一、本文介绍
本文给大家带来的是主干网络RevColV1，翻译过来就是可逆列网络去发表于ICLR2022，其是一种新型的神经网络设计(和以前的网络结构的传播方式不太一样)，由多个子网络（列）通过多级可逆连接组成。这种设计允许在前向传播过程中特征解耦，保持总信息无压缩或丢弃。其非常适合数据集庞大的目标检测任务，数据集数量越多其效果性能越好，亲测在包含1000个图片的数据集上其涨点效果就非常明显了，大家可以多动手尝试，其RevColV2的论文同时已经发布如果代码开源我也会第一时间给大家上传。
推荐指数：⭐⭐⭐⭐⭐
涨点效果：⭐⭐⭐⭐⭐
专栏回顾：YOLOv8改进系列专栏——本专栏持续复习各种顶会内容——科研必备
训练结果对比图->
我这里数据集非常小只有不到一千张图片，同时我用的最微量的版本，涨点幅度还算可以，大家可以尝试其中比较高的版本，大家数据集的图片估计也多效果估计会有一个比较大的提升。
目录
一、本文介绍
二、RevColV1的框架原理
2.1 RevColV1的基本原理
2.1.1 可逆连接设计
2.1.2 特征解耦
2.2 RevColV1的表现
三、RevColV1的核心代码
四、手把手教你添加RevColV1机制
修改一
修改二
修改三
修改四
修改五
修改六
修改七
修改八
五、RevColV1的yaml文件
六、成功运行记录
七、本文总结
二、RevColV1的框架原理
官方论文地址： 官方论文地址
官方代码地址： 官方代码地址
2.1 RevColV1的基本原理
RevCol的主要原理和思想是利用可逆连接来设计网络结构，允许信息在网络的不同分支（列）间自由流动而不丢失。这种多列结构在前向传播过程中逐渐解耦特征，并保持全部信息，而不是进行压缩或舍弃。这样的设计提高了网络在图像分类、对象检测和语义分割等计算机视觉任务中的表现，尤其是在参数量大和数据集大时。
RevCol的创新点我将其总结为以下几点：
1. 可逆连接设计：通过多个子网络（列）间的可逆连接，保证信息在前向传播过程中不丢失。
2. 特征解耦：在每个列中，特征逐渐被解耦，保持总信息而非压缩或舍弃。
3. 适用于大型数据集和参数：在大型数据集和高参数预算下表现出色。
4. 跨模型应用：可作为宏架构方式，应用于变换器或其他神经网络，改善计算机视觉和NLP任务的性能。
简单总结：RevCol通过其独特的多列结构和可逆连接设计，使得网络能够在处理信息时保持完整性，提高特征处理的效率。这种架构在数据丰富且复杂的情况下尤为有效，且可灵活应用于不同类型的神经网络模型中。
其中的创新点第四点不用叙述了，网络结构可以应用于我们的YOLOv8就是最好的印证。
这是论文中的图片1，展示了传统单列网络（a）与RevCol（b）的信息传播对比。在图(a)中，信息通过一个接一个的层线性传播，每层处理后传递给下一层直至输出。而在图(b)中，RevCol通过多个并行列（Col 1 到 Col N）处理信息，其中可逆连接（蓝色曲线）允许信息在列间传递，保持低级别和语义级别的信息传播。这种结构有助于整个网络维持更丰富的信息，并且每个列都能从其他列中学习到信息，增强了特征的表达和网络的学习能力(但是这种做法导致模型的参数量非常巨大，而且训练速度缓慢计算量比较大)。
2.1.1 可逆连接设计
在RevCol中的可逆连接设计允许多个子网络（称为列）之间进行信息的双向流动。这意味着在前向传播的过程中，每一列都能接收到前一列的信息，并将自己的处理结果传递给下一列，同时能够保留传递过程中的所有信息。这种设计避免了在传统的深度网络中常见的信息丢失问题，特别是在网络层次较深时。因此，RevCol可以在深层网络中维持丰富的特征表示，从而提高了模型对数据的表示能力和学习效率。
这张图片展示了RevCol网络的不同组成部分和信息流动方式。
- 图 (a) 展示了RevNet中的一个可逆单元，标识了不同时间步长的状态。
- 图 (b) 展示了多级可逆单元，所有输入在不同级别上进行信息交换。
- 图 (c) 提供了整个可逆列网络架构的概览，其中包含了简化的多级可逆单元。
整个设计允许信息在网络的不同层级和列之间自由流动，而不会丢失任何信息，这对于深层网络的学习和特征提取是非常有益的(我觉得这里有点类似于Neck部分允许层级之间相互交流信息)。
2.1.2 特征解耦
特征解耦是指在RevCol网络的每个子网络（列）中，特征通过可逆连接传递，同时独立地进行处理和学习。这样，每个列都能保持输入信息的完整性，而不会像传统的深度网络那样，在层与层之间传递时压缩或丢弃信息。随着信息在列中的前进，特征之间的关联性逐渐减弱（解耦），使得网络能够更细致地捕捉并强调重要的特征，这有助于提高模型在复杂任务上的性能和泛化能力。
这张图展示了RevCol网络的一个级别（Level l）的微观设计，以及特征融合模块（Fusion Block）的设计。在图（a）中，展示了ConvNeXt级别的标准结构，包括下采样块和残差块。图（b）中的RevCol级别包含了融合模块、残差块和可逆操作。这里的特征解耦是通过融合模块实现的，该模块接收相邻级别的特征图 ,
作为输入，并将它们融合以生成新的特征表示。这样，不同级别的特征在融合过程中被解耦，每个级别维持其信息而不压缩或舍弃。图（c）详细描述了融合模块的内部结构，它通过上采样和下采样操作处理不同分辨率的特征图，然后将它们线性叠加，形成为ConvNeXt块提供的特征。这种设计让特征在不同分辨率间流动时进行有效融合。
2.2 RevColV1的表现
这张图片展示了伴随着FLOPs的增长TOP1的准确率情况，可以看出RevColV1伴随着FLOPs的增加效果逐渐明显。
三、RevColV1的核心代码
下面的代码是RevColV1的全部代码，其中包含多个版本，但是大家需要注意这个模型训练非常耗时，参数量非常大，但是其特点就是参数量越大效果越好。其使用方式看章节四。
# --------------------------------------------------------
# Reversible Column Networks
# Copyright (c) 2022 Megvii Inc.
# Licensed under The Apache License 2.0 [see LICENSE for details]
# Written by Yuxuan Cai
# --------------------------------------------------------
from typing import Tuple, Any, List
from timm.models.layers import trunc_normal_
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import DropPath
class UpSampleConvnext(nn.Module):
def __init__(self, ratio, inchannel, outchannel):
super().__init__()
self.ratio = ratio
self.channel_reschedule = nn.Sequential(
# LayerNorm(inchannel, eps=1e-6, data_format="channels_last"),
nn.Linear(inchannel, outchannel),
LayerNorm(outchannel, eps=1e-6, data_format="channels_last"))
self.upsample = nn.Upsample(scale_factor=2 ** ratio, mode='nearest')
def forward(self, x):
x = x.permute(0, 2, 3, 1)
x = self.channel_reschedule(x)
x = x = x.permute(0, 3, 1, 2)
return self.upsample(x)
class LayerNorm(nn.Module):
r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
shape (batch_size, height, width, channels) while channels_first corresponds to inputs
with shape (batch_size, channels, height, width).
"""
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_first", elementwise_affine=True):
super().__init__()
self.elementwise_affine = elementwise_affine
if elementwise_affine:
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.eps = eps
self.data_format = data_format
if self.data_format not in ["channels_last", "channels_first"]:
raise NotImplementedError
self.normalized_shape = (normalized_shape,)
def forward(self, x):
if self.data_format == "channels_last":
return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
elif self.data_format == "channels_first":
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
if self.elementwise_affine:
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
class ConvNextBlock(nn.Module):
r""" ConvNeXt Block. There are two equivalent implementations:
(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
We use (2) as we find it slightly faster in PyTorch
Args:
dim (int): Number of input channels.
drop_path (float): Stochastic depth rate. Default: 0.0
layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
"""
def __init__(self, in_channel, hidden_dim, out_channel, kernel_size=3, layer_scale_init_value=1e-6, drop_path=0.0):
super().__init__()
self.dwconv = nn.Conv2d(in_channel, in_channel, kernel_size=kernel_size, padding=(kernel_size - 1) // 2,
groups=in_channel) # depthwise conv
self.norm = nn.LayerNorm(in_channel, eps=1e-6)
self.pwconv1 = nn.Linear(in_channel, hidden_dim) # pointwise/1x1 convs, implemented with linear layers
self.act = nn.GELU()
self.pwconv2 = nn.Linear(hidden_dim, out_channel)
self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((out_channel)),
requires_grad=True) if layer_scale_init_value > 0 else None
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
input = x
x = self.dwconv(x)
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
x = self.norm(x)
x = self.pwconv1(x)
x = self.act(x)
# print(f"x min: {x.min()}, x max: {x.max()}, input min: {input.min()}, input max: {input.max()}, x mean: {x.mean()}, x var: {x.var()}, ratio: {torch.sum(x>8)/x.numel()}")
x = self.pwconv2(x)
if self.gamma is not None:
x = self.gamma * x
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
x = input + self.drop_path(x)
return x
class Decoder(nn.Module):
def __init__(self, depth=[2, 2, 2, 2], dim=[112, 72, 40, 24], block_type=None, kernel_size=3) -> None:
super().__init__()
self.depth = depth
self.dim = dim
self.block_type = block_type
self._build_decode_layer(dim, depth, kernel_size)
self.projback = nn.Sequential(
nn.Conv2d(
in_channels=dim[-1],
out_channels=4 ** 2 * 3, kernel_size=1),
nn.PixelShuffle(4),
)
def _build_decode_layer(self, dim, depth, kernel_size):
normal_layers = nn.ModuleList()
upsample_layers = nn.ModuleList()
proj_layers = nn.ModuleList()
norm_layer = LayerNorm
for i in range(1, len(dim)):
module = [self.block_type(dim[i], dim[i], dim[i], kernel_size) for _ in range(depth[i])]
normal_layers.append(nn.Sequential(*module))
upsample_layers.append(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True))
proj_layers.append(nn.Sequential(
nn.Conv2d(dim[i - 1], dim[i], 1, 1),
norm_layer(dim[i]),
nn.GELU()
))
self.normal_layers = normal_layers
self.upsample_layers = upsample_layers
self.proj_layers = proj_layers
def _forward_stage(self, stage, x):
x = self.proj_layers[stage](x)
x = self.upsample_layers[stage](x)
return self.normal_layers[stage](x)
def forward(self, c3):
x = self._forward_stage(0, c3) # 14
x = self._forward_stage(1, x) # 28
x = self._forward_stage(2, x) # 56
x = self.projback(x)
return x
class SimDecoder(nn.Module):
def __init__(self, in_channel, encoder_stride) -> None:
super().__init__()
self.projback = nn.Sequential(
LayerNorm(in_channel),
nn.Conv2d(
in_channels=in_channel,
out_channels=encoder_stride ** 2 * 3, kernel_size=1),
nn.PixelShuffle(encoder_stride),
)
def forward(self, c3):
return self.projback(c3)
def get_gpu_states(fwd_gpu_devices) -> Tuple[List[int], List[torch.Tensor]]:
# This will not error out if "arg" is a CPU tensor or a non-tensor type because
# the conditionals short-circuit.
fwd_gpu_states = []
for device in fwd_gpu_devices:
with torch.cuda.device(device):
fwd_gpu_states.append(torch.cuda.get_rng_state())
return fwd_gpu_states
def get_gpu_device(*args):
fwd_gpu_devices = list(set(arg.get_device() for arg in args
if isinstance(arg, torch.Tensor) and arg.is_cuda))
return fwd_gpu_devices
def set_device_states(fwd_cpu_state, devices, states) -> None:
torch.set_rng_state(fwd_cpu_state)
for device, state in zip(devices, states):
with torch.cuda.device(device):
torch.cuda.set_rng_state(state)
def detach_and_grad(inputs: Tuple[Any, ...]) -> Tuple[torch.Tensor, ...]:
if isinstance(inputs, tuple):
out = []
for inp in inputs:
if not isinstance(inp, torch.Tensor):
out.append(inp)
continue
x = inp.detach()
x.requires_grad = True
out.append(x)
return tuple(out)
else:
raise RuntimeError(
"Only tuple of tensors is supported. Got Unsupported input type: ", type(inputs).__name__)
def get_cpu_and_gpu_states(gpu_devices):
return torch.get_rng_state(), get_gpu_states(gpu_devices)
class ReverseFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, run_functions, alpha, *args):
l0, l1, l2, l3 = run_functions
alpha0, alpha1, alpha2, alpha3 = alpha
ctx.run_functions = run_functions
ctx.alpha = alpha
ctx.preserve_rng_state = True
ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
"dtype": torch.get_autocast_gpu_dtype(),
"cache_enabled": torch.is_autocast_cache_enabled()}
ctx.cpu_autocast_kwargs = {"enabled": torch.is_autocast_cpu_enabled(),
"dtype": torch.get_autocast_cpu_dtype(),
"cache_enabled": torch.is_autocast_cache_enabled()}
assert len(args) == 5
[x, c0, c1, c2, c3] = args
if type(c0) == int:
ctx.first_col = True
else:
ctx.first_col = False
with torch.no_grad():
gpu_devices = get_gpu_device(*args)
ctx.gpu_devices = gpu_devices
ctx.cpu_states_0, ctx.gpu_states_0 = get_cpu_and_gpu_states(gpu_devices)
c0 = l0(x, c1) + c0 * alpha0
ctx.cpu_states_1, ctx.gpu_states_1 = get_cpu_and_gpu_states(gpu_devices)
c1 = l1(c0, c2) + c1 * alpha1
ctx.cpu_states_2, ctx.gpu_states_2 = get_cpu_and_gpu_states(gpu_devices)
c2 = l2(c1, c3) + c2 * alpha2
ctx.cpu_states_3, ctx.gpu_states_3 = get_cpu_and_gpu_states(gpu_devices)
c3 = l3(c2, None) + c3 * alpha3
ctx.save_for_backward(x, c0, c1, c2, c3)
return x, c0, c1, c2, c3
@staticmethod
def backward(ctx, *grad_outputs):
x, c0, c1, c2, c3 = ctx.saved_tensors
l0, l1, l2, l3 = ctx.run_functions
alpha0, alpha1, alpha2, alpha3 = ctx.alpha
gx_right, g0_right, g1_right, g2_right, g3_right = grad_outputs
(x, c0, c1, c2, c3) = detach_and_grad((x, c0, c1, c2, c3))
with torch.enable_grad(), \
torch.random.fork_rng(devices=ctx.gpu_devices, enabled=ctx.preserve_rng_state), \
torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs), \
torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):
g3_up = g3_right
g3_left = g3_up * alpha3 ##shortcut
set_device_states(ctx.cpu_states_3, ctx.gpu_devices, ctx.gpu_states_3)
oup3 = l3(c2, None)
torch.autograd.backward(oup3, g3_up, retain_graph=True)
with torch.no_grad():
c3_left = (1 / alpha3) * (c3 - oup3) ## feature reverse
g2_up = g2_right + c2.grad
g2_left = g2_up * alpha2 ##shortcut
(c3_left,) = detach_and_grad((c3_left,))
set_device_states(ctx.cpu_states_2, ctx.gpu_devices, ctx.gpu_states_2)
oup2 = l2(c1, c3_left)
torch.autograd.backward(oup2, g2_up, retain_graph=True)
c3_left.requires_grad = False
cout3 = c3_left * alpha3 ##alpha3 update
torch.autograd.backward(cout3, g3_up)
with torch.no_grad():
c2_left = (1 / alpha2) * (c2 - oup2) ## feature reverse
g3_left = g3_left + c3_left.grad if c3_left.grad is not None else g3_left
g1_up = g1_right + c1.grad
g1_left = g1_up * alpha1 ##shortcut
(c2_left,) = detach_and_grad((c2_left,))
set_device_states(ctx.cpu_states_1, ctx.gpu_devices, ctx.gpu_states_1)
oup1 = l1(c0, c2_left)
torch.autograd.backward(oup1, g1_up, retain_graph=True)
c2_left.requires_grad = False
cout2 = c2_left * alpha2 ##alpha2 update
torch.autograd.backward(cout2, g2_up)
with torch.no_grad():
c1_left = (1 / alpha1) * (c1 - oup1) ## feature reverse
g0_up = g0_right + c0.grad
g0_left = g0_up * alpha0 ##shortcut
g2_left = g2_left + c2_left.grad if c2_left.grad is not None else g2_left ## Fusion
(c1_left,) = detach_and_grad((c1_left,))
set_device_states(ctx.cpu_states_0, ctx.gpu_devices, ctx.gpu_states_0)
oup0 = l0(x, c1_left)
torch.autograd.backward(oup0, g0_up, retain_graph=True)
c1_left.requires_grad = False
cout1 = c1_left * alpha1 ##alpha1 update
torch.autograd.backward(cout1, g1_up)
with torch.no_grad():
c0_left = (1 / alpha0) * (c0 - oup0) ## feature reverse
gx_up = x.grad ## Fusion
g1_left = g1_left + c1_left.grad if c1_left.grad is not None else g1_left ## Fusion
c0_left.requires_grad = False
cout0 = c0_left * alpha0 ##alpha0 update
torch.autograd.backward(cout0, g0_up)
if ctx.first_col:
return None, None, gx_up, None, None, None, None
else:
return None, None, gx_up, g0_left, g1_left, g2_left, g3_left
class Fusion(nn.Module):
def __init__(self, level, channels, first_col) -> None:
super().__init__()
self.level = level
self.first_col = first_col
self.down = nn.Sequential(
nn.Conv2d(channels[level - 1], channels[level], kernel_size=2, stride=2),
LayerNorm(channels[level], eps=1e-6, data_format="channels_first"),
) if level in [1, 2, 3] else nn.Identity()
if not first_col:
self.up = UpSampleConvnext(1, channels[level + 1], channels[level]) if level in [0, 1, 2] else nn.Identity()
def forward(self, *args):
c_down, c_up = args
if self.first_col:
x = self.down(c_down)
return x
if self.level == 3:
x = self.down(c_down)
else:
x = self.up(c_up) + self.down(c_down)
return x
class Level(nn.Module):
def __init__(self, level, channels, layers, kernel_size, first_col, dp_rate=0.0) -> None:
super().__init__()
countlayer = sum(layers[:level])
expansion = 4
self.fusion = Fusion(level, channels, first_col)
modules = [ConvNextBlock(channels[level], expansion * channels[level], channels[level], kernel_size=kernel_size,
layer_scale_init_value=1e-6, drop_path=dp_rate[countlayer + i]) for i in
range(layers[level])]
self.blocks = nn.Sequential(*modules)
def forward(self, *args):
x = self.fusion(*args)
x = self.blocks(x)
return x
class SubNet(nn.Module):
def __init__(self, channels, layers, kernel_size, first_col, dp_rates, save_memory) -> None:
super().__init__()
shortcut_scale_init_value = 0.5
self.save_memory = save_memory
self.alpha0 = nn.Parameter(shortcut_scale_init_value * torch.ones((1, channels[0], 1, 1)),
requires_grad=True) if shortcut_scale_init_value > 0 else None
self.alpha1 = nn.Parameter(shortcut_scale_init_value * torch.ones((1, channels[1], 1, 1)),
requires_grad=True) if shortcut_scale_init_value > 0 else None
self.alpha2 = nn.Parameter(shortcut_scale_init_value * torch.ones((1, channels[2], 1, 1)),
requires_grad=True) if shortcut_scale_init_value > 0 else None
self.alpha3 = nn.Parameter(shortcut_scale_init_value * torch.ones((1, channels[3], 1, 1)),
requires_grad=True) if shortcut_scale_init_value > 0 else None
self.level0 = Level(0, channels, layers, kernel_size, first_col, dp_rates)
self.level1 = Level(1, channels, layers, kernel_size, first_col, dp_rates)
self.level2 = Level(2, channels, layers, kernel_size, first_col, dp_rates)
self.level3 = Level(3, channels, layers, kernel_size, first_col, dp_rates)
def _forward_nonreverse(self, *args):
x, c0, c1, c2, c3 = args
c0 = (self.alpha0) * c0 + self.level0(x, c1)
c1 = (self.alpha1) * c1 + self.level1(c0, c2)
c2 = (self.alpha2) * c2 + self.level2(c1, c3)
c3 = (self.alpha3) * c3 + self.level3(c2, None)
return c0, c1, c2, c3
def _forward_reverse(self, *args):
local_funs = [self.level0, self.level1, self.level2, self.level3]
alpha = [self.alpha0, self.alpha1, self.alpha2, self.alpha3]
_, c0, c1, c2, c3 = ReverseFunction.apply(
local_funs, alpha, *args)
return c0, c1, c2, c3
def forward(self, *args):
self._clamp_abs(self.alpha0.data, 1e-3)
self._clamp_abs(self.alpha1.data, 1e-3)
self._clamp_abs(self.alpha2.data, 1e-3)
self._clamp_abs(self.alpha3.data, 1e-3)
if self.save_memory:
return self._forward_reverse(*args)
else:
return self._forward_nonreverse(*args)
def _clamp_abs(self, data, value):
with torch.no_grad():
sign = data.sign()
data.abs_().clamp_(value)
data *= sign
class Classifier(nn.Module):
def __init__(self, in_channels, num_classes):
super().__init__()
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.classifier = nn.Sequential(
nn.LayerNorm(in_channels, eps=1e-6), # final norm layer
nn.Linear(in_channels, num_classes),
)
def forward(self, x):
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
class FullNet(nn.Module):
def __init__(self, channels=[32, 64, 96, 128], layers=[2, 3, 6, 3], num_subnet=5, kernel_size=3, drop_path=0.0,
save_memory=True, inter_supv=True) -> None:
super().__init__()
self.num_subnet = num_subnet
self.inter_supv = inter_supv
self.channels = channels
self.layers = layers
self.stem = nn.Sequential(
nn.Conv2d(3, channels[0], kernel_size=4, stride=4),
LayerNorm(channels[0], eps=1e-6, data_format="channels_first")
)
dp_rate = [x.item() for x in torch.linspace(0, drop_path, sum(layers))]
for i in range(num_subnet):
first_col = True if i == 0 else False
self.add_module(f'subnet{str(i)}', SubNet(
channels, layers, kernel_size, first_col, dp_rates=dp_rate, save_memory=save_memory))
self.apply(self._init_weights)
self.width_list = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]
def forward(self, x):
c0, c1, c2, c3 = 0, 0, 0, 0
x = self.stem(x)
for i in range(self.num_subnet):
c0, c1, c2, c3 = getattr(self, f'subnet{str(i)}')(x, c0, c1, c2, c3)
return [c0, c1, c2, c3]
def _init_weights(self, module):
if isinstance(module, nn.Conv2d):
trunc_normal_(module.weight, std=.02)
nn.init.constant_(module.bias, 0)
elif isinstance(module, nn.Linear):
trunc_normal_(module.weight, std=.02)
nn.init.constant_(module.bias, 0)
##-------------------------------------- Tiny -----------------------------------------
def revcol_tiny(save_memory=True, inter_supv=True, drop_path=0.1, kernel_size=3):
channels = [64, 128, 256, 512]
layers = [2, 2, 4, 2]
num_subnet = 4
return FullNet(channels, layers, num_subnet, drop_path=drop_path, save_memory=save_memory, inter_supv=inter_supv,
kernel_size=kernel_size)
##-------------------------------------- Small -----------------------------------------
def revcol_small(save_memory=True, inter_supv=True, drop_path=0.3, kernel_size=3):
channels = [64, 128, 256, 512]
layers = [2, 2, 4, 2]
num_subnet = 8
return FullNet(channels, layers, num_subnet, drop_path=drop_path, save_memory=save_memory, inter_supv=inter_supv,
kernel_size=kernel_size)
##-------------------------------------- Base -----------------------------------------
def revcol_base(save_memory=True, inter_supv=True, drop_path=0.4, kernel_size=3, head_init_scale=None):
channels = [72, 144, 288, 576]
layers = [1, 1, 3, 2]
num_subnet = 16
return FullNet(channels, layers, num_subnet, drop_path=drop_path, save_memory=save_memory, inter_supv=inter_supv, kernel_size=kernel_size)
##-------------------------------------- Large -----------------------------------------
def revcol_large(save_memory=True, inter_supv=True, drop_path=0.5, kernel_size=3, head_init_scale=None):
channels = [128, 256, 512, 1024]
layers = [1, 2, 6, 2]
num_subnet = 8
return FullNet(channels, layers, num_subnet, drop_path=drop_path, save_memory=save_memory, inter_supv=inter_supv, kernel_size=kernel_size)
##--------------------------------------Extra-Large -----------------------------------------
def revcol_xlarge(save_memory=True, inter_supv=True, drop_path=0.5, kernel_size=3, head_init_scale=None):
channels = [224, 448, 896, 1792]
layers = [1, 2, 6, 2]
num_subnet = 8
return FullNet(channels, layers, num_subnet, drop_path=drop_path, save_memory=save_memory, inter_supv=inter_supv, kernel_size=kernel_size)
# model = revcol_xlarge(True)
# # 示例输入
# input = torch.randn(64, 3, 224, 224)
# output = model(input)
#
# print(len(output))#torch.Size([3, 64, 224, 224])四、手把手教你添加RevColV1机制
这个主干的网络结构添加起来算是所有的改进机制里最麻烦的了，因为有一些网略结构可以用yaml文件搭建出来，有一些网络结构其中的一些细节根本没有办法用yaml文件去搭建，用yaml文件去搭建会损失一些细节部分(而且一个网络结构设计很多细节的结构修改方式都不一样，一个一个去修改大家难免会出错)，所以这里让网络直接返回整个网络，然后修改部分 yolo代码以后就都以这种形式添加了，以后我提出的网络模型基本上都会通过这种方式修改，我也会进行一些模型细节改进。创新出新的网络结构大家直接拿来用就可以的。下面开始添加教程->
(同时每一个后面都有代码，大家拿来复制粘贴替换即可，但是要看好了不要复制粘贴替换多了)
修改一
我们复制网络结构代码到“ultralytics/nn/modules”目录下创建一个py文件复制粘贴进去 ，我这里起的名字是RevColV1。
修改二
找到如下的文件"ultralytics/nn/tasks.py" 在开始的部分导入我们的模型如下图。
from .modules.RevColV1 import revcol_tiny, revcol_base, revcol_xlarge, revcol_large, revcol_small修改三
添加如下两行代码！！！
修改四
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，我这里只添加了部分的版本，大家有兴趣这个RevColV1还有更多的版本可以添加，看我给的代码函数头即可。
elif m in {自行添加对应的模型即可，下面都是一样的}:
m = m()
c2 = m.width_list # 返回通道列表
backbone = True修改五
下面的两个红框内都是需要改动的。
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修改六
如下的也需要修改，全部按照我的来。
代码如下把原先的代码替换了即可。
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)修改七
修改七和前面的都不太一样，需要修改前向传播中的一个部分， 已经离开了parse_model方法了。
可以在图片中开代码行数，没有离开task.py文件都是同一个文件。 同时这个部分有好几个前向传播都很相似，大家不要看错了，是70多行左右的！！！，同时我后面提供了代码，大家直接复制粘贴即可，有时间我针对这里会出一个视频。
代码如下->
def _predict_once(self, x, profile=False, visualize=False):
"""
Perform a forward pass through the network.
Args:
x (torch.Tensor): The input tensor to the model.
profile (bool): Print the computation time of each layer if True, defaults to False.
visualize (bool): Save the feature maps of the model if True, defaults to False.
Returns:
(torch.Tensor): The last output of the model.
"""
y, dt = [], [] # outputs
for m in self.model:
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)
return x到这里就完成了修改部分，但是这里面细节很多，大家千万要注意不要替换多余的代码，导致报错，也不要拉下任何一部，都会导致运行失败，而且报错很难排查！！！很难排查！！！
修改八
我们找到如下文件'ultralytics/utils/torch_utils.py'按照如下的图片进行修改，否则容易打印不出来计算量。
五、RevColV1的yaml文件
复制如下yaml文件进行运行！！！
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 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=yolov8n.yaml' will call yolov8.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOP
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, revcol_tiny, []] # 4
- [-1, 1, SPPF, [1024, 5]] # 5
# YOLOv8.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 6
- [[-1, 3], 1, Concat, [1]] # 7 cat backbone P4
- [-1, 3, C2f, [512]] # 8
- [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 9
- [[-1, 2], 1, Concat, [1]] # 10 cat backbone P3
- [-1, 3, C2f, [256]] # 11 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]] # 12
- [[-1, 8], 1, Concat, [1]] # 13 cat head P4
- [-1, 3, C2f, [512]] # 14 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]] # 15
- [[-1, 5], 1, Concat, [1]] # 16 cat head P5
- [-1, 3, C2f, [1024]] # 17 (P5/32-large)
- [[11, 14, 17], 1, Detect, [nc]] # Detect(P3, P4, P5)
六、成功运行记录
下面是成功运行的截图，已经完成了有1个epochs的训练，图片太大截不全第2个epochs了。
七、本文总结
到此本文的正式分享内容就结束了，在这里给大家推荐我的YOLOv8改进有效涨点专栏，本专栏目前为新开的平均质量分98分，后期我会根据各种最新的前沿顶会进行论文复现，也会对一些老的改进机制进行补充，目前本专栏免费阅读(暂时，大家尽早关注不迷路~)，如果大家觉得本文帮助到你了，订阅本专栏，关注后续更多的更新~
专栏回顾：YOLOv8改进系列专栏——本专栏持续复习各种顶会内容——科研必备
