一、前言
MobileOne论文：https://arxiv.org/abs/2206.04040
MobileOne github：https://github.com/apple/ml-mobileone
二、基本原理
使用Reparameterize重参数化实现模型的轻量化，基本模块如下图所示。
三、改进方法
说明： 该部分的改进代码尽可能地根据官方代码的写法与YOLOv7项目进行整合；
3.1 改进分析
通过阅读MobileOne源码和结合论文中Table2可以发现以下两点：
（1）Table2中Block Type全写为MobileOne Block，但在源码中的Stage1和后面的Block是稍有不同的，因此在3.2改进YOLOv7时中使用MobileOne Block和MobileOne进行区分；
（2）源码将Stage4和Stage5写在了一起，因此在换Backbone时我们也写在一起，因此在yaml中会看到Stage1后面Blocks个数为【2，8，10，1】
3.2 实现步骤
步骤一：构建MobileOneBlock、MobileOne、SEBlock、reparameterize模块
在项目文件中的models/common.py中加入以下代码
#====MobileOne====#
import copy as copy2 # 为防止与common原来引入的copy冲突, for mobileone reparameterize
from typing import Optional, List, Tuple
class SEBlock(nn.Module):
""" Squeeze and Excite module.
https://arxiv.org/pdf/1709.01507.pdf
"""
def __init__(self, in_channels: int, rd_ratio: float = 0.0625) -> None:
""" Construct a Squeeze and Excite Module.
:param in_channels: Number of input channels.
:param rd_ratio: Input channel reduction ratio.
"""
super(SEBlock, self).__init__()
self.reduce = nn.Conv2d(in_channels=in_channels,out_channels=int(in_channels * rd_ratio), kernel_size=1, stride=1, bias=True)
self.expand = nn.Conv2d(in_channels=int(in_channels * rd_ratio),out_channels=in_channels, kernel_size=1, stride=1, bias=True)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
""" Apply forward pass. """
b, c, h, w = inputs.size()
x = F.avg_pool2d(inputs, kernel_size=[h, w])
x = self.reduce(x)
x = F.relu(x)
x = self.expand(x)
x = torch.sigmoid(x)
x = x.view(-1, c, 1, 1)
return inputs * x
class MobileOneBlock(nn.Module):
""" MobileOne building block. https://arxiv.org/pdf/2206.04040.pdf
"""
def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1,
padding: int = 0, dilation: int = 1, groups: int = 1, use_se: bool = False, num_conv_branches: int = 1, inference_mode: bool = False) -> None:
""" Construct a MobileOneBlock module.
:param in_channels: Number of channels in the input.
:param out_channels: Number of channels produced by the block.
:param kernel_size: Size of the convolution kernel.
:param stride: Stride size.
:param padding: Zero-padding size.
:param dilation: Kernel dilation factor.
:param groups: Group number.
:param inference_mode: If True, instantiates model in inference mode.
:param use_se: Whether to use SE-ReLU activations.
:param num_conv_branches: Number of linear conv branches.
"""
super(MobileOneBlock, self).__init__()
self.inference_mode = inference_mode
self.groups = groups
self.stride = stride
self.kernel_size = kernel_size
self.in_channels = in_channels
self.out_channels = out_channels
self.num_conv_branches = num_conv_branches # 4
# Check if SE-ReLU is requested
if use_se:
self.se = SEBlock(out_channels)
else:
self.se = nn.Identity()
self.activation = nn.ReLU()
if inference_mode:
self.reparam_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True)
else:
# Re-parameterizable skip connection
self.rbr_skip = nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else None # BN skip
# Re-parameterizable conv branches
rbr_conv = list()
for _ in range(self.num_conv_branches):
rbr_conv.append(self._conv_bn(kernel_size=kernel_size, padding=padding))
self.rbr_conv = nn.ModuleList(rbr_conv)
# Re-parameterizable scale branch
self.rbr_scale = None
if kernel_size > 1:
self.rbr_scale = self._conv_bn(kernel_size=1, padding=0)
def forward(self, x: torch.Tensor) -> torch.Tensor:
""" Apply forward pass. """
# Inference mode forward pass.
if self.inference_mode:
return self.activation(self.se(self.reparam_conv(x)))
# Multi-branched train-time forward pass.
# Skip branch output
identity_out = 0
if self.rbr_skip is not None:
identity_out = self.rbr_skip(x)
# Scale branch output
scale_out = 0
if self.rbr_scale is not None:
scale_out = self.rbr_scale(x)
# Other branches
out = scale_out + identity_out
for ix in range(self.num_conv_branches):
out += self.rbr_conv[ix](x)
return self.activation(self.se(out))
def reparameterize(self):
""" Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
architecture used at training time to obtain a plain CNN-like structure
for inference.
"""
if self.inference_mode:
return
kernel, bias = self._get_kernel_bias()
self.reparam_conv = nn.Conv2d(in_channels=self.rbr_conv[0].conv.in_channels,
out_channels=self.rbr_conv[0].conv.out_channels,
kernel_size=self.rbr_conv[0].conv.kernel_size,
stride=self.rbr_conv[0].conv.stride,
padding=self.rbr_conv[0].conv.padding,
dilation=self.rbr_conv[0].conv.dilation,
groups=self.rbr_conv[0].conv.groups,
bias=True)
self.reparam_conv.weight.data = kernel
self.reparam_conv.bias.data = bias
# Delete un-used branches
for para in self.parameters():
para.detach_()
self.__delattr__('rbr_conv')
self.__delattr__('rbr_scale')
if hasattr(self, 'rbr_skip'):
self.__delattr__('rbr_skip')
self.inference_mode = True
def _get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]:
""" Method to obtain re-parameterized kernel and bias.
Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83
:return: Tuple of (kernel, bias) after fusing branches.
"""
# get weights and bias of scale branch
kernel_scale = 0
bias_scale = 0
if self.rbr_scale is not None:
kernel_scale, bias_scale = self._fuse_bn_tensor(self.rbr_scale)
# Pad scale branch kernel to match conv branch kernel size.
pad = self.kernel_size // 2
kernel_scale = torch.nn.functional.pad(kernel_scale, [pad, pad, pad, pad])
# get weights and bias of skip branch
kernel_identity = 0
bias_identity = 0
if self.rbr_skip is not None:
kernel_identity, bias_identity = self._fuse_bn_tensor(self.rbr_skip)
# get weights and bias of conv branches
kernel_conv = 0
bias_conv = 0
for ix in range(self.num_conv_branches):
_kernel, _bias = self._fuse_bn_tensor(self.rbr_conv[ix])
kernel_conv += _kernel
bias_conv += _bias
kernel_final = kernel_conv + kernel_scale + kernel_identity
bias_final = bias_conv + bias_scale + bias_identity
return kernel_final, bias_final
def _fuse_bn_tensor(self, branch) -> Tuple[torch.Tensor, torch.Tensor]:
""" Method to fuse batchnorm layer with preceeding conv layer.
Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
:param branch:
:return: Tuple of (kernel, bias) after fusing batchnorm.
"""
if isinstance(branch, nn.Sequential):
kernel = branch.conv.weight
running_mean = branch.bn.running_mean
running_var = branch.bn.running_var
gamma = branch.bn.weight
beta = branch.bn.bias
eps = branch.bn.eps
else:
assert isinstance(branch, nn.BatchNorm2d)
if not hasattr(self, 'id_tensor'):
input_dim = self.in_channels // self.groups
kernel_value = torch.zeros((self.in_channels, input_dim, self.kernel_size, self.kernel_size),
dtype=branch.weight.dtype, device=branch.weight.device)
for i in range(self.in_channels):
kernel_value[i, i % input_dim,self.kernel_size // 2, self.kernel_size // 2] = 1
self.id_tensor = kernel_value
kernel = self.id_tensor
running_mean = branch.running_mean
running_var = branch.running_var
gamma = branch.weight
beta = branch.bias
eps = branch.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1, 1)
return kernel * t, beta - running_mean * gamma / std
def _conv_bn(self, kernel_size: int, padding: int) -> nn.Sequential:
""" Helper method to construct conv-batchnorm layers.
:param kernel_size: Size of the convolution kernel.
:param padding: Zero-padding size.
:return: Conv-BN module.
"""
mod_list = nn.Sequential()
mod_list.add_module('conv', nn.Conv2d(in_channels=self.in_channels,out_channels=self.out_channels,
kernel_size=kernel_size, stride=self.stride, padding=padding, groups=self.groups, bias=False))
mod_list.add_module('bn', nn.BatchNorm2d(num_features=self.out_channels))
return mod_list
class MobileOne(nn.Module):
""" MobileOne Model https://arxiv.org/pdf/2206.04040.pdf """
def __init__(self,
in_channels, out_channels,
num_blocks_per_stage = 2, num_conv_branches: int = 1,
use_se: bool = False, num_se: int = 0,
inference_mode: bool = False, ) -> None:
""" Construct MobileOne model.
:param num_blocks_per_stage: List of number of blocks per stage.
:param num_classes: Number of classes in the dataset.
:param width_multipliers: List of width multiplier for blocks in a stage.
:param inference_mode: If True, instantiates model in inference mode.
:param use_se: Whether to use SE-ReLU activations.
:param num_conv_branches: Number of linear conv branches.
"""
super().__init__()
self.inference_mode = inference_mode
self.use_se = use_se
self.num_conv_branches = num_conv_branches
self.stage = self._make_stage(in_channels, out_channels, num_blocks_per_stage, num_se_blocks= num_se if use_se else 0)
# planes指输出通道
def _make_stage(self, in_channels, out_channels, num_blocks: int, num_se_blocks: int) -> nn.Sequential:
""" Build a stage of MobileOne model.
:param planes: Number of output channels.
:param num_blocks: Number of blocks in this stage.
:param num_se_blocks: Number of SE blocks in this stage.
:return: A stage of MobileOne model.
"""
# Get strides for all layers
strides = [2] + [1]*(num_blocks-1)
blocks = []
for ix, stride in enumerate(strides): # 用于训练几个blocks
use_se = False
if num_se_blocks > num_blocks:
raise ValueError("Number of SE blocks cannot " "exceed number of layers.")
if ix >= (num_blocks - num_se_blocks):
use_se = True
# Depthwise conv
blocks.append(MobileOneBlock(in_channels=in_channels, out_channels=in_channels,
kernel_size=3, stride=stride, padding=1, groups=in_channels,
inference_mode=self.inference_mode, use_se=use_se, num_conv_branches=self.num_conv_branches))
# Pointwise conv
blocks.append(MobileOneBlock(in_channels=in_channels, out_channels=out_channels,
kernel_size=1, stride=1, padding=0, groups=1,
inference_mode=self.inference_mode, use_se=use_se, num_conv_branches=self.num_conv_branches))
in_channels = out_channels
return nn.Sequential(*blocks)
def forward(self, x: torch.Tensor) -> torch.Tensor:
""" Apply forward pass. """
x = self.stage(x)
return x
def reparameterize_model(model: torch.nn.Module) -> nn.Module:
""" Method returns a model where a multi-branched structure
used in training is re-parameterized into a single branch
for inference.
:param model: MobileOne model in train mode.
:return: MobileOne model in inference mode.
"""
# Avoid editing original graph
model = copy2.deepcopy(model)
for module in model.modules():
if hasattr(module, 'reparameterize'):
module.reparameterize()
return model
步骤二：在yolo.py的parse_model添加Mobileone的构建块
elif m in [MobileOneBlock, MobileOne]:
c1, c2 = ch[f], args[0]
args = [c1, c2, *args[1:]]
步骤三：创建新的模型文件
此处以更换yolov7-tiny的backbone为例，且修改为mobileone中的ms0模型，命名yolov7-tiny-ms0.yaml
# parameters
nc: 3 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# yolov7-tiny backbone
backbone:
# [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True
[
[-1, 1, MobileOneBlock, [48, 3, 2, 1]], # 0
[-1, 1, MobileOne, [48, 2, 4, False, 0]], # MobileOne [out_channels, num_blocks, num_conv_branches, use_se, num_se, inference_mode]
[-1, 1, MobileOne, [128, 8, 4, False, 0]],
[-1, 1, MobileOne, [256, 10, 4, False, 0]],
[ -1, 1, MobileOne, [512, 1, 4, False, 0]], # 4
]
# yolov7-tiny head
head:
[[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, SP, [5]],
[-2, 1, SP, [9]],
[-3, 1, SP, [13]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -7], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 13
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[3, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 23
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2], 1, Concat, [1]], # 27
[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 33
[-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]],
[[-1, 23], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 41
[-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]],
[[-1, 13], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 49
[33, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[41, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[49, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]], # 52
[[50,51,52], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
]
步骤五：推理部分reparameterize
在yolo.py文件中的Model类中的fuse方法，加入MobileOne和MobileOneBlock部分
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
print('Fusing layers... ')
for m in self.model.modules():
if isinstance(m, RepConv):
#print(f" fuse_repvgg_block")
m.fuse_repvgg_block()
elif isinstance(m, RepConv_OREPA):
#print(f" switch_to_deploy")
m.switch_to_deploy()
#======该部分
elif isinstance(m, (MobileOne, MobileOneBlock)) and hasattr(m, 'reparameterize'):
m.reparameterize()
#=======
elif type(m) is Conv and hasattr(m, 'bn'):
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
delattr(m, 'bn') # remove batchnorm
m.forward = m.fuseforward # update forward
elif isinstance(m, (IDetect, IAuxDetect)):
m.fuse()
m.forward = m.fuseforward
self.info()
return self
完成以上5步就可以正常开始训练和测试了~
四、预训练权重
该部分的与训练权重是在MobileOne官方的MobileOne-ms0的官方预训练权重，已兼容YOLOv7项目。
link：https://github.com/uniquechow/YOLO_series_doc/tree/main/lightweight/MobileOne
若有其他问题，可私信交流~~~
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