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如何使用mmdetection训练自己的数据可以参考这篇文章，在这篇文章中只是用训练集进行训练，没有用到验证集验证模型的指标，因此这篇文章中将会讨论如何增加验证集，并且使用tensorboard可视化训练集与验证集的指标参数。

以cascade_rcnn_hrnetv2p_w32_20e.py为例，原始的文件内容如下：

# model settingsmodel = dict(    type='CascadeRCNN',    num_stages=3,    pretrained='open-mmlab://msra/hrnetv2_w32',    backbone=dict(        type='HRNet',        extra=dict(            stage1=dict(                num_modules=1,                num_branches=1,                block='BOTTLENECK',                num_blocks=(4, ),                num_channels=(64, )),            stage2=dict(                num_modules=1,                num_branches=2,                block='BASIC',                num_blocks=(4, 4),                num_channels=(32, 64)),            stage3=dict(                num_modules=4,                num_branches=3,                block='BASIC',                num_blocks=(4, 4, 4),                num_channels=(32, 64, 128)),            stage4=dict(                num_modules=3,                num_branches=4,                block='BASIC',                num_blocks=(4, 4, 4, 4),                num_channels=(32, 64, 128, 256)))),    neck=dict(type='HRFPN', in_channels=[32, 64, 128, 256], out_channels=256),    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=81,            target_means=[0., 0., 0., 0.],            target_stds=[0.1, 0.1, 0.2, 0.2],            reg_class_agnostic=True,            loss_cls=dict(                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),        dict(            type='SharedFCBBoxHead',            num_fcs=2,            in_channels=256,            fc_out_channels=1024,            roi_feat_size=7,            num_classes=81,            target_means=[0., 0., 0., 0.],            target_stds=[0.05, 0.05, 0.1, 0.1],            reg_class_agnostic=True,            loss_cls=dict(                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),        dict(            type='SharedFCBBoxHead',            num_fcs=2,            in_channels=256,            fc_out_channels=1024,            roi_feat_size=7,            num_classes=81,            target_means=[0., 0., 0., 0.],            target_stds=[0.033, 0.033, 0.067, 0.067],            reg_class_agnostic=True,            loss_cls=dict(                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, 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),            pos_weight=-1,            debug=False),        dict(            assigner=dict(                type='MaxIoUAssigner',                pos_iou_thr=0.6,                neg_iou_thr=0.6,                min_pos_iou=0.6,                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),        dict(            assigner=dict(                type='MaxIoUAssigner',                pos_iou_thr=0.7,                neg_iou_thr=0.7,                min_pos_iou=0.7,                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)    ],    stage_loss_weights=[1, 0.5, 0.25])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),    keep_all_stages=False)# 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),    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']),]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,    workers_per_gpu=2,    train=dict(        type=dataset_type,        ann_file=data_root + 'annotations/instances_train2017.json',        img_prefix=data_root + 'train2017/',        pipeline=train_pipeline),    val=dict(        type=dataset_type,        ann_file=data_root + 'annotations/instances_val2017.json',        img_prefix=data_root + 'val2017/',        pipeline=test_pipeline),    test=dict(        type=dataset_type,        ann_file=data_root + 'annotations/instances_val2017.json',        img_prefix=data_root + 'val2017/',        pipeline=test_pipeline))# optimizeroptimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)optimizer_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=[16, 19])checkpoint_config = dict(interval=1)# yapf:disablelog_config = dict(    interval=50,    hooks=[        dict(type='TextLoggerHook'),        # dict(type='TensorboardLoggerHook')    ])# yapf:enable# runtime settingstotal_epochs = 20dist_params = dict(backend='nccl')log_level = 'INFO'work_dir = './work_dirs/cascade_rcnn_hrnetv2p_w32'load_from = Noneresume_from = Noneworkflow = [('train', 1)]

其中配置文件中的workflow参数的含义是工作流，比如workflow = [('train', 1), ('val', 1)]表示按照训练一个epoch，接着再进行一个epoch的验证的策略迭代训练，如果workflow = [('train', 1)]表示训练完一个epoch后接着进行下一个epoch的训练，期间不进行验证。因此，增加验证集需要配置workflow，比如我们期望训练完一个epoch后接着进行验证，从而可以评估当前模型在验证集上的效果，这样我们设置workflow = [('train', 1), ('val', 1)]。

仅仅修改了这里，训练完一个epoch接下来进行验证的时候会报如下错误：

这是因为，还缺少验证集pipline的配置，默认配置文件中是没有val_pipeline的配置的，我们需要参考train_pipeline的内容添加val_pipeline,在cascade_rcnn_hrnetv2p_w32_20e.py中的train_pipeline与test_pipeline之间添加val_pipeline的内容：

val_pipeline = [    dict(type='LoadImageFromFile'),    dict(type='LoadAnnotations', with_bbox=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']),]

可以看到val_pipeline的内容与train_pipline的内容是一样的。添加了val_pipeline后，还需要将cascade_rcnn_hrnetv2p_w32_20e.py中验证集的pipeline修改为刚才添加的val_pipeline,修改后如下：

data = dict(    imgs_per_gpu=2,    workers_per_gpu=2,    train=dict(        type=dataset_type,        ann_file=data_root + 'annotations/instances_train2017.json',        img_prefix=data_root + 'train2017/',        pipeline=train_pipeline),    val=dict(        type=dataset_type,        ann_file=data_root + 'annotations/instances_val2017.json',        img_prefix=data_root + 'val2017/',        pipeline=val_pipeline),    test=dict(        type=dataset_type,        ann_file=data_root + 'annotations/instances_val2017.json',        img_prefix=data_root + 'val2017/',        pipeline=test_pipeline))

至此，验证集的添加工作就完成了。

接下来添加tensorboard的配置（要使用tensorboard进行可视化需要提前安装tensorboardx，执行sudo pip install tensorboardx即可安装），默认配置文件中是不开启tensorboard的配置的，只需要在log_config中的tensorboard注释取消即可，如下：

log_config = dict(    interval=1,    hooks=[        dict(type='TextLoggerHook'),        dict(type='TensorboardLoggerHook')    ])

 至此，所有的配置已完成，修改后的完整的配置文件内容如下：

# model settingsmodel = dict(    type='CascadeRCNN',    num_stages=3,    pretrained='open-mmlab://msra/hrnetv2_w32',    backbone=dict(        type='HRNet',        extra=dict(            stage1=dict(                num_modules=1,                num_branches=1,                block='BOTTLENECK',                num_blocks=(4, ),                num_channels=(64, )),            stage2=dict(                num_modules=1,                num_branches=2,                block='BASIC',                num_blocks=(4, 4),                num_channels=(32, 64)),            stage3=dict(                num_modules=4,                num_branches=3,                block='BASIC',                num_blocks=(4, 4, 4),                num_channels=(32, 64, 128)),            stage4=dict(                num_modules=3,                num_branches=4,                block='BASIC',                num_blocks=(4, 4, 4, 4),                num_channels=(32, 64, 128, 256)))),    neck=dict(type='HRFPN', in_channels=[32, 64, 128, 256], out_channels=256),    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=20,            target_means=[0., 0., 0., 0.],            target_stds=[0.1, 0.1, 0.2, 0.2],            reg_class_agnostic=True,            loss_cls=dict(                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),        dict(            type='SharedFCBBoxHead',            num_fcs=2,            in_channels=256,            fc_out_channels=1024,            roi_feat_size=7,            num_classes=20,            target_means=[0., 0., 0., 0.],            target_stds=[0.05, 0.05, 0.1, 0.1],            reg_class_agnostic=True,            loss_cls=dict(                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),        dict(            type='SharedFCBBoxHead',            num_fcs=2,            in_channels=256,            fc_out_channels=1024,            roi_feat_size=7,            num_classes=20,            target_means=[0., 0., 0., 0.],            target_stds=[0.033, 0.033, 0.067, 0.067],            reg_class_agnostic=True,            loss_cls=dict(                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, 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),            pos_weight=-1,            debug=False),        dict(            assigner=dict(                type='MaxIoUAssigner',                pos_iou_thr=0.6,                neg_iou_thr=0.6,                min_pos_iou=0.6,                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),        dict(            assigner=dict(                type='MaxIoUAssigner',                pos_iou_thr=0.7,                neg_iou_thr=0.7,                min_pos_iou=0.7,                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)    ],    stage_loss_weights=[1, 0.5, 0.25])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),    keep_all_stages=False)# dataset settingsdataset_type = 'VOCDataset'data_root = 'data/VOCdevkit/'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),    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']),]val_pipeline = [    dict(type='LoadImageFromFile'),    dict(type='LoadAnnotations', with_bbox=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']),]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=1,    workers_per_gpu=8,    train=dict(        type=dataset_type,        ann_file=data_root + 'VOC2007/ImageSets/Main/train.txt',        img_prefix=data_root + 'VOC2007',        pipeline=train_pipeline),    val=dict(        type=dataset_type,        ann_file=data_root + 'VOC2007/ImageSets/Main/val.txt',        img_prefix=data_root + 'VOC2007',        pipeline=val_pipeline),    test=dict(        type=dataset_type,        ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',        img_prefix=data_root + 'VOC2007',        pipeline=test_pipeline))# optimizeroptimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)optimizer_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=[16, 19])checkpoint_config = dict(interval=1)# yapf:disablelog_config = dict(    interval=1,    hooks=[        dict(type='TextLoggerHook'),        dict(type='TensorboardLoggerHook')    ])# yapf:enable# runtime settingstotal_epochs = 20dist_params = dict(backend='nccl')log_level = 'INFO'work_dir = './work_dirs/cascade_rcnn_hrnetv2p_w32'load_from = Noneresume_from = Noneworkflow = [('train', 1), ('val', 1)]

 开启tensorboard，执行如下命令：

tensorboard --logdir=work_dirs/cascade_rcnn_hrnetv2p_w32/

开启成功可以看到如下现象：

 我们可以通过浏览器访问，便可以看到tensorboard的信息，但是通常我们训练是在服务器上进行的，可能不方便接显示器，无法通过服务器的浏览器对上述连接进行访问，因此需要端口转发将服务器上的6006端口转发到其它便于访问的主机上，一种简单的方式是通过ssh实现端口转发，在PC上开启命令行模式输入如下指令：

ssh -p 50000 -L 16006:127.0.0.1:6006 lhy@192.168.3.11

接下来，在PC的浏览器上输入如下链接：

http://127.0.0.1:16006/

如果一切正常，可以看到tensorboard的界面了：

左边一栏是训练集的指标，右边是验证集的指标。

另外，mmdetection会自动收集log信息，存储在work_dirs/cascade_rcnn_hrnetv2p_w32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 work_dirs/cascade_rcnn_hrnetv2p_w32/20191130_125701.log.json  --keys loss --out losses.pdf

结果图：

疑惑：在添加验证集训练时，只看到训练集的日志信息，看不到验证集的日志信息，mmdetection的日志存储目录下也只有训练集的日志信息没有验证集的日志信息，有哪位知悉原因望指教。

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