了解配置文件¶
MMDetection 和其他 OpenMMLab 库使用 MMEngine 的配置文件系统。它具有模块化和继承设计，方便进行各种实验。
配置文件内容¶
MMDetection 使用模块化设计，所有具有不同功能的模块都可以通过配置文件进行配置。以 Mask R-CNN 为例，我们将根据不同的功能模块介绍配置文件中的每个字段。
模型配置¶
在 MMDetection 的配置文件中，我们使用 model 来设置目标检测算法组件。除了神经网络组件，例如 backbone、neck 等，它还需要 data_preprocessor、train_cfg 和 test_cfg。 data_preprocessor 负责处理数据加载器输出的一批数据。模型配置文件中的 train_cfg 和 test_cfg 用于组件的训练和测试超参数。
model = dict(
type='MaskRCNN', # The name of detector
data_preprocessor=dict( # The config of data preprocessor, usually includes image normalization and padding
type='DetDataPreprocessor', # The type of the data preprocessor, refer to https://mmdetection.cn/en/latest/api.html#mmdet.models.data_preprocessors.DetDataPreprocessor
mean=[123.675, 116.28, 103.53], # Mean values used to pre-training the pre-trained backbone models, ordered in R, G, B
std=[58.395, 57.12, 57.375], # Standard variance used to pre-training the pre-trained backbone models, ordered in R, G, B
bgr_to_rgb=True, # whether to convert image from BGR to RGB
pad_mask=True, # whether to pad instance masks
pad_size_divisor=32), # The size of padded image should be divisible by ``pad_size_divisor``
backbone=dict( # The config of backbone
type='ResNet', # The type of backbone network. Refer to https://mmdetection.cn/en/latest/api.html#mmdet.models.backbones.ResNet
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 stage
frozen_stages=1, # The weights in the first stage are frozen
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://mmdetection.cn/en/latest/api.html#mmdet.models.necks.FPN 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://mmdetection.cn/en/latest/api.html#mmdet.models.dense_heads.RPNHead 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/main/mmdet/models/task_modules/prior_generators/anchor_generator.py#L18 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 the methods. Refer to https://github.com/open-mmlab/mmdetection/blob/main/mmdet/models/task_modules/coders/delta_xywh_bbox_coder.py#L13 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. Refer to https://github.com/open-mmlab/mmdetection/blob/main/mmdet/models/losses/cross_entropy_loss.py#L201 for more details
use_sigmoid=True, # RPN usually performs two-class classification, so it usually uses the 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/main/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',
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/main/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py#L13 for details.
roi_layer=dict( # Config of RoI Layer
type='RoIAlign', # Type of RoI Layer, DeformRoIPoolingPack and ModulatedDeformRoIPoolingPack are also supported. Refer to https://mmcv.readthedocs.io/en/latest/api.html#mmcv.ops.RoIAlign 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 with 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/main/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py#L220 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 the 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://mmdetection.cn/en/latest/api.html#mmdet.models.roi_heads.FCNMaskHead 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/main/mmdet/models/task_modules/assigners/max_iou_assigner.py#L14 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/main/mmdet/models/task_modules/samplers/random_sampler.py#L14 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/main/mmdet/models/task_modules/assigners/max_iou_assigner.py#L14 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/main/mmdet/models/task_modules/samplers/random_sampler.py#L14 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
数据集和评估器配置¶
数据加载器 是 运行器 的训练、验证和测试所必需的。需要设置数据集和数据管道来构建数据加载器。由于这部分的复杂性，我们使用中间变量来简化数据加载器配置文件的编写。
dataset_type = 'CocoDataset' # Dataset type, this will be used to define the dataset
data_root = 'data/coco/' # Root path of data
backend_args = None # Arguments to instantiate the corresponding file backend
train_pipeline = [ # Training data processing pipeline
dict(type='LoadImageFromFile', backend_args=backend_args), # 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=True), # Whether to convert the polygon mask to instance mask, set False for acceleration and to save memory
dict(
type='Resize', # Pipeline that resizes the images and their annotations
scale=(1333, 800), # The largest scale of the images
keep_ratio=True # Whether to keep the ratio between height and width
),
dict(
type='RandomFlip', # Augmentation pipeline that flips the images and their annotations
prob=0.5), # The probability to flip
dict(type='PackDetInputs') # Pipeline that formats the annotation data and decides which keys in the data should be packed into data_samples
]
test_pipeline = [ # Testing data processing pipeline
dict(type='LoadImageFromFile', backend_args=backend_args), # First pipeline to load images from file path
dict(type='Resize', scale=(1333, 800), keep_ratio=True), # Pipeline that resizes the images
dict(
type='PackDetInputs', # Pipeline that formats the annotation data and decides which keys in the data should be packed into data_samples
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict( # Train dataloader config
batch_size=2, # Batch size of a single GPU
num_workers=2, # Worker to pre-fetch data for each single GPU
persistent_workers=True, # If ``True``, the dataloader will not shut down the worker processes after an epoch end, which can accelerate training speed.
sampler=dict( # training data sampler
type='DefaultSampler', # DefaultSampler which supports both distributed and non-distributed training. Refer to https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.dataset.DefaultSampler.html#mmengine.dataset.DefaultSampler
shuffle=True), # randomly shuffle the training data in each epoch
batch_sampler=dict(type='AspectRatioBatchSampler'), # Batch sampler for grouping images with similar aspect ratio into a same batch. It can reduce GPU memory cost.
dataset=dict( # Train dataset config
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_train2017.json', # Path of annotation file
data_prefix=dict(img='train2017/'), # Prefix of image path
filter_cfg=dict(filter_empty_gt=True, min_size=32), # Config of filtering images and annotations
pipeline=train_pipeline,
backend_args=backend_args))
val_dataloader = dict( # Validation dataloader config
batch_size=1, # Batch size of a single GPU. If batch-size > 1, the extra padding area may influence the performance.
num_workers=2, # Worker to pre-fetch data for each single GPU
persistent_workers=True, # If ``True``, the dataloader will not shut down the worker processes after an epoch end, which can accelerate training speed.
drop_last=False, # Whether to drop the last incomplete batch, if the dataset size is not divisible by the batch size
sampler=dict(
type='DefaultSampler',
shuffle=False), # not shuffle during validation and testing
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True, # Turn on the test mode of the dataset to avoid filtering annotations or images
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader # Testing dataloader config
评估器 用于计算训练模型在验证集和测试集上的指标。评估器的配置文件包含一个或多个指标配置文件。
val_evaluator = dict( # Validation evaluator config
type='CocoMetric', # The coco metric used to evaluate AR, AP, and mAP for detection and instance segmentation
ann_file=data_root + 'annotations/instances_val2017.json', # Annotation file path
metric=['bbox', 'segm'], # Metrics to be evaluated, `bbox` for detection and `segm` for instance segmentation
format_only=False,
backend_args=backend_args)
test_evaluator = val_evaluator # Testing evaluator config
由于测试集没有标注文件，MMDetection 中的 test_dataloader 和 test_evaluator 配置通常等于 val 的。如果要保存测试集上的检测结果，可以这样编写配置文件
# inference on test dataset and
# format the output results for submission.
test_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'annotations/image_info_test-dev2017.json',
data_prefix=dict(img='test2017/'),
test_mode=True,
pipeline=test_pipeline))
test_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/image_info_test-dev2017.json',
metric=['bbox', 'segm'], # Metrics to be evaluated
format_only=True, # Only format and save the results to coco json file
outfile_prefix='./work_dirs/coco_detection/test') # The prefix of output json files
训练和测试配置¶
MMEngine 的运行器使用循环来控制训练、验证和测试过程。用户可以使用这些字段设置最大训练 epoch 和验证间隔。
train_cfg = dict(
type='EpochBasedTrainLoop', # The training loop type. Refer to https://github.com/open-mmlab/mmengine/blob/main/mmengine/runner/loops.py
max_epochs=12, # Maximum training epochs
val_interval=1) # Validation intervals. Run validation every epoch.
val_cfg = dict(type='ValLoop') # The validation loop type
test_cfg = dict(type='TestLoop') # The testing loop type
优化配置¶
optim_wrapper 是用于配置优化相关设置的字段。优化器包装器不仅提供优化器的功能，还支持梯度裁剪、混合精度训练等功能。在 优化器包装器教程 中了解更多。
optim_wrapper = dict( # Optimizer wrapper config
type='OptimWrapper', # Optimizer wrapper type, switch to AmpOptimWrapper to enable mixed precision training.
optimizer=dict( # Optimizer config. Support all kinds of optimizers in PyTorch. Refer to https://pytorch.ac.cn/docs/stable/optim.html#algorithms
type='SGD', # Stochastic gradient descent optimizer
lr=0.02, # The base learning rate
momentum=0.9, # Stochastic gradient descent with momentum
weight_decay=0.0001), # Weight decay of SGD
clip_grad=None, # Gradient clip option. Set None to disable gradient clip. Find usage in https://mmengine.readthedocs.io/en/latest/tutorials/optimizer.html
)
param_scheduler 是一个配置调整优化超参数方法（如学习率和动量）的字段。用户可以组合多个调度器来创建所需的参数调整策略。在 参数调度器教程 和 参数调度器 API 文档 中了解更多。
param_scheduler = [
# Linear learning rate warm-up scheduler
dict(
type='LinearLR', # Use linear policy to warmup learning rate
start_factor=0.001, # The ratio of the starting learning rate used for warmup
by_epoch=False, # The warmup learning rate is updated by iteration
begin=0, # Start from the first iteration
end=500), # End the warmup at the 500th iteration
# The main LRScheduler
dict(
type='MultiStepLR', # Use multi-step learning rate policy during training
by_epoch=True, # The learning rate is updated by epoch
begin=0, # Start from the first epoch
end=12, # End at the 12th epoch
milestones=[8, 11], # Epochs to decay the learning rate
gamma=0.1) # The learning rate decay ratio
]
钩子配置¶
用户可以将钩子附加到训练、验证和测试循环，以便在运行时插入一些操作。有两个不同的钩子字段，一个是 default_hooks，另一个是 custom_hooks。
default_hooks 是一个钩子配置文件的字典，它们是运行时必须需要的钩子。它们具有默认优先级，不应修改。如果未设置，运行器将使用默认值。要禁用默认钩子，用户可以将其配置设置为 None。在 HOOK 中了解更多。
default_hooks = dict(
timer=dict(type='IterTimerHook'), # Update the time spent during iteration into message hub
logger=dict(type='LoggerHook', interval=50), # Collect logs from different components of Runner and write them to terminal, JSON file, tensorboard and wandb .etc
param_scheduler=dict(type='ParamSchedulerHook'), # update some hyper-parameters of optimizer
checkpoint=dict(type='CheckpointHook', interval=1), # Save checkpoints periodically
sampler_seed=dict(type='DistSamplerSeedHook'), # Ensure distributed Sampler shuffle is active
visualization=dict(type='DetVisualizationHook')) # Detection Visualization Hook. Used to visualize validation and testing process prediction results
custom_hooks 是所有其他钩子配置文件的列表。用户可以开发自己的钩子并将它们插入到这个字段中。
custom_hooks = []
运行时配置¶
default_scope = 'mmdet' # The default registry scope to find modules. Refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html
env_cfg = dict(
cudnn_benchmark=False, # Whether to enable cudnn benchmark
mp_cfg=dict( # Multi-processing config
mp_start_method='fork', # Use fork to start multi-processing threads. 'fork' usually faster than 'spawn' but maybe unsafe. See discussion in https://github.com/pytorch/pytorch/issues/1355
opencv_num_threads=0), # Disable opencv multi-threads to avoid system being overloaded
dist_cfg=dict(backend='nccl'), # Distribution configs
)
vis_backends = [dict(type='LocalVisBackend')] # Visualization backends. Refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/visualization.html
visualizer = dict(
type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')
log_processor = dict(
type='LogProcessor', # Log processor to process runtime logs
window_size=50, # Smooth interval of log values
by_epoch=True) # Whether to format logs with epoch type. Should be consistent with the train loop's type.
log_level = 'INFO' # The level of logging.
load_from = None # Load model checkpoint as a pre-trained model from a given path. This will not resume training.
resume = False # Whether to resume from the checkpoint defined in `load_from`. If `load_from` is None, it will resume the latest checkpoint in the `work_dir`.
基于迭代的配置¶
除了基于 epoch 的训练循环之外，MMEngine 的运行器还提供了基于迭代的训练循环。要使用基于迭代的训练，用户应该修改 train_cfg、param_scheduler、train_dataloader、default_hooks 和 log_processor。以下是如何将基于 epoch 的 RetinaNet 配置更改为基于迭代的示例：configs/retinanet/retinanet_r50_fpn_90k_coco.py
# Iter-based training config
train_cfg = dict(
_delete_=True, # Ignore the base config setting (optional)
type='IterBasedTrainLoop', # Use iter-based training loop
max_iters=90000, # Maximum iterations
val_interval=10000) # Validation interval
# Change the scheduler to iter-based
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=90000,
by_epoch=False,
milestones=[60000, 80000],
gamma=0.1)
]
# Switch to InfiniteSampler to avoid dataloader restart
train_dataloader = dict(sampler=dict(type='InfiniteSampler'))
# Change the checkpoint saving interval to iter-based
default_hooks = dict(checkpoint=dict(by_epoch=False, interval=10000))
# Change the log format to iter-based
log_processor = dict(by_epoch=False)
配置文件继承¶
在 config/_base_ 下有 4 种基本组件类型，数据集、模型、调度、默认运行时。许多方法可以通过这些模型之一轻松构建，例如 Faster R-CNN、Mask R-CNN、Cascade R-CNN、RPN、SSD。由 _base_ 中的组件组成的配置被称为原始配置。
对于同一个文件夹下的所有配置文件，建议只保留一个原始配置文件。所有其他配置文件都应该从原始配置文件继承。这样，继承级别最大为 3。
为了便于理解，我们建议贡献者从现有方法继承。例如，如果对 Faster R-CNN 做了一些修改，用户可以首先通过指定 _base_ = ../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py 来继承基本的 Faster R-CNN 结构，然后修改配置文件中必要的字段。
如果正在构建一种与现有方法没有任何结构共享的全新方法，可以创建一个名为 xxx_rcnn 的文件夹，该文件夹位于 configs 下，
有关详细文档，请参阅 mmengine 配置教程。
通过设置 _base_ 字段，我们可以设置当前配置文件继承自哪些文件。
当 _base_ 是文件路径的字符串时，表示从一个配置文件继承内容。
_base_ = './mask-rcnn_r50_fpn_1x_coco.py'
当 _base_ 是多个文件路径的列表时，表示从多个文件继承。
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
如果要检查配置文件，可以运行 python tools/misc/print_config.py /PATH/TO/CONFIG 来查看完整的配置。
忽略基本配置文件中的一些字段¶
有时，你可能想设置 _delete_=True 来忽略基本配置文件中的一些字段。你可以参考 mmengine 配置教程 来获得一个简单的说明。
例如，在 MMDetection 中，要使用以下配置文件更改 Mask R-CNN 的主干网络。
model = dict(
type='MaskRCNN',
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(...),
rpn_head=dict(...),
roi_head=dict(...))
ResNet 和 HRNet 使用不同的关键字来构建。
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
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))),
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
neck=dict(...))
_delete_=True 将会用新的键替换 backbone 字段中的所有旧键。
在配置文件中使用中间变量¶
配置文件中使用了一些中间变量，例如数据集中的 train_pipeline/test_pipeline。需要注意的是，当修改子配置文件中的中间变量时，需要将中间变量再次传递到相应的字段中。例如，我们想使用多尺度策略训练 Mask R-CNN。 train_pipeline/test_pipeline 是我们想要修改的中间变量。
_base_ = './mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomResize', scale=[(1333, 640), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
首先定义新的 train_pipeline/test_pipeline 并将其传递到数据加载器字段。
类似地，如果我们想从 SyncBN 切换到 BN 或 MMSyncBN，我们需要替换配置文件中的所有 norm_cfg。
_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),
...)
在 _base_ 文件中重用变量¶
如果用户想要重用基本文件中的变量，可以使用 {{_base_.xxx}} 获取对应变量的副本。例如
_base_ = './mask-rcnn_r50_fpn_1x_coco.py'
a = {{_base_.model}} # Variable `a` is equal to the `model` defined in `_base_`
通过脚本参数修改配置文件¶
当使用 tools/train.py 或 tools/test.py 提交作业时，可以指定 --cfg-options 来修改配置文件。
更新字典链的配置文件键。
配置文件选项可以按照原始配置文件中字典键的顺序指定。例如，
--cfg-options model.backbone.norm_eval=False将模型骨干网络中的所有 BN 模块更改为train模式。更新配置文件列表中的键。
一些配置文件字典在您的配置文件中被组成一个列表。例如，训练流水线
train_dataloader.dataset.pipeline通常是一个列表，例如[dict(type='LoadImageFromFile'), ...]。如果您想将流水线中的'LoadImageFromFile'更改为'LoadImageFromNDArray'，您可以指定--cfg-options data.train.pipeline.0.type=LoadImageFromNDArray。更新列表/元组的值。
如果要更新的值是列表或元组。例如，配置文件通常设置
model.data_preprocessor.mean=[123.675, 116.28, 103.53]。如果您想更改均值，您可以指定--cfg-options model.data_preprocessor.mean="[127,127,127]"。请注意，引号"是支持列表/元组数据类型所必需的，并且在指定的值中不允许使用空格。
配置文件命名风格¶
我们遵循以下风格命名配置文件。建议贡献者遵循相同的风格。
{algorithm name}_{model component names [component1]_[component2]_[...]}_{training settings}_{training dataset information}_{testing dataset information}.py
文件名分为五个部分。所有部分和组件都用 _ 连接，每个部分或组件的单词用 - 连接。
{algorithm name}: 算法的名称。它可以是检测器名称，例如faster-rcnn、mask-rcnn等。或者可以是半监督或知识蒸馏算法，例如soft-teacher、lad等。{model component names}: 算法中使用的组件的名称，例如骨干网络、颈部等。例如，r50-caffe_fpn_gn-head表示在算法中使用 caffe 风格的 ResNet50、FPN 和带 Group Norm 的检测头。{training settings}: 训练设置的信息，例如批大小、增强、损失技巧、调度器和 epoch/迭代。例如：4xb4-mixup-giou-coslr-100e表示使用 8 个 GPU x 每个 GPU 4 个图像、mixup 增强、GIoU 损失、余弦退火学习率，并训练 100 个 epoch。一些缩写{gpu x batch_per_gpu}: GPU 和每个 GPU 的样本数。bN表示每个 GPU 的批大小为 N。例如，4xb4是 4 个 GPU x 每个 GPU 4 个图像的简写。如果未提及，默认使用8xb2。{schedule}: 训练计划，选项为1x、2x、20e等。1x和2x分别表示 12 个 epoch 和 24 个 epoch。20e在级联模型中采用，表示 20 个 epoch。对于1x/2x，初始学习率在第 8/16 个和第 11/22 个 epoch 分别下降 10 倍。对于20e，初始学习率在第 16 个和第 19 个 epoch 分别下降 10 倍。
{training dataset information}: 训练数据集名称，例如coco、coco-panoptic、cityscapes、voc-0712、wider-face。{testing dataset information}(可选): 在一个数据集上训练但在另一个数据集上测试的模型的测试数据集名称。如果没有提及，则表示模型是在同一个数据集类型上训练和测试的。