type
m.np = sum(x.numel() for x in m_.parameters()) # number params
m_.i, m_.f, m_.type = i + 4 if backbone else i, f, t # attach index, 'from' index, type
if verbose:
LOGGER.info(f'{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f} {t:<45}{str(args):<30}') # print
save.extend(
x % (i + 4 if backbone else i) for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)
if i == 0:
ch = []
if isinstance(c2, list):
ch.extend(c2)
if len(c2) != 5:
ch.insert(0, 0)
else:
ch.append(c2)4.7 修改七
修改七这里非常要注意，不是文件开头YOLOv8的那predict，是400+行的RTDETR的predict！！！初始模型如下，用我给的代码替换即可！！！
代码如下->
def predict(self, x, profile=False, visualize=False, batch=None, augment=False, embed=None):
"""
Perform a forward pass through the model.
Args:
x (torch.Tensor): The input tensor.
profile (bool, optional): If True, profile the computation time for each layer. Defaults to False.
visualize (bool, optional): If True, save feature maps for visualization. Defaults to False.
batch (dict, optional): Ground truth data for evaluation. Defaults to None.
augment (bool, optional): If True, perform data augmentation during inference. Defaults to False.
embed (list, optional): A list of feature vectors/embeddings to return.
Returns:
(torch.Tensor): Model's output tensor.
"""
y, dt, embeddings = [], [], [] # outputs
for m in self.model[:-1]: # except the head part
if m.f != -1: # if not from previous layer
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
if profile:
self._profile_one_layer(m, x, dt)
if hasattr(m, 'backbone'):
x = m(x)
if len(x) != 5: # 0 - 5
x.insert(0, None)
for index, i in enumerate(x):
if index in self.save:
y.append(i)
else:
y.append(None)
x = x[-1] # 最后一个输出传给下一层
else:
x = m(x) # run
y.append(x if m.i in self.save else None) # save output
if visualize:
feature_visualization(x, m.type, m.i, save_dir=visualize)
if embed and m.i in embed:
embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
if m.i == max(embed):
return torch.unbind(torch.cat(embeddings, 1), dim=0)
head = self.model[-1]
x = head([y[j] for j in head.f], batch) # head inference
return x4.8 修改八
我们将下面的s用640替换即可，这一步也是部分的主干可以不修改，但有的不修改就会报错，所以我们还是修改一下。
4.9 RT-DETR不能打印计算量问题的解决
计算的GFLOPs计算异常不打印，所以需要额外修改一处， 我们找到如下文件'ultralytics/utils/torch_utils.py'文件内有如下的代码按照如下的图片进行修改，大家看好函数就行，其中红框的640可能和你的不一样， 然后用我给的代码替换掉整个代码即可。
def get_flops(model, imgsz=640):
"""Return a YOLO model's FLOPs."""
try:
model = de_parallel(model)
p = next(model.parameters())
# stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride
stride = 640
im = torch.empty((1, 3, stride, stride), device=p.device) # input image in BCHW format
flops = thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1E9 * 2 if thop else 0 # stride GFLOPs
imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
return flops * imgsz[0] / stride * imgsz[1] / stride # 640x640 GFLOPs
except Exception:
return 0
4.10 可选修改
有些读者的数据集部分图片比较特殊，在验证的时候会导致形状不匹配的报错，如果大家在验证的时候报错形状不匹配的错误可以固定验证集的图片尺寸，方法如下 ->
找到下面这个文件ultralytics/models/yolo/detect/train.py然后其中有一个类是DetectionTrainer class中的build_dataset函数中的一个参数rect=mode == 'val'改为rect=False
五、EfficientformerV2的yaml文件
5.1 yaml文件
大家复制下面的yaml文件，然后通过我给大家的运行代码运行即可，RT-DETR的调参部分需要后面的文章给大家讲，现在目前免费给大家看这一部分不开放。
# Ultralytics YOLO , AGPL-3.0 license
# RT-DETR-l object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
# [depth, width, max_channels]
l: [1.00, 1.00, 1024]
# 支持下面的版本均可替换
# ['efficientformerv2_s0', 'efficientformerv2_s1', 'efficientformerv2_s2', 'efficientformerv2_l']'
backbone:
# [from, repeats, module, args]
- [-1, 1, efficientformerv2_s0, []] # 4
head:
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5 input_proj.2
- [-1, 1, AIFI, [1024, 8]] # 6
- [-1, 1, Conv, [256, 1, 1]] # 7, Y5, lateral_convs.0
- [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 8
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9 input_proj.1
- [[-2, -1], 1, Concat, [1]] # 10
- [-1, 3, RepC3, [256, 0.5]] # 11, fpn_blocks.0
- [-1, 1, Conv, [256, 1, 1]] # 12, Y4, lateral_convs.1
- [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 13
- [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.0
- [[-2, -1], 1, Concat, [1]] # 15 cat backbone P4
- [-1, 3, RepC3, [256, 0.5]] # X3 (16), fpn_blocks.1
- [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
- [[-1, 12], 1, Concat, [1]] # 18 cat Y4
- [-1, 3, RepC3, [256, 0.5]] # F4 (19), pan_blocks.0
- [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
- [[-1, 7], 1, Concat, [1]] # 21 cat Y5
- [-1, 3, RepC3, [256, 0.5]] # F5 (22), pan_blocks.1
- [[16, 19, 22], 1, RTDETRDecoder, [nc, 256, 300, 4, 8, 3]] # Detect(P3, P4, P5)5.2 运行文件
大家可以创建一个train.py文件将下面的代码粘贴进去然后替换你的文件运行即可开始训练。
import warnings
from ultralytics import RTDETR
warnings.filterwarnings('ignore')
if __name__ == '__main__':
model = RTDETR('替换你想要运行的yaml文件')
# model.load('') # 可以加载你的版本预训练权重
model.train(data=r'替换你的数据集地址即可',
cache=False,
imgsz=640,
epochs=72,
batch=4,
workers=0,
device='0',
project='runs/RT-DETR-train',
name='exp',
# amp=True
)5.3 成功训练截图
下面是成功运行的截图（确保我的改进机制是可用的），已经完成了有1个epochs的训练，图片太大截不全第2个epochs了。
六、全文总结
从今天开始正式开始更新RT-DETR剑指论文专栏，本专栏的内容会迅速铺开，在短期呢大量更新，价格也会乘阶梯性上涨，所以想要和我一起学习RT-DETR改进，可以在前期直接关注，本文专栏旨在打造全网最好的RT-DETR专栏为想要发论文的家进行服务。
专栏链接：RT-DETR剑指论文专栏，持续复现各种顶会内容——论文收割机RT-DETR
