1、通过脚本参数修改配置。

当使用"tools/train.py"或"tools/test.py"提交作业时，可以指定–cfg-options来就地修改配置。

1、Update config keys of dict chains（更新配置文件字典中的键）

配置选项可以按照原始配置中dict键的顺序指定。

For example, 
--cfg-options model.backbone.norm_eval=False 
changes all BN modules in model backbones to train mode.

2、Update keys inside a list of configs.（更新配置文件中的）

Some config dicts are composed as a list in your config. For example, the training pipeline data.train.pipeline is normally a list e.g. [dict(type=‘LoadImageFromFile’), …]. If you want to change ‘LoadImageFromFile’ to ‘LoadImageFromWebcam’ in the pipeline, you may specify --cfg-options data.train.pipeline.0.type=LoadImageFromWebcam.

3、Update values of list/tuples.（更新列表数组中的值）

If the value to be updated is a list or a tuple. For example, the config file normally sets workflow=[(‘train’, 1)]. If you want to change this key, you may specify --cfg-options workflow=“[(train,1),(val,1)]”. Note that the quotation mark ” is necessary to support list/tuple data types, and that NO white space is allowed inside the quotation marks in the specified valu

2、配置文件命名约定

我们遵循下面的样式来命名配置文件

{model}_[model setting]_{backbone}_{neck}_[norm setting]_[misc]_[gpu x batch_per_gpu]_{dataset}_{data setting}_{angle version}

{xxx} is required field and [yyy] is optional.

{model}: model type like rotated_faster_rcnn, rotated_retinanet, etc.

[model setting]: specific setting for some model, like hbb for rotated_retinanet, etc.某些模型的特定设置，如rotated_retinanet的HBB等

{backbone}: backbone type like r50 (ResNet-50), swin_tiny (SWIN-tiny).

{neck}: neck type like fpn, refpn.

[norm_setting]: bn (Batch Normalization) is used unless specified, other norm layer types could be gn (Group Normalization), syncbn (Synchronized Batch Normalization). gn-head/gn-neck indicates GN is applied in head/neck only, while gn-all means GN is applied in the entire model, e.g. backbone, neck, head.
除非指定，否则使用bn (Batch Normalization)，其他规范层类型可以是gn (Group Normalization)， syncbn (Synchronized Batch Normalization)。GN -head/ GN -neck表示GN仅应用于头部/颈部，而GN -all表示GN应用于整个模型，例如脊柱、颈部、头部

[misc]: miscellaneous setting/plugins of the model, e.g. dconv, gcb, attention, albu, mstrain.
模型的其他设置/插件，如:dconv, gcb, attention, albu, mstrain

[gpu x batch_per_gpu]: GPUs and samples per GPU, 1xb2 is used by default.

{dataset}: dataset like dota.

{angle version}: like oc, le135, or le90.

例如：cfa_r50_fpn_1x_dota_le135.py  
cfa代表模型，字段model；
r50代表选用的网络骨架r50 (ResNet-50)，字段backbone
fpn代表neck字段，
1x代表[gpu x batch_per_gpu]，1xb2 is used by default。
dota代表数据类型
le135，代表旋转框的角度类型

3、RotatedRetinaNet的一个例子

为了帮助用户对一个现代检测系统的完整配置和模块有一个基本的概念，下面我们简要介绍一下使用ResNet50和FPN的RotatedRetinaNet的配置。要了解每个模块的更详细用法和相应的替代方案，请参阅API文档。

angle_version = 'oc'  # The angle version
model = dict(
    type='RotatedRetinaNet',  # The name of detector
    backbone=dict(  # The config of backbone
        type='ResNet',  # The type of the backbone
        depth=50,  # The depth of backbone
        num_stages=4,  # Number of stages of the backbone.
        out_indices=(0, 1, 2, 3),  # The index of output feature maps produced in each stages
        frozen_stages=1,  # The weights in the first 1 stage are fronzen
        zero_init_residual=False,  # Whether to use zero init for last norm layer in resblocks to let them behave as identity.
        norm_cfg=dict(  # The config of normalization layers.
            type='BN',  # Type of norm layer, usually it is BN or GN
            requires_grad=True),  # Whether to train the gamma and beta in BN
        norm_eval=True,  # Whether to freeze the statistics in BN
        style='pytorch',  # The style of backbone, 'pytorch' means that stride 2 layers are in 3x3 conv, 'caffe' means stride 2 layers are in 1x1 convs.
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),  # The ImageNet pretrained backbone to be loaded
    neck=dict(
        type='FPN',  # The neck of detector is FPN. We also support 'ReFPN'
        in_channels=[256, 512, 1024, 2048],  # The input channels, this is consistent with the output channels of backbone
        out_channels=256,  # The output channels of each level of the pyramid feature map
        start_level=1,  # Index of the start input backbone level used to build the feature pyramid
        add_extra_convs='on_input',  # It specifies the source feature map of the extra convs
        num_outs=5),  # The number of output scales
    bbox_head=dict(
        type='RotatedRetinaHead',# The type of bbox head is 'RRetinaHead'
        num_classes=15,  # Number of classes for classification
        in_channels=256,  # Input channels for bbox head
        stacked_convs=4,  # Number of stacking convs of the head
        feat_channels=256,  # Number of hidden channels
        assign_by_circumhbbox='oc',  # The angle version of obb2hbb
        anchor_generator=dict(  # The config of anchor generator
            type='RotatedAnchorGenerator',  # The type of anchor generator
            octave_base_scale=4,  # The base scale of octave.
            scales_per_octave=3,  #  Number of scales for each octave.
            ratios=[1.0, 0.5, 2.0],  # The ratio between height and width.
            strides=[8, 16, 32, 64, 128]),  # The strides of the anchor generator. This is consistent with the FPN feature strides.
        bbox_coder=dict(  # Config of box coder to encode and decode the boxes during training and testing
            type='DeltaXYWHAOBBoxCoder',  # Type of box coder.
            angle_range='oc',  # The angle version of box coder.
            norm_factor=None,  # The norm factor of box coder.
            edge_swap=False,  # The edge swap flag of box coder.
            proj_xy=False,  # The project flag of box coder.
            target_means=(0.0, 0.0, 0.0, 0.0, 0.0),  # The target means used to encode and decode boxes
            target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),  # The standard variance used to encode and decode boxes
        loss_cls=dict(  # Config of loss function for the classification branch
            type='FocalLoss',  # Type of loss for classification branch
            use_sigmoid=True,  #  Whether the prediction is used for sigmoid or softmax
            gamma=2.0,  # The gamma for calculating the modulating factor
            alpha=0.25,  # A balanced form for Focal Loss
            loss_weight=1.0),  # Loss weight of the classification branch
        loss_bbox=dict(  # Config of loss function for the regression branch
            type='L1Loss',  # Type of loss
            loss_weight=1.0)),  # Loss weight of the regression branch
    train_cfg=dict(  # Config of training hyperparameters
        assigner=dict(  # Config of assigner
            type='MaxIoUAssigner',  # Type of assigner
            pos_iou_thr=0.5,  # IoU >= threshold 0.5 will be taken as positive samples
            neg_iou_thr=0.4,  # IoU < threshold 0.4 will be taken as negative samples
            min_pos_iou=0,  # The minimal IoU threshold to take boxes as positive samples
            ignore_iof_thr=-1,  # IoF threshold for ignoring bboxes
            iou_calculator=dict(type='RBboxOverlaps2D')),  # Type of Calculator for IoU
        allowed_border=-1,  # The border allowed after padding for valid anchors.
        pos_weight=-1,  # The weight of positive samples during training.
        debug=False),  # Whether to set the debug mode
    test_cfg=dict(  # Config of testing hyperparameters
        nms_pre=2000,  # The number of boxes before NMS
        min_bbox_size=0,  # The allowed minimal box size
        score_thr=0.05,  # Threshold to filter out boxes
        nms=dict(iou_thr=0.1), # NMS threshold
        max_per_img=2000))  # The number of boxes to be kept after NMS.
dataset_type = 'DOTADataset'  # Dataset type, this will be used to define the dataset
data_root = '../datasets/split_1024_dota1_0/'  # Root path of data
img_norm_cfg = dict(  # Image normalization config to normalize the input images
    mean=[123.675, 116.28, 103.53],  # Mean values used to pre-training the pre-trained backbone models
    std=[58.395, 57.12, 57.375],  # Standard variance used to pre-training the pre-trained backbone models
    to_rgb=True)  # The channel orders of image used to pre-training the pre-trained backbone models
train_pipeline = [  # Training pipeline
    dict(type='LoadImageFromFile'),  # First pipeline to load images from file path
    dict(type='LoadAnnotations',  # Second pipeline to load annotations for current image
         with_bbox=True),  # Whether to use bounding box, True for detection
    dict(type='RResize',  # Augmentation pipeline that resize the images and their annotations
         img_scale=(1024, 1024)),  # The largest scale of image
    dict(type='RRandomFlip',  # Augmentation pipeline that flip the images and their annotations
         flip_ratio=0.5,  # The ratio or probability to flip
         version='oc'),  # The angle version
    dict(
        type='Normalize',  # Augmentation pipeline that normalize the input images
        mean=[123.675, 116.28, 103.53],  # These keys are the same of img_norm_cfg since the
        std=[58.395, 57.12, 57.375],  # keys of img_norm_cfg are used here as arguments
        to_rgb=True),
    dict(type='Pad',  # Padding config
         size_divisor=32),  # The number the padded images should be divisible
    dict(type='DefaultFormatBundle'),  # Default format bundle to gather data in the pipeline
    dict(type='Collect',  # Pipeline that decides which keys in the data should be passed to the detector
         keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),  # First pipeline to load images from file path
    dict(
        type='MultiScaleFlipAug',  # An encapsulation that encapsulates the testing augmentations
        img_scale=(1024, 1024),  # Decides the largest scale for testing, used for the Resize pipeline
        flip=False,  # Whether to flip images during testing
        transforms=[
            dict(type='RResize'),  # Use resize augmentation
            dict(
                type='Normalize',  # Normalization config, the values are from img_norm_cfg
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad',  # Padding config to pad images divisible by 32.
                 size_divisor=32),
            dict(type='DefaultFormatBundle'),  # Default format bundle to gather data in the pipeline
            dict(type='Collect',  # Collect pipeline that collect necessary keys for testing.
                 keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,  # Batch size of a single GPU
    workers_per_gpu=2,  # Worker to pre-fetch data for each single GPU
    train=dict(  # Train dataset config
        type='DOTADataset',  # Type of dataset
        ann_file=
        '../datasets/split_1024_dota1_0/trainval/annfiles/',  # Path of annotation file
        img_prefix=
        '../datasets/split_1024_dota1_0/trainval/images/',  # Prefix of image path
        pipeline=[  # pipeline, this is passed by the train_pipeline created before.
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='RResize', img_scale=(1024, 1024)),
            dict(type='RRandomFlip', flip_ratio=0.5, version='oc'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        version='oc'),
    val=dict(  # Validation dataset config
        type='DOTADataset',
        ann_file=
        '../datasets/split_1024_dota1_0/trainval/annfiles/',
        img_prefix=
        '../datasets/split_1024_dota1_0/trainval/images/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1024, 1024),
                flip=False,
                transforms=[
                    dict(type='RResize'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='DefaultFormatBundle'),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        version='oc'),
    test=dict(  # Test dataset config, modify the ann_file for test-dev/test submission
        type='DOTADataset',
        ann_file=
        '../datasets/split_1024_dota1_0/test/images/',
        img_prefix=
        '../datasets/split_1024_dota1_0/test/images/',
        pipeline=[  # Pipeline is passed by test_pipeline created before
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1024, 1024),
                flip=False,
                transforms=[
                    dict(type='RResize'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='DefaultFormatBundle'),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        version='oc'))
evaluation = dict(  # The config to build the evaluation hook
    interval=12,  # Evaluation interval
    metric='mAP')  # Metrics used during evaluation
optimizer = dict(  # Config used to build optimizer
    type='SGD',  # Type of optimizers
    lr=0.0025,  # Learning rate of optimizers
    momentum=0.9,  # Momentum
    weight_decay=0.0001)  # Weight decay of SGD
optimizer_config = dict(  # Config used to build the optimizer hook
    grad_clip=dict(
        max_norm=35,
        norm_type=2))
lr_config = dict(  # Learning rate scheduler config used to register LrUpdater hook
    policy='step',  # The policy of scheduler
    warmup='linear',  # The warmup policy, also support `exp` and `constant`.
    warmup_iters=500,  # The number of iterations for warmup
    warmup_ratio=0.3333333333333333,  # The ratio of the starting learning rate used for warmup
    step=[8, 11])  # Steps to decay the learning rate
runner = dict(
    type='EpochBasedRunner',  # Type of runner to use (i.e. IterBasedRunner or EpochBasedRunner)
    max_epochs=12) # Runner that runs the workflow in total max_epochs. For IterBasedRunner use `max_iters`
checkpoint_config = dict(  # Config to set the checkpoint hook
    interval=12)  # The save interval is 12
log_config = dict(  # config to register logger hook
    interval=50,  # Interval to print the log
    hooks=[
        # dict(type='TensorboardLoggerHook')  # The Tensorboard logger is also supported
        dict(type='TextLoggerHook')
    ])  # The logger used to record the training process.
dist_params = dict(backend='nccl')  # Parameters to setup distributed training, the port can also be set.
log_level = 'INFO'  # The level of logging.
load_from = None  # load models as a pre-trained model from a given path. This will not resume training.
resume_from = None  # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.
workflow = [('train', 1)]  # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 12 epochs according to the total_epochs.
work_dir = './work_dirs/rotated_retinanet_hbb_r50_fpn_1x_dota_oc'  # Directory to save the model checkpoints and logs for the current experiments.

4、在配置中使用中间变量

一些中间变量在配置文件中使用，比如数据集中的train_pipeline/test_pipeline。值得注意的是，当修改子配置中的中间变量时，用户需要将中间变量再次传递到相应的字段中。例如，我们希望使用离线多尺度策略来训练rol- trans。Train_pipeline是我们想修改的中间变量。

_base_ = ['./roi_trans_r50_fpn_1x_dota_le90.py']

data_root = '../datasets/split_ms_dota1_0/'
angle_version = 'le90'
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='RResize', img_scale=(1024, 1024)),
    dict(
        type='RRandomFlip',
        flip_ratio=[0.25, 0.25, 0.25],
        direction=['horizontal', 'vertical', 'diagonal'],
        version=angle_version),
    dict(
        type='PolyRandomRotate',
        rotate_ratio=0.5,
        angles_range=180,
        auto_bound=False,
        version=angle_version),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
data = dict(
    train=dict(
        pipeline=train_pipeline,
        ann_file=data_root + 'trainval/annfiles/',
        img_prefix=data_root + 'trainval/images/'),
    val=dict(
        ann_file=data_root + 'trainval/annfiles/',
        img_prefix=data_root + 'trainval/images/'),
    test=dict(
        ann_file=data_root + 'test/images/',
        img_prefix=data_root + 'test/images/'))

我们首先定义新的train_pipeline/test_pipeline，并将它们传递给数据。
类似地，如果我们想从SyncBN切换到BN或MMSyncBN，我们需要替换配置中的每个norm_cfg。

_base_ = './roi_trans_r50_fpn_1x_dota_le90.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
    backbone=dict(norm_cfg=norm_cfg),
    neck=dict(norm_cfg=norm_cfg),
    ...)