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model = dict(
type='MaskRCNN', # 检测器模型的名称
pretrained=
'torchvision://resnet50', # ImageNet预训练的backbone加载
backbone=dict( # backbone的配置
type='ResNet', # backbone类型,mmdet/models/backbones/resnet.py#304
depth=50, #backbone深度，ResNet和ResNext通常为50或101。.
num_stages=4, # backbone的阶段个数.
out_indices=(0, 1, 2, 3), # 每个阶段生成的输出特征图的索引
frozen_stages=1, # 前1个阶段的权重
norm_cfg=dict( # 归一化层的配置
type='BN', # norm layer的类型, BN or GN
requires_grad=True), # 是否训练BN中的gamma和betaBN
norm_eval=True, # 是否冻结统计 BN
style='pytorch'), # 主干的样式“ pytorch”表示第2步的步幅为3x3转换，“ caffe”表示第2步的步幅为1x1的转换。
neck=dict(
type='FPN', # neck是FPN. 还支持 'NASFPN', 'PAFPN', etc.
in_channels=[256, 512, 1024, 2048], # 输入通道，与backbone的输出通道一致
out_channels=256, # 金字塔特征图的每一级的输出通道
num_outs=5), # 输出数量
...
train_cfg = dict( # rpn和rcnn的训练超参数配置
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
...
)
test_cfg = dict( # 用于测试rpn和rcnn的超参数的配置
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' # 数据集类型
data_root = 'data/coco/' # 数据的根路径
img_norm_cfg = dict( # 图像规范化配置
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
)
train_pipeline = [ # 训练管道
dict(type='LoadImageFromFile'), # 从文件路径加载图像
dict(
type='LoadAnnotations', # 为当前图像加载注释
with_bbox=True, #是否使用边界框, True for detection
with_mask=True, #是否使用实例掩码, True for instance segmentation
poly2mask=False), # Whether to convert the polygon mask to instance mask, set False for acceleration and to save memory
...
]
test_pipeline = [ # 测试管道
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
...
]
data = dict(
samples_per_gpu=2, # 单个GPU的批处理大小
workers_per_gpu=2, # 为每个GPU预取数据的辅助进程
train=dict( # 训练数据集配置
type='CocoDataset',
ann_file='data/coco/annotations/instances_train2017.json', # 批注文件的路径
img_prefix='data/coco/train2017/', # 图像路径前缀
pipeline=[ # pipeline, this is passed by the train_pipeline created before.
...
]),
val=dict( # 验证数据集配置
type='CocoDataset',
ann_file='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
pipeline=[ # Pipeline is passed by test_pipeline created before
...
]),
test=dict( #测试数据集配置, 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
...
],
samples_per_gpu=2 # Batch size of a single GPU used in testing
))
evaluation = dict( # 构建评价值钩子的配置,
interval=1, # Evaluation interval
metric=['bbox', 'segm']) # Metrics used during evaluation
optimizer = dict( # 构建优化器的配置
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( # 构建优化器挂钩的配置
grad_clip=None) # Most of the methods do not use gradient clip
lr_config = dict( # 用于注册LrUpdater钩子的学习率调度程序配置
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', max_epochs=12) # Runner that runs the workflow in total max_epochs
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
dist_params = dict(backend='nccl') # 参数设置分布式训练，端口也可以设置
load_from = None # 从给定路径将模型作为预先训练的模型加载。这将不会恢复训练。
resume_from = None # 从给定路径恢复检查点开始训练
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.
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