首先，mmpose是top-down的关键点检测算法，因此需要前置一个目标检测器，这里我们选用openmmlab官方的mmdetection代码仓库。

（一）MMpose安装以及环境配置

1：在openmmlab的github官网下载mmpose源码：

https://github.com/open-mmlab/mmpose

同样下载mmdetection的源码 ：

https://github.com/open-mmlab/mmdetection

2，下载完代码后需要创建一个新的conda虚拟环境，这里我创建的是openmmlab的虚拟环境，并且需要在虚拟环境中安装以下依赖环境包：

（1）安装pytoch

pip3 install install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

（2）用MM命令安装MMCV

pip install -U openmim
mim install mmengine
mim install 'mmcv>=2.0.0rc3'
mim install "mmdet>=3.0.0rc6"
mim install "mmpose>=1.1.0"

（3）安装其他工具包

pip install opencv-python pillow matplotlib seaborn tqdm pycocotools -i https://pypi.tuna.tsinghua.edu.cn/simple

 3，需要在两个代码仓库中创建checkpoint文件夹与outputs文件夹和data文件夹

# 创建 checkpoint 文件夹，用于存放预训练模型权重文件
os.mkdir('checkpoint')

# 创建 outputs 文件夹，用于存放预测结果
os.mkdir('outputs')

# 创建 data 文件夹，用于存放图片和视频素材
os.mkdir('data')

os.mkdir('data/test')

4，检查是否安装成功

在openmmlab环境中，创建python文件，运行没有报错即为安装成功

import torch, torchvision
import mmcv
import mmpose
from mmcv.ops import get_compiling_cuda_version, get_compiler_version

# 检查 Pytorch
print('Pytorch 版本', torch.__version__)
print('CUDA 是否可用',torch.cuda.is_available())

# 检查 mmcv
print('MMCV版本', mmcv.__version__)
print('CUDA版本', get_compiling_cuda_version())
print('编译器版本', get_compiler_version())

# 检查 mmpose
print('mmpose版本', mmpose.__version__)

（二）我们需要准备自己的数据集，在自己的数据集是COCO格式的数据集

这里可以用labelme标注框与点，然后通过转换脚本来转成coco格式的数据集并将数据集放入我们创建好的mmpose与mmdetection中的data文件夹中

（三） 下载config配置文件

mmpose中的数据集路径指定，预训练模型的指定，训练超参数，模型权重等等都是在config文件中指定。所以需要在data文件夹下添加config文件，mmpose和mmdetection文件夹中都需要。

（1）faster-rcnn目标检测config文件下载地址：

 https://zihao-openmmlab.obs.myhuaweicloud.com/20220610-mmpose/triangle_dataset/faster_r_cnn_triangle.py 

config文件如下：

# 数据集类型及路径
dataset_type = 'CocoDataset'
data_root = 'data/Triangle_215_Keypoint_coco/'
metainfo = {'classes': ('sjb_rect',)}
NUM_CLASSES = len(metainfo['classes'])

# 预训练模型权重
load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'

# 训练超参数
MAX_EPOCHS = 50
TRAIN_BATCH_SIZE = 1
VAL_BATCH_SIZE = 1
VAL_INTERVAL = 5 # 每隔多少轮评估保存一次模型权重
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=MAX_EPOCHS, val_interval=VAL_INTERVAL)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

# Pipeline
backend_args = None
train_pipeline = [
    dict(type='LoadImageFromFile', backend_args=backend_args),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=backend_args),
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
    # If you don't have a gt annotation, delete the pipeline
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]

# DataLoader
train_dataloader = dict(
    batch_size=TRAIN_BATCH_SIZE,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        metainfo=metainfo,
        ann_file='train_coco.json',
        data_prefix=dict(img='images/'),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=train_pipeline,
        backend_args=backend_args))
val_dataloader = dict(
    batch_size=VAL_BATCH_SIZE,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        metainfo=metainfo,
        data_root=data_root,
        ann_file='val_coco.json',
        data_prefix=dict(img='images/'),
        test_mode=True,
        pipeline=test_pipeline,
        backend_args=backend_args))
test_dataloader = val_dataloader

# Evaluator 测试集上的评估指标
# val_evaluator = dict(type='CocoMetric',ann_file=data_root + 'val_coco.json',metric='bbox',format_only=False, backend_args=backend_args)
# val_evaluator = dict(type='VOCMetric', metric='mAP', eval_mode='11points')

val_evaluator = [
    dict(type='CocoMetric',ann_file=data_root + 'val_coco.json',metric='bbox',format_only=False, backend_args=backend_args), 
    dict(type='VOCMetric', metric='mAP', eval_mode='11points')
]

test_evaluator = val_evaluator

# 模型结构
model = dict(
    type='FasterRCNN',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_size_divisor=32),
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=NUM_CLASSES,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0., 0., 0., 0.],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
    # model training and testing settings
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=False,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100)
        # soft-nms is also supported for rcnn testing
        # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
    ))

# 学习率
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
    dict(
        type='MultiStepLR',
        begin=0,
        end=12,
        by_epoch=True,
        milestones=[8, 11],
        gamma=0.1)
]

# 优化器
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))

# Scaling LR automatically
auto_scale_lr = dict(enable=False, base_batch_size=16)

default_scope = 'mmdet'

# Hook
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=1),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=2, save_best='coco/bbox_mAP'), # auto coco/bbox_mAP_50 coco/bbox_mAP_75 coco/bbox_mAP_s
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='DetVisualizationHook'))

env_cfg = dict(
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'),
)

vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)

log_level = 'INFO'
load_from = None
resume = False

如果训练自己的数据集，则需要修改以下这三行：

# 数据集类型及路径
dataset_type = 'CocoDataset'
data_root = 'data/Triangle_215_Keypoint_coco/'
metainfo = {'classes': ('sjb_rect',)}
NUM_CLASSES = len(metainfo['classes'])

dataset_type是数据集的格式，这里我们使用的是coco格式的数据集，因此dataset_type是CocoDataset。

data_root是数据集的路径，这里填自己的数据集路径即可。

metainfo 是框的类别名称，这里填写标注的名称即可。

最后可以改一下超参数中的batch-size，根据显存大小来决定。

（四）在完成上述操作后，我们可以开始训练了，训练要先训练mmdetection目标检测器。建议通过命令行来运行，其实就是执行tools/train.py训练脚本，训练命令如下：

（1）faster-rcnn模型的训练（预测速度较慢，不建议使用）

python tools/train.py data/faster_r_cnn_triangle.py

（2）RTM-det模型的训练（预测速度较快且较为精准，建议使用） 

python tools/train.py data/rtmdet_tiny_triangle.py

（3）在完成训练后会生成workdir文件夹，该文件夹中包含训练日志以及模型的pth文件等。最后我们需要对训练完成的目标检测器进行测试，测试采用tools/test.py这个脚本，需要指定cfg文件以及训练完成的模型pth文件。测试命令如下：

python tools/test.py data/rtmdet_tiny_triangle.py \
                      work_dirs/rtmdet_tiny_triangle/epoch_200.pth

可以使用以下脚本可视化训练日志

import pandas as pd
from tqdm import tqdm
from matplotlib import colors as mcolors
import random

import matplotlib.pyplot as plt
plt.rcParams['axes.unicode_minus']=False  # 用来正常显示负号


# 日志文件路径
log_path = 'work_dirs/faster_r_cnn_triangle/20230511_234855/vis_data/scalars.json'

with open(log_path, "r") as f:
    json_list = f.readlines()


len(json_list)

print(eval(json_list[4]))

df_train = pd.DataFrame()
df_test = pd.DataFrame()
for each in tqdm(json_list):
    if 'coco/bbox_mAP' in each:
        df_test = df_test.append(eval(each), ignore_index=True)
    else:
        df_train = df_train.append(eval(each), ignore_index=True)


print(df_train)
print(df_test)


#导出训练日志表格
df_train.to_csv('训练日志-训练集.csv', index=False)
df_test.to_csv('训练日志-测试集.csv', index=False)

#设置Matplotlib中文字体
# # windows操作系统
# plt.rcParams['font.sans-serif']=['SimHei']  # 用来正常显示中文标签 
# plt.rcParams['axes.unicode_minus']=False  # 用来正常显示负号

# Linux操作系统
import matplotlib 
import matplotlib.pyplot as plt
matplotlib.rc("font",family='SimHei') # 中文字体

#测试中文字体是否设置成功
plt.plot([1,2,3], [100,500,300])
plt.title('matplotlib中文字体测试', fontsize=25)
plt.xlabel('X轴', fontsize=15)
plt.ylabel('Y轴', fontsize=15)
plt.show()

#可视化辅助函数
random.seed(124)
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'black', 'indianred', 'brown', 'firebrick', 'maroon', 'darkred', 'red', 'sienna', 'chocolate', 'yellow', 'olivedrab', 'yellowgreen', 'darkolivegreen', 'forestgreen', 'limegreen', 'darkgreen', 'green', 'lime', 'seagreen', 'mediumseagreen', 'darkslategray', 'darkslategrey', 'teal', 'darkcyan', 'dodgerblue', 'navy', 'darkblue', 'mediumblue', 'blue', 'slateblue', 'darkslateblue', 'mediumslateblue', 'mediumpurple', 'rebeccapurple', 'blueviolet', 'indigo', 'darkorchid', 'darkviolet', 'mediumorchid', 'purple', 'darkmagenta', 'fuchsia', 'magenta', 'orchid', 'mediumvioletred', 'deeppink', 'hotpink']
markers = [".",",","o","v","^","<",">","1","2","3","4","8","s","p","P","*","h","H","+","x","X","D","d","|","_",0,1,2,3,4,5,6,7,8,9,10,11]
linestyle = ['--', '-.', '-']

def get_line_arg():
    '''
    随机产生一种绘图线型
    '''
    line_arg = {}
    line_arg['color'] = random.choice(colors)
    # line_arg['marker'] = random.choice(markers)
    line_arg['linestyle'] = random.choice(linestyle)
    line_arg['linewidth'] = random.randint(1, 4)
    # line_arg['markersize'] = random.randint(3, 5)
    return line_arg


#训练集损失函数

print(df_train.columns)

metrics = ['loss', 'loss_bbox', 'loss_cls', 'loss_rpn_cls', 'loss_rpn_bbox']

plt.figure(figsize=(16, 8))

x = df_train['step']
for y in metrics:
    plt.plot(x, df_train[y], label=y, **get_line_arg())

plt.tick_params(labelsize=20)
plt.xlabel('step', fontsize=20)
plt.ylabel('loss', fontsize=20)
plt.title('训练集损失函数', fontsize=25)
plt.savefig('训练集损失函数.pdf', dpi=120, bbox_inches='tight')

plt.legend(fontsize=20)

plt.show()



#训练集准确率
metrics = ['acc']

plt.figure(figsize=(16, 8))

x = df_train['step']
for y in metrics:
    plt.plot(x, df_train[y], label=y, **get_line_arg())

plt.tick_params(labelsize=20)
plt.xlabel('step', fontsize=20)
plt.ylabel('loss', fontsize=20)
plt.title('训练集准确率', fontsize=25)
plt.savefig('训练集准确率.pdf', dpi=120, bbox_inches='tight')

plt.legend(fontsize=20)

plt.show()



#测试集评估指标-MS COCO Metric

print(df_test.columns)

metrics = ['coco/bbox_mAP', 'coco/bbox_mAP_50', 'coco/bbox_mAP_75', 'coco/bbox_mAP_s', 'coco/bbox_mAP_m', 'coco/bbox_mAP_l']

plt.figure(figsize=(16, 8))

x = df_test['step']
for y in metrics:
    plt.plot(x, df_test[y], label=y, **get_line_arg())

plt.tick_params(labelsize=20)
# plt.ylim([0, 100])
plt.xlabel('Epoch', fontsize=20)
plt.ylabel(y, fontsize=20)
plt.title('测试集评估指标', fontsize=25)
plt.savefig('测试集分类评估指标.pdf', dpi=120, bbox_inches='tight')

plt.legend(fontsize=20)

plt.show()


#测试集评估指标-PASCAL VOC Metric

metrics = ['pascal_voc/mAP', 'pascal_voc/AP50']

plt.figure(figsize=(16, 8))

x = df_test['step']
for y in metrics:
    plt.plot(x, df_test[y], label=y, **get_line_arg())

plt.tick_params(labelsize=20)
# plt.ylim([0, 100])
plt.xlabel('Epoch', fontsize=20)
plt.ylabel(y, fontsize=20)
plt.title('测试集评估指标', fontsize=25)
plt.savefig('测试集分类评估指标.pdf', dpi=120, bbox_inches='tight')

plt.legend(fontsize=20)

plt.show()

（五）在完成上述操作后，我们需要对模型进行精简转换，经过精简转换之后，模型.pth权重文件大小缩小为原来的一半以上，但不影响推理结果和推理速度。具体是通过python tools/model_converters/publish_model.py 这个脚本来实现的，需要指定模型的pth文件以及config文件。执行命令如下：

（1）faster-rcnn的模型精简转换脚本命令

python tools/model_converters/publish_model.py \
        work_dirs/faster_r_cnn_triangle/epoch_50.pth \
        checkpoint/faster_r_cnn_triangle_epoch_50_202305120846.pth

（2）RTMdet-tiny的模型精简转换脚本命令

python tools/model_converters/publish_model.py \
        work_dirs/rtmdet_tiny_triangle/epoch_200.pth \
        checkpoint/rtmdet_tiny_triangle_epoch_200_202305120847.pth

（六）下载mmpose的congfig文件，需要将该文件放在mmpose的data目录下。下载网址如下：

https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220610-mmpose/triangle_dataset/rtmpose-s_triangle_8xb256-420e_coco-256x192.py

下子完成后同样需要修改前三行和batch-size 和训练元数据，mmpose的config文件如下：

_base_ = ['mmpose::_base_/default_runtime.py']

# 数据集类型及路径
dataset_type = 'CocoDataset'
data_mode = 'topdown'
data_root = 'data/Triangle_215_Keypoint_coco/'

# 三角板关键点检测数据集-元数据
dataset_info = {
    'dataset_name':'Triangle_215_Keypoint_coco',
    'classes':'sjb_rect',
    'paper_info':{
        'author':'Tongji Zihao',
        'title':'Triangle Keypoints Detection',
        'container':'OpenMMLab',
        'year':'2023',
        'homepage':'https://space.bilibili.com/1900783'
    },
    'keypoint_info':{
        0:{'name':'angle_30','id':0,'color':[255,0,0],'type': '','swap': ''},
        1:{'name':'angle_60','id':1,'color':[0,255,0],'type': '','swap': ''},
        2:{'name':'angle_90','id':2,'color':[0,0,255],'type': '','swap': ''}
    },
    'skeleton_info': {
        0: {'link':('angle_30','angle_60'),'id': 0,'color': [100,150,200]},
        1: {'link':('angle_60','angle_90'),'id': 1,'color': [200,100,150]},
        2: {'link':('angle_90','angle_30'),'id': 2,'color': [150,120,100]}
    },
    'joint_weights':[1.0, 1.0, 1.0], #对这三个关键点的重视程度是否一样，这里可以自行修改
    'sigmas':[0.026,0.025,0.025] #衡量不同的人在标注过程中的偏差，保持默认即可
}

NUM_KEYPOINTS = len(dataset_info['keypoint_info'])

# 训练超参数
max_epochs = 300 # 训练 epoch 总数
val_interval = 10 # 每隔多少个 epoch 保存一次权重文件
train_cfg = {'max_epochs': max_epochs, 'val_interval': val_interval}
train_batch_size = 16
val_batch_size = 8
stage2_num_epochs = 0
base_lr = 4e-3
randomness = dict(seed=21)

# 优化器
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
    paramwise_cfg=dict(
        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))

# 学习率
param_scheduler = [
    dict(
        type='LinearLR', start_factor=1.0e-5, by_epoch=False, begin=0, end=20),
    dict(
        # use cosine lr from 210 to 420 epoch
        type='CosineAnnealingLR',
        eta_min=base_lr * 0.05,
        begin=max_epochs // 2,
        end=max_epochs,
        T_max=max_epochs // 2,
        by_epoch=True,
        convert_to_iter_based=True),
]

# automatically scaling LR based on the actual training batch size
auto_scale_lr = dict(base_batch_size=1024)

# codec settings
codec = dict(
    type='SimCCLabel',
    input_size=(256, 256), #重要参数
    sigma=(12, 12),#重要参数
    simcc_split_ratio=2.0,
    normalize=False,
    use_dark=False)

# 不同输入图像尺寸的参数搭配
# input_size=(256, 256),
# sigma=(12, 12)
# in_featuremap_size=(8, 8)
# input_size可以换成 256、384、512、1024，三个参数等比例缩放
# sigma 表示关键点一维高斯分布的标准差，越大越容易学习，但精度上限会降低，越小越严格，对于人体、人脸等高精度场景，可以调小，RTMPose 原始论文中为 5.66 调整tinputsize要等比例缩放sigma和 in_featuremap_size

# 不同模型的 config： https://github.com/open-mmlab/mmpose/tree/dev-1.x/projects/rtmpose/rtmpose/body_2d_keypoint

## 模型：RTMPose-S
model = dict(
    type='TopdownPoseEstimator',
    data_preprocessor=dict(
        type='PoseDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True),
    backbone=dict(
        _scope_='mmdet',
        type='CSPNeXt',
        arch='P5',
        expand_ratio=0.5,
        deepen_factor=0.67,
        widen_factor=0.75,
        out_indices=(4, ),
        channel_attention=True,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU'),
        init_cfg=dict(
            type='Pretrained',
            prefix='backbone.',
            checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' +
            'rtmdet/cspnext_rsb_pretrain/cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth',  # noqa E501
        )),
    head=dict(
        type='RTMCCHead',
        in_channels=768,
        out_channels=NUM_KEYPOINTS,
        input_size=codec['input_size'],
        in_featuremap_size=(8, 8), #重要参数
        simcc_split_ratio=codec['simcc_split_ratio'],
        final_layer_kernel_size=7,
        gau_cfg=dict(
            hidden_dims=256,
            s=128,
            expansion_factor=2,
            dropout_rate=0.,
            drop_path=0.,
            act_fn='SiLU',
            use_rel_bias=False,
            pos_enc=False),
        loss=dict(
            type='KLDiscretLoss',
            use_target_weight=True,
            beta=10.,
            label_softmax=True),
        decoder=codec),
    test_cfg=dict(flip_test=True))

## 模型：RTMPose-M
# model = dict(
#     type='TopdownPoseEstimator',
#     data_preprocessor=dict(
#         type='PoseDataPreprocessor',
#         mean=[123.675, 116.28, 103.53],
#         std=[58.395, 57.12, 57.375],
#         bgr_to_rgb=True),
#     backbone=dict(
#         _scope_='mmdet',
#         type='CSPNeXt',
#         arch='P5',
#         expand_ratio=0.5,
#         deepen_factor=0.67,
#         widen_factor=0.75,
#         out_indices=(4, ),
#         channel_attention=True,
#         norm_cfg=dict(type='SyncBN'),
#         act_cfg=dict(type='SiLU'),
#         init_cfg=dict(
#             type='Pretrained',
#             prefix='backbone.',
#             checkpoint='https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth'
#         )),
#     head=dict(
#         type='RTMCCHead',
#         in_channels=768,
#         out_channels=NUM_KEYPOINTS,
#         input_size=codec['input_size'],
#         in_featuremap_size=(8, 8),
#         simcc_split_ratio=codec['simcc_split_ratio'],
#         final_layer_kernel_size=7,
#         gau_cfg=dict(
#             hidden_dims=256,
#             s=128,
#             expansion_factor=2,
#             dropout_rate=0.,
#             drop_path=0.,
#             act_fn='SiLU',
#             use_rel_bias=False,
#             pos_enc=False),
#         loss=dict(
#             type='KLDiscretLoss',
#             use_target_weight=True,
#             beta=10.,
#             label_softmax=True),
#         decoder=codec),
#     test_cfg=dict(flip_test=True))

## 模型：RTMPose-L
# model = dict(
#     type='TopdownPoseEstimator',
#     data_preprocessor=dict(
#         type='PoseDataPreprocessor',
#         mean=[123.675, 116.28, 103.53],
#         std=[58.395, 57.12, 57.375],
#         bgr_to_rgb=True),
#     backbone=dict(
#         _scope_='mmdet',
#         type='CSPNeXt',
#         arch='P5',
#         expand_ratio=0.5,
#         deepen_factor=1.,
#         widen_factor=1.,
#         out_indices=(4, ),
#         channel_attention=True,
#         norm_cfg=dict(type='SyncBN'),
#         act_cfg=dict(type='SiLU'),
#         init_cfg=dict(
#             type='Pretrained',
#             prefix='backbone.',
#             checkpoint='https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-l_8xb256-rsb-a1-600e_in1k-6a760974.pth'
#         )),
#     head=dict(
#         type='RTMCCHead',
#         in_channels=1024,
#         out_channels=NUM_KEYPOINTS,
#         input_size=codec['input_size'],
#         in_featuremap_size=(8, 8),
#         simcc_split_ratio=codec['simcc_split_ratio'],
#         final_layer_kernel_size=7,
#         gau_cfg=dict(
#             hidden_dims=256,
#             s=128,
#             expansion_factor=2,
#             dropout_rate=0.,
#             drop_path=0.,
#             act_fn='SiLU',
#             use_rel_bias=False,
#             pos_enc=False),
#         loss=dict(
#             type='KLDiscretLoss',
#             use_target_weight=True,
#             beta=10.,
#             label_softmax=True),
#         decoder=codec),
#     test_cfg=dict(flip_test=True))


backend_args = dict(backend='local')
# backend_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         f'{data_root}': 's3://openmmlab/datasets/detection/coco/',
#         f'{data_root}': 's3://openmmlab/datasets/detection/coco/'
#     }))

# pipelines
train_pipeline = [
    dict(type='LoadImage', backend_args=backend_args),
    dict(type='GetBBoxCenterScale'),
    dict(type='RandomFlip', direction='horizontal'),
    # dict(type='RandomHalfBody'),
    dict(
        type='RandomBBoxTransform', scale_factor=[0.8, 1.2], rotate_factor=30),
    dict(type='TopdownAffine', input_size=codec['input_size']),
    dict(type='mmdet.YOLOXHSVRandomAug'),
    dict(
        type='Albumentation',
        transforms=[
            dict(type='ChannelShuffle', p=0.5),
            dict(type='CLAHE', p=0.5),
            # dict(type='Downscale', scale_min=0.7, scale_max=0.9, p=0.2),
            dict(type='ColorJitter', p=0.5),
            dict(
                type='CoarseDropout',
                max_holes=4,
                max_height=0.3,
                max_width=0.3,
                min_holes=1,
                min_height=0.2,
                min_width=0.2,
                p=0.5),
        ]),
    dict(type='GenerateTarget', encoder=codec),
    dict(type='PackPoseInputs')
]
# train_pipeline = [
#     dict(type='LoadImage', backend_args=backend_args),
#     dict(type='GetBBoxCenterScale'),
#     dict(type='TopdownAffine', input_size=codec['input_size']),
#     dict(type='GenerateTarget', encoder=codec),
#     dict(type='PackPoseInputs')
# ]
val_pipeline = [
    dict(type='LoadImage', backend_args=backend_args),
    dict(type='GetBBoxCenterScale'),
    dict(type='TopdownAffine', input_size=codec['input_size']),
    dict(type='PackPoseInputs')
]

train_pipeline_stage2 = [
    dict(type='LoadImage', backend_args=backend_args),
    dict(type='GetBBoxCenterScale'),
    dict(type='RandomFlip', direction='horizontal'),
    dict(type='RandomHalfBody'),
    dict(
        type='RandomBBoxTransform',
        shift_factor=0.,
        scale_factor=[0.75, 1.25],
        rotate_factor=60),
    dict(type='TopdownAffine', input_size=codec['input_size']),
    dict(type='mmdet.YOLOXHSVRandomAug'),
    dict(
        type='Albumentation',
        transforms=[
            dict(type='Blur', p=0.1),
            dict(type='MedianBlur', p=0.1),
            dict(
                type='CoarseDropout',
                max_holes=1,
                max_height=0.4,
                max_width=0.4,
                min_holes=1,
                min_height=0.2,
                min_width=0.2,
                p=0.5),
        ]),
    dict(type='GenerateTarget', encoder=codec),
    dict(type='PackPoseInputs')
]

# data loaders
train_dataloader = dict(
    batch_size=train_batch_size,
    num_workers=1,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        metainfo=dataset_info,
        data_mode=data_mode,
        ann_file='train_coco.json',
        data_prefix=dict(img='images/'),
        pipeline=train_pipeline,
    ))
val_dataloader = dict(
    batch_size=val_batch_size,
    num_workers=1,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
    # dataset=dict(
    #     type=dataset_type,
    #     data_root=data_root,
    #     metainfo=dataset_info,
    #     data_mode=data_mode,
    #     ann_file='val_coco.json',
    #     # bbox_file=f'{data_root}person_detection_results/'
    #     # 'COCO_val2017_detections_AP_H_56_person.json',
    #     data_prefix=dict(img='images/'),
    #     test_mode=True,
    #     pipeline=val_pipeline,
    # )
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        metainfo=dataset_info,
        data_mode=data_mode,
        ann_file='val_coco.json',
        data_prefix=dict(img='images/'),
        pipeline=val_pipeline,
    ))
test_dataloader = val_dataloader

# hooks
# default_hooks = dict(
#     checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1),
#     logger=dict(interval=1),
# )

# default_hooks = {
#     'checkpoint': {'save_best': 'coco/AP','rule': 'greater','max_keep_ckpts': 1},
#     'logger': {'interval': 1}
# }

default_hooks = {
    'checkpoint': {'save_best': 'PCK','rule': 'greater','max_keep_ckpts': 2},
    'logger': {'interval': 1}
}

custom_hooks = [
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0002,
        update_buffers=True,
        priority=49),
    dict(
        type='mmdet.PipelineSwitchHook',
        switch_epoch=max_epochs - stage2_num_epochs,
        switch_pipeline=train_pipeline_stage2)
]

# evaluators
# val_evaluator = dict(type='CocoMetric', ann_file=data_root + 'val_coco.json')
# val_evaluator = dict(type='PCKAccuracy')
# val_evaluator = [dict(type='CocoMetric', ann_file=data_root + 'val_coco.json'), dict(type='PCKAccuracy')]

# val_evaluator = [
#     dict(type='CocoMetric', ann_file=data_root + 'val_coco.json'),
#     dict(type='PCKAccuracy'),
#     dict(type='AUC'),
#     dict(type='NME', norm_mode='keypoint_distance')
# ]

val_evaluator = [
    dict(type='CocoMetric', ann_file=data_root + 'val_coco.json'),
    dict(type='PCKAccuracy'),
    dict(type='AUC'),
    dict(type='NME', norm_mode='keypoint_distance', keypoint_indices=[1, 2])
]

test_evaluator = val_evaluator

（七）训练RTM-pose关键点检测算法

训练同样选用命令行的方式，需要指定config文件 

python tools/train.py data/rtmpose-s_triangle_8xb256-420e_coco-256x192.py

训练完成后可以在测试集上评估精度，脚本命令如下：需要指定config文件以及训练完成的模型pth文件。

python tools/test.py data/rtmdet_tiny_triangle.py \
                      work_dirs/rtmdet_tiny_triangle/epoch_200.pth

（八）RTM-pose可视化训练日志脚本

import pandas as pd
from tqdm import tqdm

import matplotlib.pyplot as plt
plt.rcParams['axes.unicode_minus']=False  # 用来正常显示负号

#载入训练日志

# 日志文件路径
log_path = 'work_dirs/rtmpose-s_triangle_8xb256-420e_coco-256x192/20230512_091723/vis_data/scalars.json'

with open(log_path, "r") as f:
    json_list = f.readlines()

len(json_list)
eval(json_list[4])


df_train = pd.DataFrame()
df_test = pd.DataFrame()
for each in tqdm(json_list):
    if 'coco/AP' in each:
        df_test = df_test.append(eval(each), ignore_index=True)
    else:
        df_train = df_train.append(eval(each), ignore_index=True)


print(df_train)
print(df_test)


df_train.to_csv('训练日志-训练集.csv', index=False)
df_test.to_csv('训练日志-测试集.csv', index=False)



#导出训练日志表格


df_train.to_csv('训练日志-训练集.csv', index=False)
df_test.to_csv('训练日志-测试集.csv', index=False)

#设置Matplotlib中文字体

# # windows操作系统
# plt.rcParams['font.sans-serif']=['SimHei']  # 用来正常显示中文标签 
# plt.rcParams['axes.unicode_minus']=False  # 用来正常显示负号

# Linux操作系统

matplotlib.rc("font",family='SimHei') # 中文字体


#测试中文字体

plt.plot([1,2,3], [100,500,300])
plt.title('matplotlib中文字体测试', fontsize=25)
plt.xlabel('X轴', fontsize=15)
plt.ylabel('Y轴', fontsize=15)
plt.show()

#可视化辅助函数

from matplotlib import colors as mcolors
import random
random.seed(124)
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'black', 'indianred', 'brown', 'firebrick', 'maroon', 'darkred', 'red', 'sienna', 'chocolate', 'yellow', 'olivedrab', 'yellowgreen', 'darkolivegreen', 'forestgreen', 'limegreen', 'darkgreen', 'green', 'lime', 'seagreen', 'mediumseagreen', 'darkslategray', 'darkslategrey', 'teal', 'darkcyan', 'dodgerblue', 'navy', 'darkblue', 'mediumblue', 'blue', 'slateblue', 'darkslateblue', 'mediumslateblue', 'mediumpurple', 'rebeccapurple', 'blueviolet', 'indigo', 'darkorchid', 'darkviolet', 'mediumorchid', 'purple', 'darkmagenta', 'fuchsia', 'magenta', 'orchid', 'mediumvioletred', 'deeppink', 'hotpink']
markers = [".",",","o","v","^","<",">","1","2","3","4","8","s","p","P","*","h","H","+","x","X","D","d","|","_",0,1,2,3,4,5,6,7,8,9,10,11]
linestyle = ['--', '-.', '-']

def get_line_arg():
    '''
    随机产生一种绘图线型
    '''
    line_arg = {}
    line_arg['color'] = random.choice(colors)
    # line_arg['marker'] = random.choice(markers)
    line_arg['linestyle'] = random.choice(linestyle)
    line_arg['linewidth'] = random.randint(1, 4)
    # line_arg['markersize'] = random.randint(3, 5)
    return line_arg

#训练集损失函数
print(df_train.columns)

metrics = ['loss', 'loss_kpt']
plt.figure(figsize=(16, 8))

x = df_train['step']
for y in metrics:
    plt.plot(x, df_train[y], label=y, **get_line_arg())

plt.tick_params(labelsize=20)
plt.xlabel('step', fontsize=20)
plt.ylabel('loss', fontsize=20)
plt.title('训练集损失函数', fontsize=25)
plt.savefig('训练集损失函数.pdf', dpi=120, bbox_inches='tight')

plt.legend(fontsize=20)

plt.show()

#训练集准确率

metrics = ['acc_pose']

plt.figure(figsize=(16, 8))

x = df_train['step']
for y in metrics:
    plt.plot(x, df_train[y], label=y, **get_line_arg())

plt.tick_params(labelsize=20)
plt.xlabel('step', fontsize=20)
plt.ylabel('loss', fontsize=20)
plt.title('训练集准确率', fontsize=25)
plt.savefig('训练集准确率.pdf', dpi=120, bbox_inches='tight')

plt.legend(fontsize=20)

plt.show()


#测试集评估指标-MS COCO Metric

print(df_test.columns)

metrics = ['coco/AP', 'coco/AP .5', 'coco/AP .75', 'coco/AP (M)', 'coco/AP (L)', 'coco/AR', 'coco/AR .5', 'coco/AR .75', 'coco/AR (M)', 'coco/AR (L)', 'PCK', 'AUC']


plt.figure(figsize=(16, 8))

x = df_test['step']
for y in metrics:
    plt.plot(x, df_test[y], label=y, **get_line_arg())

plt.tick_params(labelsize=20)
# plt.ylim([0, 100])
plt.xlabel('Epoch', fontsize=20)
plt.ylabel(y, fontsize=20)
plt.title('测试集评估指标', fontsize=25)
plt.savefig('测试集分类评估指标.pdf', dpi=120, bbox_inches='tight')

plt.legend(fontsize=20)

plt.show()


#测试集评估指标-NME

metrics = ['NME']

plt.figure(figsize=(16, 8))

x = df_test['step']
for y in metrics:
    plt.plot(x, df_test[y], label=y, **get_line_arg())

plt.tick_params(labelsize=20)
# plt.ylim([0, 100])
plt.xlabel('Epoch', fontsize=20)
plt.ylabel(y, fontsize=20)
plt.title('测试集评估指标', fontsize=25)
plt.savefig('测试集分类评估指标.pdf', dpi=120, bbox_inches='tight')

plt.legend(fontsize=20)

plt.show()

(九)mmpose模型权重文件精简转换

模型权重文件精简转换采用的是tools/misc/publish_model.py脚本，需要指定训练完成的模型的pth文件以及config文件。具体转换命令如下：

python tools/misc/publish_model.py \
        work_dirs/rtmpose-s_triangle_8xb256-420e_coco-256x192/epoch_300.pth \
        checkpoint/rtmpose_s_triangle_300.pth

（十）使用精简转换后的模型进行预测，预测同样分为两个分支，一个是基于faster-rcnn的，另一个是基于rtm-pose的。

1：基于faster-rcnn的模型预测，预测命令如下：

python demo/topdown_demo_with_mmdet.py \
        data/faster_r_cnn_triangle.py \
        checkpoint/faster_r_cnn_triangle_epoch_50_202305120846-76d9dde3.pth \
        data/rtmpose-s_triangle_8xb256-420e_coco-256x192.py \
        checkpoint/rtmpose_s_triangle_300-34bfaeb2_20230512.pth \
        --input data/test_triangle/triangle_7.jpg \
        --output-root outputs/G2_Fasterrcnn-RTMPose \
        --device cuda:0 \
        --bbox-thr 0.5 \
        --kpt-thr 0.5 \
        --nms-thr 0.3 \
        --radius 36 \
        --thickness 30 \
        --draw-bbox \
        --draw-heatmap \
        --show-kpt-idx

2：基于rtmdet的模型预测，预测命令如下：

python demo/topdown_demo_with_mmdet.py \
        data/rtmdet_tiny_triangle.py \
        checkpoint/rtmdet_tiny_triangle_epoch_200_202305120847-3cd02a8f.pth \
        data/rtmpose-s_triangle_8xb256-420e_coco-256x192.py \
        checkpoint/rtmpose_s_triangle_300-34bfaeb2_20230512.pth \
        --input data/test_triangle/triangle_4.jpg \
        --output-root outputs/G2_RTMDet-RTMPose \
        --device cuda:0 \
        --bbox-thr 0.5 \
        --kpt-thr 0.5 \
        --nms-thr 0.3 \
        --radius 36 \
        --thickness 30 \
        --draw-bbox \
        --draw-heatmap \

同样也可以输入视频进行预测，预测命令如下：该命令是基于rtmdet的。

python demo/topdown_demo_with_mmdet.py \
        data/rtmdet_tiny_triangle.py \
        checkpoint/rtmdet_tiny_triangle_epoch_200_202305120847-3cd02a8f.pth \
        data/rtmpose-s_triangle_8xb256-420e_coco-256x192.py \
        checkpoint/rtmpose_s_triangle_300-34bfaeb2_20230512.pth \
        --input data/test_triangle/triangle_9.mp4 \
        --output-root outputs/G2_Video \
        --device cuda:0 \
        --bbox-thr 0.5 \
        --kpt-thr 0.5 \
        --nms-thr 0.3 \
        --radius 16 \
        --thickness 10 \
        --draw-bbox \
        --draw-heatmap \
        --show-kpt-idx