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一、准备数据集

准备自己的数据

mmdetection支持coco格式和voc格式的数据集，下面将分别介绍这两种数据集的使用方式

coco数据集

官方推荐coco数据集按照以下的目录形式存储，以coco2017数据集为例

mmdetection├── mmdet├── tools├── configs├── data│   ├── coco│   │   ├── annotations│   │   ├── train2017│   │   ├── val2017│   │   ├── test2017

推荐以软连接的方式创建data文件夹，下面是创建软连接的步骤

cd mmdetectionmkdir dataln -s $COCO_ROOT data

其中，$COCO_ROOT需改为你的coco数据集根目录

voc数据集

与coco数据集类似，将voc数据集按照以下的目录形式存储，以VOC2007为例

mmdetection├── mmdet├── tools├── configs├── data│   ├── VOCdevkit│   │   ├── VOC2007│   │   │   ├── Annotations│   │   │   ├── JPEGImages│   │   │   ├── ImageSets│   │   │   │   ├── Main│   │   │   │   │   ├── test.txt│   │   │   │   │   ├── trainval.txt

同样推荐以软连接的方式创建

cd mmdetectionmkdir dataln -s $VOC2007_ROOT data/VOCdevkit

其中，$VOC2007_ROOT需改为你的VOC2007数据集根目录

二、修改一些配置文件和代码文件

修改配置文件，配置文件在configs文件夹下面，根据自己的情况进行选择，

本人选择的是configs/mask_rcnn_r101_fpn_1x.py根据自己情况修改说明，如果选择faster rcnn请根据自己情况进行修改：

# model settingsmodel = dict(    type='MaskRCNN',    pretrained='torchvision://resnet101',    backbone=dict(        type='ResNet',        depth=101,        num_stages=4,        out_indices=(0, 1, 2, 3),        frozen_stages=1,        style='pytorch'),    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_scales=[8],        anchor_ratios=[0.5, 1.0, 2.0],        anchor_strides=[4, 8, 16, 32, 64],        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='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),    bbox_roi_extractor=dict(        type='SingleRoIExtractor',        roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),        out_channels=256,        featmap_strides=[4, 8, 16, 32]),    bbox_head=dict(        type='SharedFCBBoxHead',        num_fcs=2,        in_channels=256,        fc_out_channels=1024,        roi_feat_size=7,        num_classes=6,#数据集类别数,默认是81,因为coco数据集为80+1(背景),我的数据集只有5个类别,加上背景也就是6个类别        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='SmoothL1Loss', beta=1.0, loss_weight=1.0)),    mask_roi_extractor=dict(        type='SingleRoIExtractor',        roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),        out_channels=256,        featmap_strides=[4, 8, 16, 32]),    mask_head=dict(        type='FCNMaskHead',        num_convs=4,        in_channels=256,        conv_out_channels=256,        num_classes=6,#数据集类别数,默认是81,因为coco数据集为80+1(背景),我的数据集只有5个类别,加上背景也就是6个类别        loss_mask=dict(            type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))# model training and testing settingstrain_cfg = dict(    rpn=dict(        assigner=dict(            type='MaxIoUAssigner',            pos_iou_thr=0.7,            neg_iou_thr=0.3,            min_pos_iou=0.3,            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=0,        pos_weight=-1,        debug=False),    rpn_proposal=dict(        nms_across_levels=False,        nms_pre=2000,        nms_post=2000,        max_num=2000,        nms_thr=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,            ignore_iof_thr=-1),        sampler=dict(            type='RandomSampler',            num=512,            pos_fraction=0.25,            neg_pos_ub=-1,            add_gt_as_proposals=True),        mask_size=28,        pos_weight=-1,        debug=False))test_cfg = dict(    rpn=dict(        nms_across_levels=False,        nms_pre=1000,        nms_post=1000,        max_num=1000,        nms_thr=0.7,        min_bbox_size=0),    rcnn=dict(        score_thr=0.05,        nms=dict(type='nms', iou_thr=0.5),        max_per_img=100,        mask_thr_binary=0.5))# dataset settingsdataset_type = 'CocoDataset'data_root = 'data/coco/'img_norm_cfg = dict(    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)train_pipeline = [    dict(type='LoadImageFromFile'),    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),    dict(type='RandomFlip', flip_ratio=0.5),    dict(type='Normalize', **img_norm_cfg),    dict(type='Pad', size_divisor=32),    dict(type='DefaultFormatBundle'),    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),]test_pipeline = [    dict(type='LoadImageFromFile'),    dict(        type='MultiScaleFlipAug',        img_scale=(1333, 800),        flip=False,        transforms=[            dict(type='Resize', keep_ratio=True),            dict(type='RandomFlip'),            dict(type='Normalize', **img_norm_cfg),            dict(type='Pad', size_divisor=32),            dict(type='ImageToTensor', keys=['img']),            dict(type='Collect', keys=['img']),        ])]data = dict(    imgs_per_gpu=2,#每张gpu训练多少张图片  batch_size = gpu_num(训练使用gpu数量) * imgs_per_gpu    workers_per_gpu=1,    train=dict(        type=dataset_type,        ann_file=data_root + 'annotations/instances_train2014.json',        img_prefix=data_root + 'train2014/',        pipeline=train_pipeline),    val=dict(        type=dataset_type,        ann_file=data_root + 'annotations/instances_val2014.json',        img_prefix=data_root + 'val2014/',        pipeline=test_pipeline),    test=dict(        type=dataset_type,        ann_file=data_root + 'annotations/instances_val2014.json',        img_prefix=data_root + 'val2014/',        pipeline=test_pipeline))# optimizeroptimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)#lr = 0.00125*batch_sizoptimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))# learning policylr_config = dict(    policy='step',    warmup='linear',    warmup_iters=500,    warmup_ratio=1.0 / 3,    step=[8, 11])checkpoint_config = dict(interval=1)# yapf:disablelog_config = dict(    interval=50,    hooks=[        dict(type='TextLoggerHook'),        # dict(type='TensorboardLoggerHook')#如果需要开启tensorboard将其注释取消    ])# yapf:enable# runtime settingstotal_epochs = 12#总的循环次数dist_params = dict(backend='nccl')log_level = 'INFO'work_dir = './work_dirs/mask_rcnn_r101_fpn_1x'#在训练过程中会将训练日志和权重保存在这个文件夹路径下面，详细看下面load_from = Noneresume_from = Noneworkflow = [('train', 1)]

注意：配置文件中的默认学习率是8个gpu和2个img/gpu(batch size= 8*2 = 16)。根据线性缩放规则，如果您使用不同的GPU数目或img/gpu，您需要设置与batch size成比例的学习率。例如，如果4GPUs * 2 img/gpu的lr=0.01，那么16GPUs * 4 img/gpu的lr=0.08。

每张gpu训练多少张图片 batch_size = gpu_num(训练使用gpu数量) * imgs_per_gpu

lr = 0.00125*batch_siz

进一步修改一些地方:

定义数据种类，需要修改的地方在mmdetection/mmdet/datasets/coco.py。把CLASSES的那个tuple改为自己数据集对应的种类tuple即可。例如：

CLASSES = ('bicycle', 'car', 'bus', 'person','tvmonitor')

接着在mmdetection/mmdet/core/evaluation/class_names.py修改coco_classes数据集类别，这个关系到后面test的时候结果图中显示的类别名称。例如：

def coco_classes():    return [        'bicycle', 'car', 'bus', 'person','tvmonitor'    ]

三、训练：

单个GPU训练：

第一次训练会下载预训练模型，如下

(open-mmlab) bubble@XPS-8930:~/mmdetection/0827/mmdetection$ python tools/train.py configs/mask_rcnn_r101_fpn_1x.py2019-09-16 22:14:50,684 - INFO - Distributed training: False2019-09-16 22:14:51,399 - INFO - load model from: torchvision://resnet101Downloading: "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth" to /home/bubble/.cache/torch/checkpoints/resnet101-5d3b4d8f.pth100.0%

训练过程log输出

(open-mmlab) bubble@XPS-8930:~/mmdetection/0827/mmdetection$ python tools/train.py configs/mask_rcnn_r101_fpn_1x.py2019-09-18 21:31:10,284 - INFO - Distributed training: False2019-09-18 21:31:11,067 - INFO - load model from: torchvision://resnet1012019-09-18 21:31:13,031 - WARNING - The model and loaded state dict do not match exactlyunexpected key in source state_dict: fc.weight, fc.biasloading annotations into memory...Done (t=0.17s)creating index...index created!2019-09-18 21:31:23,956 - INFO - Start running, host: bubble@XPS-8930, work_dir: /home/bubble/mmdetection/0827/mmdetection/work_dirs/mask_rcnn_r101_fpn_1x2019-09-18 21:31:23,956 - INFO - workflow: [('train', 1)], max: 12 epochs2019-09-18 21:31:46,277 - INFO - Epoch [1][50/8001]	lr: 0.00797, eta: 11:53:25, time: 0.446, data_time: 0.012, memory: 3241, loss_rpn_cls: 0.2262, loss_rpn_bbox: 0.0602, loss_cls: 0.7983, acc: 94.5195, loss_bbox: 0.0827, loss_mask: 0.6030, loss: 1.77042019-09-18 21:32:05,929 - INFO - Epoch [1][100/8001]	lr: 0.00931, eta: 11:10:49, time: 0.393, data_time: 0.004, memory: 3241, loss_rpn_cls: 0.1831, loss_rpn_bbox: 0.0712, loss_cls: 0.5860, acc: 94.6523, loss_bbox: 0.1161, loss_mask: 0.4851, loss: 1.4414..................2019-09-19 08:42:04,113 - INFO - Epoch [12][7950/8001]	lr: 0.00020, eta: 0:00:21, time: 0.427, data_time: 0.005, memory: 3290, loss_rpn_cls: 0.0112, loss_rpn_bbox: 0.0188, loss_cls: 0.1462, acc: 95.1758, loss_bbox: 0.0890, loss_mask: 0.1974, loss: 0.46262019-09-19 08:42:25,439 - INFO - Epoch [12][8000/8001]	lr: 0.00020, eta: 0:00:00, time: 0.427, data_time: 0.004, memory: 3290, loss_rpn_cls: 0.0106, loss_rpn_bbox: 0.0215, loss_cls: 0.1168, acc: 95.9844, loss_bbox: 0.0715, loss_mask: 0.1791, loss: 0.3995

在训练过程

mmdetection整个文件夹情况：

.├── build├── checkpoints├── configs├── data├── demo├── docker├── docs├── LICENSE├── mmdet├── mmdet.egg-info├── README.md├── requirements.txt├── setup.py├── tests├── tools└── work_dirs

训练过程中log和权重保存路径

work_dirs/└── mask_rcnn_r101_fpn_1x    ├── 20190918_213123.log    ├── 20190918_213123.log.json    ├── epoch_10.pth    ├── epoch_11.pth    ├── epoch_12.pth    ├── epoch_1.pth    ├── epoch_2.pth    ├── epoch_3.pth    ├── epoch_4.pth    ├── epoch_5.pth    ├── epoch_6.pth    ├── epoch_7.pth    ├── epoch_8.pth    ├── epoch_9.pth    └── latest.pth -> epoch_12.pth

四、训练过程各种损失值和准确率可视化效果

4.1、tensorboard

开启tensorboard，记得在config配置文件里将dict(type='TensorboardLoggerHook')注释取消掉

在新的终端中执行如下命令：

tensorboard --logdir=path --port=8090#port=8090可以自己指定的端口，默认不需要--port其端口是6006

4.1.1、本地跑mmdetection的话直接在PC的浏览器上输入如下链接

http://127.0.0.1:16006

4.1.2、远程访问服务器上面的tensorboard

bubble@XPS-8930:~$ ssh -p 10005 -L 16006:127.0.0.1:8090 root@192.168.1.162#10005是自己的docker系统镜像的端口号，-L 16006:127.0.0.18090意思是将自己PC的16006端口映射成docker里tensorboard的8090#接下来，在PC的浏览器上输入如下链接http://127.0.0.1:16006

过程如下图所示：

tensorboard效果如下所示：

4.2、mmdetection自带的log分析工具

mmdetection会自动收集log信息，存储在work_dirs/目录下，官方提供了tools/analyze_logs.py工具可以轻松的可视化日志信息，如可视化损失值并保存为pdf文件，执行如下命令：

python tools/analyze_logs.py plot_curve log.json --keys loss_cls loss_reg --out losses.pdf

效果如图所示：

mmdetection使用tensorboard可视化训练集与验证集指标参数

mmdetection使用其自带工具可视化训练集与验证集指标参数

具体可以查看官网：

五、Testing

有两个方法可以进行测试。

5.1、如果只是想看一下效果而不要进行定量指标分析的话，可以运行之前那个demo.py文件，但是要改一下checkpoint_file的地址路径，使用我们上一步跑出来的work_dirs下的pth文件。例如：

checkpoint_file = 'work_dirs/epoch_100.pth'

5.2、使用test命令来进行测试评估一些参数

5.2.1 coco数据集，例如：

python tools/test.py configs/your_confige.py work_dirs/your_model_.pth --out ./result/result_100.pkl --eval bbox

类似于下面：

root@64e7169a4f30:/mmdetection# python tools/test.py configs/mask_rcnn_r101_fpn_1x.py work_dirs/mask_rcnn_r101_fpn_1x/latest.pth --eval bbox --out results1.pklloading annotations into memory...Done (t=0.00s)creating index...index created![>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 67/67, 17.7 task/s, elapsed: 4s, ETA:     0swriting results to results1.pklStarting evaluate bboxLoading and preparing results...DONE (t=0.00s)creating index...index created!Running per image evaluation...Evaluate annotation type *bbox*DONE (t=0.04s).Accumulating evaluation results...DONE (t=0.01s). Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.698 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.917 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.826 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.742 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.734 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.744 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.744 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.744 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000

5.2.2 voc数据集，例如

修改tools/voc_eval.py文件中的voc_eval函数

注释如下代码：

if hasattr(dataset, 'year') and dataset.year == 2007:        dataset_name = 'voc07'    else:        dataset_name = dataset.CLASSES

在eval_map上面添加：

dataset_name = dataset.CLASSES

修改后的voc_eval函数代码如下：

def voc_eval(result_file, dataset, iou_thr=0.5):    det_results = mmcv.load(result_file)    gt_bboxes = []    gt_labels = []    gt_ignore = []    for i in range(len(dataset)):        ann = dataset.get_ann_info(i)        bboxes = ann['bboxes']        labels = ann['labels']        if 'bboxes_ignore' in ann:            ignore = np.concatenate([                np.zeros(bboxes.shape[0], dtype=np.bool),                np.ones(ann['bboxes_ignore'].shape[0], dtype=np.bool)            ])            gt_ignore.append(ignore)            bboxes = np.vstack([bboxes, ann['bboxes_ignore']])            labels = np.concatenate([labels, ann['labels_ignore']])        gt_bboxes.append(bboxes)        gt_labels.append(labels)    if not gt_ignore:        gt_ignore = None    # if hasattr(dataset, 'year') and dataset.year == 2007:    #     dataset_name = 'voc07'    # else:    #     dataset_name = dataset.CLASSES     dataset_name = dataset.CLASSES     eval_map(        det_results,        gt_bboxes,        gt_labels,        gt_ignore=gt_ignore,        scale_ranges=None,        iou_thr=iou_thr,        dataset=dataset_name,        print_summary=True)

执行如下命令：

python tools/test.py configs/your_confige.py work_dirs/your_model_.pth --out results.pkl

测试结束后生成results.pkl文件

采用voc标准计算mAP

执行如下命令：

python tools/voc_eval.py results.pkl configs/your_confige.py

类似下面这样子：

参考链接：

转载地址：http://drzlf.baihongyu.com/