Tutorial 1: Learn about Configs

We incorporate modular and inheritance design into our config system, which is convenient to conduct various experiments. If you wish to inspect the config file, you may run python tools/misc/print_config.py /PATH/TO/CONFIG to see the complete config.

Modify config through script arguments

When submitting jobs using “tools/train.py” or “tools/test.py”, you may specify --cfg-options to in-place modify the config.

  • Update config keys of dict chains.

    The config options can be specified following the order of the dict keys in the original config. For example, --cfg-options model.backbone.norm_eval=False changes the all BN modules in model backbones to train mode.

  • Update keys inside a list of configs.

    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.

  • Update values of list/tuples.

    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 value.

Config File Structure

There are 4 basic component types under config/_base_, dataset, model, schedule, default_runtime. Many methods could be easily constructed with one of each like Faster R-CNN, Mask R-CNN, Cascade R-CNN, RPN, SSD. The configs that are composed by components from _base_ are called primitive.

For all configs under the same folder, it is recommended to have only one primitive config. All other configs should inherit from the primitive config. In this way, the maximum of inheritance level is 3.

For easy understanding, we recommend contributors to inherit from existing methods. For example, if some modification is made base on Faster R-CNN, user may first inherit the basic Faster R-CNN structure by specifying _base_ = ../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py, then modify the necessary fields in the config files.

If you are building an entirely new method that does not share the structure with any of the existing methods, you may create a folder xxx_rcnn under configs,

Please refer to mmcv for detailed documentation.

Config Name Style

We follow the below style to name config files. Contributors are advised to follow the same style.

{model}_[model setting]_{backbone}_{neck}_[norm setting]_[misc]_[gpu x batch_per_gpu]_{schedule}_{dataset}

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

  • {model}: model type like faster_rcnn, mask_rcnn, etc.

  • [model setting]: specific setting for some model, like without_semantic for htc, moment for reppoints, etc.

  • {backbone}: backbone type like r50 (ResNet-50), x101 (ResNeXt-101).

  • {neck}: neck type like fpn, pafpn, nasfpn, c4.

  • [norm_setting]: bn (Batch Normalization) is used unless specified, other norm layer type 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.

  • [misc]: miscellaneous setting/plugins of model, e.g. dconv, gcb, attention, albu, mstrain.

  • [gpu x batch_per_gpu]: GPUs and samples per GPU, 8x2 is used by default.

  • {schedule}: training schedule, options are 1x, 2x, 20e, etc. 1x and 2x means 12 epochs and 24 epochs respectively. 20e is adopted in cascade models, which denotes 20 epochs. For 1x/2x, initial learning rate decays by a factor of 10 at the 8/16th and 11/22th epochs. For 20e, initial learning rate decays by a factor of 10 at the 16th and 19th epochs.

  • {dataset}: dataset like coco, cityscapes, voc_0712, wider_face.

Deprecated train_cfg/test_cfg

The train_cfg and test_cfg are deprecated in config file, please specify them in the model config. The original config structure is as below.

# deprecated
model = dict(
   type=...,
   ...
)
train_cfg=dict(...)
test_cfg=dict(...)

The migration example is as below.

# recommended
model = dict(
   type=...,
   ...
   train_cfg=dict(...),
   test_cfg=dict(...),
)

An Example of Mask R-CNN

To help the users have a basic idea of a complete config and the modules in a modern detection system, we make brief comments on the config of Mask R-CNN using ResNet50 and FPN as the following. For more detailed usage and the corresponding alternative for each modules, please refer to the API documentation.

model = dict(
    type='MaskRCNN',  # The name of detector
    backbone=dict(  # The config of backbone
        type='ResNet',  # The type of the backbone, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/backbones/resnet.py#L308 for more details.
        depth=50,  # The depth of backbone, usually it is 50 or 101 for ResNet and ResNext backbones.
        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
        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 'NASFPN', 'PAFPN', etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/fpn.py#L10 for more details.
        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
        num_outs=5),  # The number of output scales
    rpn_head=dict(
        type='RPNHead',  # The type of RPN head is 'RPNHead', we also support 'GARPNHead', etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/rpn_head.py#L12 for more details.
        in_channels=256,  # The input channels of each input feature map, this is consistent with the output channels of neck
        feat_channels=256,  # Feature channels of convolutional layers in the head.
        anchor_generator=dict(  # The config of anchor generator
            type='AnchorGenerator',  # Most of methods use AnchorGenerator, SSD Detectors uses `SSDAnchorGenerator`. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/anchor/anchor_generator.py#L10 for more details
            scales=[8],  # Basic scale of the anchor, the area of the anchor in one position of a feature map will be scale * base_sizes
            ratios=[0.5, 1.0, 2.0],  # The ratio between height and width.
            strides=[4, 8, 16, 32, 64]),  # The strides of the anchor generator. This is consistent with the FPN feature strides. The strides will be taken as base_sizes if base_sizes is not set.
        bbox_coder=dict(  # Config of box coder to encode and decode the boxes during training and testing
            type='DeltaXYWHBBoxCoder',  # Type of box coder. 'DeltaXYWHBBoxCoder' is applied for most of methods. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py#L9 for more details.
            target_means=[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]),  # The standard variance used to encode and decode boxes
        loss_cls=dict(  # Config of loss function for the classification branch
            type='CrossEntropyLoss',  # Type of loss for classification branch, we also support FocalLoss etc.
            use_sigmoid=True,  # RPN usually perform two-class classification, so it usually uses sigmoid function.
            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, we also support many IoU Losses and smooth L1-loss, etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/smooth_l1_loss.py#L56 for implementation.
            loss_weight=1.0)),  # Loss weight of the regression branch.
    roi_head=dict(  # RoIHead encapsulates the second stage of two-stage/cascade detectors.
        type='StandardRoIHead',  # Type of the RoI head. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/standard_roi_head.py#L10 for implementation.
        bbox_roi_extractor=dict(  # RoI feature extractor for bbox regression.
            type='SingleRoIExtractor',  # Type of the RoI feature extractor, most of methods uses SingleRoIExtractor. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/roi_extractors/single_level.py#L10 for details.
            roi_layer=dict(  # Config of RoI Layer
                type='RoIAlign',  # Type of RoI Layer, DeformRoIPoolingPack and ModulatedDeformRoIPoolingPack are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/roi_align/roi_align.py#L79 for details.
                output_size=7,  # The output size of feature maps.
                sampling_ratio=0),  # Sampling ratio when extracting the RoI features. 0 means adaptive ratio.
            out_channels=256,  # output channels of the extracted feature.
            featmap_strides=[4, 8, 16, 32]),  # Strides of multi-scale feature maps. It should be consistent to the architecture of the backbone.
        bbox_head=dict(  # Config of box head in the RoIHead.
            type='Shared2FCBBoxHead',  # Type of the bbox head, Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py#L177 for implementation details.
            in_channels=256,  # Input channels for bbox head. This is consistent with the out_channels in roi_extractor
            fc_out_channels=1024,  # Output feature channels of FC layers.
            roi_feat_size=7,  # Size of RoI features
            num_classes=80,  # Number of classes for classification
            bbox_coder=dict(  # Box coder used in the second stage.
                type='DeltaXYWHBBoxCoder',  # Type of box coder. 'DeltaXYWHBBoxCoder' is applied for most of methods.
                target_means=[0.0, 0.0, 0.0, 0.0],  # Means used to encode and decode box
                target_stds=[0.1, 0.1, 0.2, 0.2]),  # Standard variance for encoding and decoding. It is smaller since the boxes are more accurate. [0.1, 0.1, 0.2, 0.2] is a conventional setting.
            reg_class_agnostic=False,  # Whether the regression is class agnostic.
            loss_cls=dict(  # Config of loss function for the classification branch
                type='CrossEntropyLoss',  # Type of loss for classification branch, we also support FocalLoss etc.
                use_sigmoid=False,  # Whether to use sigmoid.
                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, we also support many IoU Losses and smooth L1-loss, etc.
                loss_weight=1.0)),  # Loss weight of the regression branch.
        mask_roi_extractor=dict(  # RoI feature extractor for mask generation.
            type='SingleRoIExtractor',  # Type of the RoI feature extractor, most of methods uses SingleRoIExtractor.
            roi_layer=dict(  # Config of RoI Layer that extracts features for instance segmentation
                type='RoIAlign',  # Type of RoI Layer, DeformRoIPoolingPack and ModulatedDeformRoIPoolingPack are also supported
                output_size=14,  # The output size of feature maps.
                sampling_ratio=0),  # Sampling ratio when extracting the RoI features.
            out_channels=256,  # Output channels of the extracted feature.
            featmap_strides=[4, 8, 16, 32]),  # Strides of multi-scale feature maps.
        mask_head=dict(  # Mask prediction head
            type='FCNMaskHead',  # Type of mask head, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py#L21 for implementation details.
            num_convs=4,  # Number of convolutional layers in mask head.
            in_channels=256,  # Input channels, should be consistent with the output channels of mask roi extractor.
            conv_out_channels=256,  # Output channels of the convolutional layer.
            num_classes=80,  # Number of class to be segmented.
            loss_mask=dict(  # Config of loss function for the mask branch.
                type='CrossEntropyLoss',  # Type of loss used for segmentation
                use_mask=True,  # Whether to only train the mask in the correct class.
                loss_weight=1.0))))  # Loss weight of mask branch.
    train_cfg = dict(  # Config of training hyperparameters for rpn and rcnn
        rpn=dict(  # Training config of rpn
            assigner=dict(  # Config of assigner
                type='MaxIoUAssigner',  # Type of assigner, MaxIoUAssigner is used for many common detectors. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
                pos_iou_thr=0.7,  # IoU >= threshold 0.7 will be taken as positive samples
                neg_iou_thr=0.3,  # IoU < threshold 0.3 will be taken as negative samples
                min_pos_iou=0.3,  # The minimal IoU threshold to take boxes as positive samples
                match_low_quality=True,  # Whether to match the boxes under low quality (see API doc for more details).
                ignore_iof_thr=-1),  # IoF threshold for ignoring bboxes
            sampler=dict(  # Config of positive/negative sampler
                type='RandomSampler',  # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
                num=256,  # Number of samples
                pos_fraction=0.5,  # The ratio of positive samples in the total samples.
                neg_pos_ub=-1,  # The upper bound of negative samples based on the number of positive samples.
                add_gt_as_proposals=False),  # Whether add GT as proposals after sampling.
            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
        rpn_proposal=dict(  # The config to generate proposals during training
            nms_across_levels=False,  # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
            nms_pre=2000,  # The number of boxes before NMS
            nms_post=1000,  # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
            max_per_img=1000,  # The number of boxes to be kept after NMS.
            nms=dict( # Config of NMS
                type='nms',  # Type of NMS
                iou_threshold=0.7 # NMS threshold
                ),
            min_bbox_size=0),  # The allowed minimal box size
        rcnn=dict(  # The config for the roi heads.
            assigner=dict(  # Config of assigner for second stage, this is different for that in rpn
                type='MaxIoUAssigner',  # Type of assigner, MaxIoUAssigner is used for all roi_heads for now. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
                pos_iou_thr=0.5,  # IoU >= threshold 0.5 will be taken as positive samples
                neg_iou_thr=0.5,  # IoU < threshold 0.5 will be taken as negative samples
                min_pos_iou=0.5,  # The minimal IoU threshold to take boxes as positive samples
                match_low_quality=False,  # Whether to match the boxes under low quality (see API doc for more details).
                ignore_iof_thr=-1),  # IoF threshold for ignoring bboxes
            sampler=dict(
                type='RandomSampler',  # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
                num=512,  # Number of samples
                pos_fraction=0.25,  # The ratio of positive samples in the total samples.
                neg_pos_ub=-1,  # The upper bound of negative samples based on the number of positive samples.
                add_gt_as_proposals=True
            ),  # Whether add GT as proposals after sampling.
            mask_size=28,  # Size of mask
            pos_weight=-1,  # The weight of positive samples during training.
            debug=False))  # Whether to set the debug mode
    test_cfg = dict(  # Config for testing hyperparameters for rpn and rcnn
        rpn=dict(  # The config to generate proposals during testing
            nms_across_levels=False,  # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
            nms_pre=1000,  # The number of boxes before NMS
            nms_post=1000,  # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
            max_per_img=1000,  # The number of boxes to be kept after NMS.
            nms=dict( # Config of NMS
                type='nms',  #Type of NMS
                iou_threshold=0.7 # NMS threshold
                ),
            min_bbox_size=0),  # The allowed minimal box size
        rcnn=dict(  # The config for the roi heads.
            score_thr=0.05,  # Threshold to filter out boxes
            nms=dict(  # Config of NMS in the second stage
                type='nms',  # Type of NMS
                iou_thr=0.5),  # NMS threshold
            max_per_img=100,  # Max number of detections of each image
            mask_thr_binary=0.5))  # Threshold of mask prediction
dataset_type = 'CocoDataset'  # Dataset type, this will be used to define the dataset
data_root = 'data/coco/'  # 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
        with_mask=True,  # Whether to use instance mask, True for instance segmentation
        poly2mask=False),  # Whether to convert the polygon mask to instance mask, set False for acceleration and to save memory
    dict(
        type='Resize',  # Augmentation pipeline that resize the images and their annotations
        img_scale=(1333, 800),  # The largest scale of image
        keep_ratio=True
    ),  # whether to keep the ratio between height and width.
    dict(
        type='RandomFlip',  # Augmentation pipeline that flip the images and their annotations
        flip_ratio=0.5),  # The ratio or probability to flip
    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', 'gt_masks'])
]
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=(1333, 800),  # Decides the largest scale for testing, used for the Resize pipeline
        flip=False,  # Whether to flip images during testing
        transforms=[
            dict(type='Resize',  # Use resize augmentation
                 keep_ratio=True),  # Whether to keep the ratio between height and width, the img_scale set here will be suppressed by the img_scale set above.
            dict(type='RandomFlip'),  # Thought RandomFlip is added in pipeline, it is not used because flip=False
            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 divisable by 32.
                size_divisor=32),
            dict(
                type='ImageToTensor',  # convert image to tensor
                keys=['img']),
            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='CocoDataset',  # Type of dataset, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/coco.py#L19 for details.
        ann_file='data/coco/annotations/instances_train2017.json',  # Path of annotation file
        img_prefix='data/coco/train2017/',  # Prefix of image path
        pipeline=[  # pipeline, this is passed by the train_pipeline created before.
            dict(type='LoadImageFromFile'),
            dict(
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=True,
                poly2mask=False),
            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            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', 'gt_masks'])
        ]),
    val=dict(  # Validation dataset config
        type='CocoDataset',
        ann_file='data/coco/annotations/instances_val2017.json',
        img_prefix='data/coco/val2017/',
        pipeline=[  # Pipeline is passed by test_pipeline created before
            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',
                        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='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(  # Test dataset config, modify the ann_file for test-dev/test submission
        type='CocoDataset',
        ann_file='data/coco/annotations/instances_val2017.json',
        img_prefix='data/coco/val2017/',
        pipeline=[  # Pipeline is passed by test_pipeline created before
            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',
                        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='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        samples_per_gpu=2  # Batch size of a single GPU used in testing
        ))
evaluation = dict(  # The config to build the evaluation hook, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7 for more details.
    interval=1,  # Evaluation interval
    metric=['bbox', 'segm'])  # Metrics used during evaluation
optimizer = dict(  # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch
    type='SGD',  # Type of optimizers, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/optimizer/default_constructor.py#L13 for more details
    lr=0.02,  # Learning rate of optimizers, see detail usages of the parameters in the documentaion of PyTorch
    momentum=0.9,  # Momentum
    weight_decay=0.0001)  # Weight decay of SGD
optimizer_config = dict(  # Config used to build the optimizer hook, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8 for implementation details.
    grad_clip=None)  # Most of the methods do not use gradient clip
lr_config = dict(  # Learning rate scheduler config used to register LrUpdater hook
    policy='step',  # The policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9.
    warmup='linear',  # The warmup policy, also support `exp` and `constant`.
    warmup_iters=500,  # The number of iterations for warmup
    warmup_ratio=
    0.001,  # 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, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.
    interval=1)  # The save interval is 1
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_dir'  # Directory to save the model checkpoints and logs for the current experiments.

FAQ

Ignore some fields in the base configs

Sometimes, you may set _delete_=True to ignore some of fields in base configs. You may refer to mmcv for simple illustration.

In MMDetection, for example, to change the backbone of Mask R-CNN with the following config.

model = dict(
    type='MaskRCNN',
    pretrained='torchvision://resnet50',
    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'),
    neck=dict(...),
    rpn_head=dict(...),
    roi_head=dict(...))

ResNet and HRNet use different keywords to construct.

_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
    pretrained='open-mmlab://msra/hrnetv2_w32',
    backbone=dict(
        _delete_=True,
        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(...))

The _delete_=True would replace all old keys in backbone field with new keys.

Use intermediate variables in configs

Some intermediate variables are used in the configs files, like train_pipeline/test_pipeline in datasets. It’s worth noting that when modifying intermediate variables in the children configs, user need to pass the intermediate variables into corresponding fields again. For example, we would like to use multi scale strategy to train a Mask R-CNN. train_pipeline/test_pipeline are intermediate variable we would like modify.

_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
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, 640), (1333, 672), (1333, 704), (1333, 736),
                   (1333, 768), (1333, 800)],
        multiscale_mode="value",
        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(
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline))

We first define the new train_pipeline/test_pipeline and pass them into data.

Similarly, if we would like to switch from SyncBN to BN or MMSyncBN, we need to substitute every norm_cfg in the config.

_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
    backbone=dict(norm_cfg=norm_cfg),
    neck=dict(norm_cfg=norm_cfg),
    ...)