【YOLO改进】主干插入SKAttention模块(基于MMYOLO)

SKAttention模块

论文链接:https://arxiv.org/pdf/1903.06586.pdf

将SKAttention模块添加到MMYOLO中

1. 将开源代码SK.py文件复制到mmyolo/models/plugins目录下

2. 导入MMYOLO用于注册模块的包: from mmyolo.registry import MODELS

3. 确保 class SKAttention中的输入维度为in_channels(因为MMYOLO会提前传入输入维度参数，所以要保持参数名的一致)

4. 利用@MODELS.register_module()将“class SKAttention(nn.Module)”注册:

5. 修改mmyolo/models/plugins/__init__.py文件

6. 在终端运行:

   python setup.py install

7. 修改对应的配置文件，并且将plugins的参数“type”设置为“BiLevelRoutingAttention”，可参考【YOLO改进】主干插入注意力机制模块CBAM（基于MMYOLO）-CSDN博客

修改后的SK.py

from collections import OrderedDict
import torch
from torch import nn
from mmyolo.registry import MODELS
 
@MODELS.register_module()
class SKAttention(nn.Module):
 
    def __init__(self, in_channels=512, kernels=[1, 3, 5, 7], reduction=16, group=1, L=32):
        super().__init__()
        self.d = max(L, in_channels // reduction)
        self.convs = nn.ModuleList([])
        for k in kernels:
            self.convs.append(
                nn.Sequential(OrderedDict([
                    ('conv', nn.Conv2d(in_channels, in_channels, kernel_size=k, padding=k // 2, groups=group)),
                    ('bn', nn.BatchNorm2d(in_channels)),
                    ('relu', nn.ReLU())
                ]))
            )
        self.fc = nn.Linear(in_channels, self.d)
        self.fcs = nn.ModuleList([])
        for i in range(len(kernels)):
            self.fcs.append(nn.Linear(self.d, in_channels))
        self.softmax = nn.Softmax(dim=0)
 
    def forward(self, x):
        bs, c, _, _ = x.size()
        conv_outs = []
        ### split
        for conv in self.convs:
            conv_outs.append(conv(x))
        feats = torch.stack(conv_outs, 0)  # k,bs,channel,h,w
 
        ### fuse
        U = sum(conv_outs)  # bs,c,h,w
 
        ### reduction channel
        S = U.mean(-1).mean(-1)  # bs,c
        Z = self.fc(S)  # bs,d
 
        ### calculate attention weight
        weights = []
        for fc in self.fcs:
            weight = fc(Z)
            weights.append(weight.view(bs, c, 1, 1))  # bs,channel
        attention_weughts = torch.stack(weights, 0)  # k,bs,channel,1,1
        attention_weughts = self.softmax(attention_weughts)  # k,bs,channel,1,1
 
        ### fuse
        V = (attention_weughts * feats).sum(0)
        return V
 
 
if __name__ == '__main__':
    input = torch.randn(50, 512, 7, 7)
    se = SKAttention(in_channels=512, reduction=8)
    output = se(input)
    print(output.shape)

修改后的__init__.py

# Copyright (c) OpenMMLab. All rights reserved.
from .cbam import CBAM
from .Biformer import BiLevelRoutingAttention
from .A2Attention import DoubleAttention
from .CoordAttention import CoordAtt
from .CoTAttention import CoTAttention
from .ECA import ECAAttention
from .EffectiveSE import EffectiveSEModule
from .EMA import EMA
from .GC import GlobalContext
from .GE import GatherExcite
from .MHSA import MHSA
from .ParNetAttention import ParNetAttention
from .PolarizedSelfAttention import ParallelPolarizedSelfAttention
from .S2Attention import S2Attention
from .SE import SEAttention
from .SequentialSelfAttention import SequentialPolarizedSelfAttention
from .SGE import SpatialGroupEnhance
from .ShuffleAttention import ShuffleAttention
from .SimAM import SimAM
from .SK import SKAttention
__all__ = ['CBAM', 'BiLevelRoutingAttention', 'DoubleAttention', 'CoordAtt','CoTAttention','ECAAttention', 'EffectiveSEModule', 'EMA',
           'GlobalContext', 'GatherExcite', 'MHSA', 'ParNetAttention','ParallelPolarizedSelfAttention','S2Attention','SEAttention',
           'SequentialPolarizedSelfAttention','SpatialGroupEnhance','ShuffleAttention','SimAM','SKAttention']

修改后的配置文件（以configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py为例）

_base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']
 
# ========================Frequently modified parameters======================
# -----data related-----
data_root = 'data/coco/'  # Root path of data
# Path of train annotation file
train_ann_file = 'annotations/instances_train2017.json'
train_data_prefix = 'train2017/'  # Prefix of train image path
# Path of val annotation file
val_ann_file = 'annotations/instances_val2017.json'
val_data_prefix = 'val2017/'  # Prefix of val image path
 
num_classes = 80  # Number of classes for classification
# Batch size of a single GPU during training
train_batch_size_per_gpu = 16
# Worker to pre-fetch data for each single GPU during training
train_num_workers = 8
# persistent_workers must be False if num_workers is 0
persistent_workers = True
 
# -----model related-----
# Basic size of multi-scale prior box
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
]
 
# -----train val related-----
# Base learning rate for optim_wrapper. Corresponding to 8xb16=128 bs
base_lr = 0.01
max_epochs = 300  # Maximum training epochs
 
model_test_cfg = dict(
    # The config of multi-label for multi-class prediction.
    multi_label=True,
    # The number of boxes before NMS
    nms_pre=30000,
    score_thr=0.001,  # Threshold to filter out boxes.
    nms=dict(type='nms', iou_threshold=0.65),  # NMS type and threshold
    max_per_img=300)  # Max number of detections of each image
 
# ========================Possible modified parameters========================
# -----data related-----
img_scale = (640, 640)  # width, height
# Dataset type, this will be used to define the dataset
dataset_type = 'YOLOv5CocoDataset'
# Batch size of a single GPU during validation
val_batch_size_per_gpu = 1
# Worker to pre-fetch data for each single GPU during validation
val_num_workers = 2
 
# Config of batch shapes. Only on val.
# It means not used if batch_shapes_cfg is None.
batch_shapes_cfg = dict(
    type='BatchShapePolicy',
    batch_size=val_batch_size_per_gpu,
    img_size=img_scale[0],
    # The image scale of padding should be divided by pad_size_divisor
    size_divisor=32,
    # Additional paddings for pixel scale
    extra_pad_ratio=0.5)
 
# -----model related-----
# The scaling factor that controls the depth of the network structure
deepen_factor = 0.33
# The scaling factor that controls the width of the network structure
widen_factor = 0.5
# Strides of multi-scale prior box
strides = [8, 16, 32]
num_det_layers = 3  # The number of model output scales
norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)  # Normalization config
 
# -----train val related-----
affine_scale = 0.5  # YOLOv5RandomAffine scaling ratio
loss_cls_weight = 0.5
loss_bbox_weight = 0.05
loss_obj_weight = 1.0
prior_match_thr = 4.  # Priori box matching threshold
# The obj loss weights of the three output layers
obj_level_weights = [4., 1., 0.4]
lr_factor = 0.01  # Learning rate scaling factor
weight_decay = 0.0005
# Save model checkpoint and validation intervals
save_checkpoint_intervals = 10
# The maximum checkpoints to keep.
max_keep_ckpts = 3
# Single-scale training is recommended to
# be turned on, which can speed up training.
env_cfg = dict(cudnn_benchmark=True)
 
# ===============================Unmodified in most cases====================
model = dict(
    type='YOLODetector',
    data_preprocessor=dict(
        type='mmdet.DetDataPreprocessor',
        mean=[0., 0., 0.],
        std=[255., 255., 255.],
        bgr_to_rgb=True),
    backbone=dict(
        ##修改部分
        plugins=[
            dict(cfg=dict(type='SKAttention'),
                 stages=(False, False, False, True))
        ],
        type='YOLOv5CSPDarknet',
        deepen_factor=deepen_factor,
        widen_factor=widen_factor,
        norm_cfg=norm_cfg,
        act_cfg=dict(type='SiLU', inplace=True)
 
    ),
    neck=dict(
        type='YOLOv5PAFPN',
        deepen_factor=deepen_factor,
        widen_factor=widen_factor,
        in_channels=[256, 512, 1024],
        out_channels=[256, 512, 1024],
        num_csp_blocks=3,
        norm_cfg=norm_cfg,
        act_cfg=dict(type='SiLU', inplace=True)),
    bbox_head=dict(
        type='YOLOv5Head',
        head_module=dict(
            type='YOLOv5HeadModule',
            num_classes=num_classes,
            in_channels=[256, 512, 1024],
            widen_factor=widen_factor,
            featmap_strides=strides,
            num_base_priors=3),
        prior_generator=dict(
            type='mmdet.YOLOAnchorGenerator',
            base_sizes=anchors,
            strides=strides),
        # scaled based on number of detection layers
        loss_cls=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=True,
            reduction='mean',
            loss_weight=loss_cls_weight *
            (num_classes / 80 * 3 / num_det_layers)),
        loss_bbox=dict(
            type='IoULoss',
            iou_mode='ciou',
            bbox_format='xywh',
            eps=1e-7,
            reduction='mean',
            loss_weight=loss_bbox_weight * (3 / num_det_layers),
            return_iou=True),
        loss_obj=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=True,
            reduction='mean',
            loss_weight=loss_obj_weight *
            ((img_scale[0] / 640)**2 * 3 / num_det_layers)),
        prior_match_thr=prior_match_thr,
        obj_level_weights=obj_level_weights),
    test_cfg=model_test_cfg)
 
albu_train_transforms = [
    dict(type='Blur', p=0.01),
    dict(type='MedianBlur', p=0.01),
    dict(type='ToGray', p=0.01),
    dict(type='CLAHE', p=0.01)
]
 
pre_transform = [
    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
    dict(type='LoadAnnotations', with_bbox=True)
]
 
train_pipeline = [
    *pre_transform,
    dict(
        type='Mosaic',
        img_scale=img_scale,
        pad_val=114.0,
        pre_transform=pre_transform),
    dict(
        type='YOLOv5RandomAffine',
        max_rotate_degree=0.0,
        max_shear_degree=0.0,
        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
        # img_scale is (width, height)
        border=(-img_scale[0] // 2, -img_scale[1] // 2),
        border_val=(114, 114, 114)),
    dict(
        type='mmdet.Albu',
        transforms=albu_train_transforms,
        bbox_params=dict(
            type='BboxParams',
            format='pascal_voc',
            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
        keymap={
            'img': 'image',
            'gt_bboxes': 'bboxes'
        }),
    dict(type='YOLOv5HSVRandomAug'),
    dict(type='mmdet.RandomFlip', prob=0.5),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
                   'flip_direction'))
]
 
train_dataloader = dict(
    batch_size=train_batch_size_per_gpu,
    num_workers=train_num_workers,
    persistent_workers=persistent_workers,
    pin_memory=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=train_ann_file,
        data_prefix=dict(img=train_data_prefix),
        filter_cfg=dict(filter_empty_gt=False, min_size=32),
        pipeline=train_pipeline))
 
test_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
    dict(
        type='LetterResize',
        scale=img_scale,
        allow_scale_up=False,
        pad_val=dict(img=114)),
    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor', 'pad_param'))
]
 
val_dataloader = dict(
    batch_size=val_batch_size_per_gpu,
    num_workers=val_num_workers,
    persistent_workers=persistent_workers,
    pin_memory=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        test_mode=True,
        data_prefix=dict(img=val_data_prefix),
        ann_file=val_ann_file,
        pipeline=test_pipeline,
        batch_shapes_cfg=batch_shapes_cfg))
 
test_dataloader = val_dataloader
 
param_scheduler = None
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(
        type='SGD',
        lr=base_lr,
        momentum=0.937,
        weight_decay=weight_decay,
        nesterov=True,
        batch_size_per_gpu=train_batch_size_per_gpu),
    constructor='YOLOv5OptimizerConstructor')
 
default_hooks = dict(
    param_scheduler=dict(
        type='YOLOv5ParamSchedulerHook',
        scheduler_type='linear',
        lr_factor=lr_factor,
        max_epochs=max_epochs),
    checkpoint=dict(
        type='CheckpointHook',
        interval=save_checkpoint_intervals,
        save_best='auto',
        max_keep_ckpts=max_keep_ckpts))
 
custom_hooks = [
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0001,
        update_buffers=True,
        strict_load=False,
        priority=49)
]
 
val_evaluator = dict(
    type='mmdet.CocoMetric',
    proposal_nums=(100, 1, 10),
    ann_file=data_root + val_ann_file,
    metric='bbox')
test_evaluator = val_evaluator
 
train_cfg = dict(
    type='EpochBasedTrainLoop',
    max_epochs=max_epochs,
    val_interval=save_checkpoint_intervals)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')