在配置系统中采用模块化和继承性设计，便于进行各种实验。如果你想查看配置文件, 你可以运行
python tools/misc/print_config.py /PATH/TO/CONFIG to see the complete config.
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
配置选项可以按照原始配置中的dict键的顺序指定。例如,
--cfg-options model.backbone.norm_eval=False
将模型骨干中的所有BN模块改为训练模式。
更新配置列表中的键一些配置字典在你的配置中组成一个列表，例如，培训管道”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.
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)]".
在config/base、dataset、model、schedule、default_runtime下有4种基本组件类型。许多方法可以很容易地构建每个像更快的R-CNN, Mask R-CNN, Cascade R-CNN, RPN, SSD。由_base_中的组件组成的配置称为原语。
对于同一个文件夹下的所有配置，建议只使用一个基本配置。所有其他配置都应该继承自原始配置。这样，继承级别的最大值为3。
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_co .py继承基本的Faster R-CNN结构，然后修改配置文件中必要的字段。
_base_ = [
'../_base_/models/mask_rcnn_swin_fpn.py',
'../_base_/datasets/coco_instance.py', #做目标检测，修改为coco_detection.py
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
如果你正在构建一个全新的方法，它不与任何现有方法共享结构，你可以在configs下创建一个文件夹xxx_rcnPlease refer to mmcv for detailed documentation.
3、配置名称的风格我们遵循下面的风格命名配置文件。建议贡献者遵循相同的风格
{model}_[model setting]_{backbone}_{neck}_[norm setting]_[misc]_[gpu x batch_per_gpu]_{schedule}_{dataset}
{xxx}为必填字段，[yyy]为可选字段。
4、 弃用train_cfg / test_cfg{model}: model type like faster_rcnn, mask_rcnn, etc.模型类型 ，例如 faster_rcnn, mask_rcnn
[model setting]: specific setting for some model, like without_semantic for htc, moment for reppoints, etc.特定型号的设置
{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（模型的其他设置/插件）, 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 ，3x means 36 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(初始学习速率在8/16和11/22时衰减10倍). 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.
train_cfg和test_cfg在配置文件中已弃用，请在模型配置中指定它们。原始配置结构如下所示。
# deprecated
model = dict(
type=...,
...
)
train_cfg=dict(...)
test_cfg=dict(...)
迁移示例如下所示。
recommended
model = dict(
type=...,
...
train_cfg=dict(...),
test_cfg=dict(...),
)
为了帮助用户对现代检测系统的完整配置和模块有一个基本的概念，我们使用ResNet50和FPN对掩码R-CNN的配置做简要的评论如下。要了解每个模块的更详细用法和相应的替代方法，请参阅API文档。
model = dict(
type='MaskRCNN', # The name of detector, detector名字
backbone=dict( # The config of backbone,主干配置
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.骨干阶段数
out_indices=(0, 1, 2, 3), # The index of output feature maps produced in each stages，各阶段产生的输出特征图的索引
frozen_stages=1, # The weights in the first 1 stage are fronzen，第一阶段的权重是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，ImageNet对主干进行了预训练，以便加载
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，输入通道，这与骨干的输出通道是一致的
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 divisible 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 documentation 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.参数设置分布式培训，端口也可以设置。
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.将模型作为预先训练的模型从给定的路径加载。这不会恢复训练。
resume_from = None # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.从一个给定的路径恢复检查点，训练将从检查点保存时开始恢复。
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.[('train'， 1)]表示只有一个工作流，名为'train'的工作流将执行一次。工作流根据total_epoch对模型进行12个epoch的训练。
work_dir = 'work_dir' # Directory to save the model checkpoints and logs for the current experiments.保存当前实验的模型检查点和日志的目录。
首先介绍一下这个配置文件所描述的框架，它是基于resnet50的backbone，有着5个fpn特征层的faster-RCNN目标检测网络，训练迭代次数为标准的12次epoch。
# model settings
model = dict(
type='FasterRCNN', # model类型
pretrained='modelzoo://resnet50', # 预训练模型：imagenet-resnet50
backbone=dict(
type='ResNet', # backbone类型
depth=50, # 网络层数
num_stages=4, # resnet的stage数量
out_indices=(0, 1, 2, 3), # 输出的stage的序号
frozen_stages=1, # 冻结的stage数量，即该stage不更新参数，-1表示所有的stage都更新参数
style='pytorch'), # 网络风格：如果设置pytorch，则stride为2的层是conv3x3的卷积层；如果设置caffe，则stride为2的层是第一个conv1x1的卷积层
neck=dict(
type='FPN', # neck类型
in_channels=[256, 512, 1024, 2048], # 输入的各个stage的通道数
out_channels=256, # 输出的特征层的通道数
num_outs=5), # 输出的特征层的数量
rpn_head=dict(
type='RPNHead', # RPN网络类型
in_channels=256, # RPN网络的输入通道数
feat_channels=256, # 特征层的通道数
anchor_scales=[8], # 生成的anchor的baselen，baselen = sqrt(w*h)，w和h为anchor的宽和高
anchor_ratios=[0.5, 1.0, 2.0], # anchor的宽高比
anchor_strides=[4, 8, 16, 32, 64], # 在每个特征层上的anchor的步长（对应于原图）
target_means=[.0, .0, .0, .0], # 均值
target_stds=[1.0, 1.0, 1.0, 1.0], # 方差
use_sigmoid_cls=True), # 是否使用sigmoid来进行分类，如果False则使用softmax来分类
bbox_roi_extractor=dict(
type='SingleRoIExtractor', # RoIExtractor类型
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2), # ROI具体参数：ROI类型为ROIalign，输出尺寸为7，sample数为2
out_channels=256, # 输出通道数
featmap_strides=[4, 8, 16, 32]), # 特征图的步长
bbox_head=dict(
type='SharedFCBBoxHead', # 全连接层类型
num_fcs=2, # 全连接层数量
in_channels=256, # 输入通道数
fc_out_channels=1024, # 输出通道数
roi_feat_size=7, # ROI特征层尺寸
num_classes=81, # 分类器的类别数量+1，+1是因为多了一个背景的类别
target_means=[0., 0., 0., 0.], # 均值
target_stds=[0.1, 0.1, 0.2, 0.2], # 方差
reg_class_agnostic=False)) # 是否采用class_agnostic的方式来预测，class_agnostic表示输出bbox时只考虑其是否为前景，后续分类的时候再根据该bbox在网络中的类别得分来分类，也就是说一个框可以对应多个类别
# model training and testing settings
train_cfg = dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner', # RPN网络的正负样本划分
pos_iou_thr=0.7, # 正样本的iou阈值
neg_iou_thr=0.3, # 负样本的iou阈值
min_pos_iou=0.3, # 正样本的iou最小值。如果assign给ground truth的anchors中最大的IOU低于0.3，则忽略所有的anchors，否则保留最大IOU的anchor
ignore_iof_thr=-1), # 忽略bbox的阈值，当ground truth中包含需要忽略的bbox时使用，-1表示不忽略
sampler=dict(
type='RandomSampler', # 正负样本提取器类型
num=256, # 需提取的正负样本数量
pos_fraction=0.5, # 正样本比例
neg_pos_ub=-1, # 最大负样本比例，大于该比例的负样本忽略，-1表示不忽略
add_gt_as_proposals=False), # 把ground truth加入proposal作为正样本
allowed_border=0, # 允许在bbox周围外扩一定的像素
pos_weight=-1, # 正样本权重，-1表示不改变原始的权重
smoothl1_beta=1 / 9.0, # 平滑L1系数
debug=False), # debug模式
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner', # RCNN网络正负样本划分
pos_iou_thr=0.5, # 正样本的iou阈值
neg_iou_thr=0.5, # 负样本的iou阈值
min_pos_iou=0.5, # 正样本的iou最小值。如果assign给ground truth的anchors中最大的IOU低于0.3，则忽略所有的anchors，否则保留最大IOU的anchor
ignore_iof_thr=-1), # 忽略bbox的阈值，当ground truth中包含需要忽略的bbox时使用，-1表示不忽略
sampler=dict(
type='RandomSampler', # 正负样本提取器类型
num=512, # 需提取的正负样本数量
pos_fraction=0.25, # 正样本比例
neg_pos_ub=-1, # 最大负样本比例，大于该比例的负样本忽略，-1表示不忽略
add_gt_as_proposals=True), # 把ground truth加入proposal作为正样本
pos_weight=-1, # 正样本权重，-1表示不改变原始的权重
debug=False)) # debug模式
test_cfg = dict(
rpn=dict( # 推断时的RPN参数
nms_across_levels=False, # 在所有的fpn层内做nms
nms_pre=2000, # 在nms之前保留的的得分最高的proposal数量
nms_post=2000, # 在nms之后保留的的得分最高的proposal数量
max_num=2000, # 在后处理完成之后保留的proposal数量
nms_thr=0.7, # nms阈值
min_bbox_size=0), # 最小bbox尺寸
rcnn=dict(
score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100) # max_per_img表示最终输出的det bbox数量
# soft-nms is also supported for rcnn testing
# e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05) # soft_nms参数
)
# dataset settings
dataset_type = 'CocoDataset' # 数据集类型
data_root = 'data/coco/' # 数据集根目录
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) # 输入图像初始化，减去均值mean并处以方差std，to_rgb表示将bgr转为rgb
data = dict(
imgs_per_gpu=2, # 每个gpu计算的图像数量
workers_per_gpu=2, # 每个gpu分配的线程数
train=dict(
type=dataset_type, # 数据集类型
ann_file=data_root + 'annotations/instances_train2017.json', # 数据集annotation路径
img_prefix=data_root + 'train2017/', # 数据集的图片路径
img_scale=(1333, 800), # 输入图像尺寸，最大边1333，最小边800
img_norm_cfg=img_norm_cfg, # 图像初始化参数
size_divisor=32, # 对图像进行resize时的最小单位，32表示所有的图像都会被resize成32的倍数
flip_ratio=0.5, # 图像的随机左右翻转的概率
with_mask=False, # 训练时附带mask
with_crowd=True, # 训练时附带difficult的样本
with_label=True), # 训练时附带label
val=dict(
type=dataset_type, # 同上
ann_file=data_root + 'annotations/instances_val2017.json', # 同上
img_prefix=data_root + 'val2017/', # 同上
img_scale=(1333, 800), # 同上
img_norm_cfg=img_norm_cfg, # 同上
size_divisor=32, # 同上
flip_ratio=0, # 同上
with_mask=False, # 同上
with_crowd=True, # 同上
with_label=True), # 同上
test=dict(
type=dataset_type, # 同上
ann_file=data_root + 'annotations/instances_val2017.json', # 同上
img_prefix=data_root + 'val2017/', # 同上
img_scale=(1333, 800), # 同上
img_norm_cfg=img_norm_cfg, # 同上
size_divisor=32, # 同上
flip_ratio=0, # 同上
with_mask=False, # 同上
with_label=False, # 同上
test_mode=True)) # 同上
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) # 优化参数，lr为学习率，momentum为动量因子，weight_decay为权重衰减因子
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) # 梯度均衡参数
# learning policy
lr_config = dict(
policy='step', # 优化策略
warmup='linear', # 初始的学习率增加的策略，linear为线性增加
warmup_iters=500, # 在初始的500次迭代中学习率逐渐增加
warmup_ratio=1.0 / 3, # 起始的学习率
step=[8, 11]) # 在第8和11个epoch时降低学习率
checkpoint_config = dict(interval=1) # 每1个epoch存储一次模型
# yapf:disable
log_config = dict(
interval=50, # 每50个batch输出一次信息
hooks=[
dict(type='TextLoggerHook'), # 控制台输出信息的风格
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 12 # 最大epoch数
dist_params = dict(backend='nccl') # 分布式参数
log_level = 'INFO' # 输出信息的完整度级别
work_dir = './work_dirs/faster_rcnn_r50_fpn_1x' # log文件和模型文件存储路径
load_from = None # 加载模型的路径，None表示从预训练模型加载
resume_from = None # 恢复训练模型的路径
workflow = [('train', 1)] # 当前工作区名称
cascade-RCNN是cvpr2018的文章，相比于faster-RCNN的改进主要在于其RCNN有三个stage，这三个stage逐级refine检测的结果，使得结果达到更高的精度。下面逐条解释其config的含义，与faster-RCNN相同的部分就不再赘述。
# model settings
model = dict(
type='CascadeRCNN',
num_stages=3, # RCNN网络的stage数量，在faster-RCNN中为1
pretrained='modelzoo://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
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],
use_sigmoid_cls=True),
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),
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),
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)
])
# model training and testing settings
train_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,
smoothl1_beta=1 / 9.0,
debug=False),
rcnn=[ # 注意，这里有3个RCNN的模块，对应开头的那个RCNN的stage数量
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]) # 3个RCNN的stage的loss权重
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
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) # 是否保留所有stage的结果
# dataset settings
dataset_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)
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/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0.5,
with_mask=False,
with_crowd=True,
with_label=True),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=False,
with_crowd=True,
with_label=True),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=False,
with_label=False,
test_mode=True))
# optimizer
optimizer = 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 policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/cascade_rcnn_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
例如，在MMDetection中，用下面的配置来改变掩码R-CNN的主干。
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(
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(...))
当_delete_=True时，将用新键替换骨干字段中的所有旧键。
2、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，我们想使用多尺度策略来训练一个面具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'
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))
我们首先定义新的train_pipeline / test_pipeline，并将它们传递给data。
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_ = './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),
...)
from mmcv import Config
import albumentations as albu
cfg = Config.fromfile('./configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_20e_coco.py')
可以使用以下的命令检查几个重要参数：
cfg.data.train
cfg.total_epochs
cfg.data.samples_per_gpu
cfg.resume_from
cfg.load_from
cfg.data
...
from mmdet.apis import set_random_seed
# Modify dataset type and path
# cfg.dataset_type = 'Xray'
# cfg.data_root = 'Xray'
cfg.data.samples_per_gpu = 4
cfg.data.workers_per_gpu = 4
# cfg.data.test.type = 'Xray'
cfg.data.test.data_root = '../mmdetection_torch_1.5'
# cfg.data.test.img_prefix = '../mmdetection_torch_1.5'
# cfg.data.train.type = 'Xray'
cfg.data.train.data_root = '../mmdetection_torch_1.5'
# cfg.data.train.ann_file = 'instances_train2014.json'
# # cfg.data.train.classes = classes
# cfg.data.train.img_prefix = '../mmdetection_torch_1.5'
# cfg.data.val.type = 'Xray'
cfg.data.val.data_root = '../mmdetection_torch_1.5'
# cfg.data.val.ann_file = 'instances_val2014.json'
# # cfg.data.train.classes = classes
# cfg.data.val.img_prefix = '../mmdetection_torch_1.5'
# modify neck classes number
# cfg.model.neck.num_outs
# modify num classes of the model in box head
# for i in range(len(cfg.model.roi_head.bbox_head)):
# cfg.model.roi_head.bbox_head[i].num_classes = 10
# cfg.data.train.pipeline[2].img_scale = (1333,800)
cfg.load_from = '../mmdetection_torch_1.5/coco_exps/latest.pth'
# cfg.resume_from = './coco_exps_v3/latest.pth'
# Set up working dir to save files and logs.
cfg.work_dir = './coco_exps_v4'
# The original learning rate (LR) is set for 8-GPU training.
# We divide it by 8 since we only use one GPU.
cfg.optimizer.lr = 0.02 / 8
# cfg.lr_config.warmup = None
# cfg.lr_config = dict(
# policy='step',
# warmup='linear',
# warmup_iters=500,
# warmup_ratio=0.001,
# # [7] yields higher performance than [6]
# step=[7])
# cfg.lr_config = dict(
# policy='step',
# warmup='linear',
# warmup_iters=500,
# warmup_ratio=0.001,
# step=[36,39])
cfg.log_config.interval = 10
# # Change the evaluation metric since we use customized dataset.
# cfg.evaluation.metric = 'mAP'
# # We can set the evaluation interval to reduce the evaluation times
# cfg.evaluation.interval = 12
# # We can set the checkpoint saving interval to reduce the storage cost
# cfg.checkpoint_config.interval = 12
# # Set seed thus the results are more reproducible
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)
# cfg.total_epochs = 40
# # We can initialize the logger for training and have a look
# # at the final config used for training
print(f'Config:\n{cfg.pretty_text}')
欢迎分享，转载请注明来源：内存溢出
微信扫一扫
支付宝扫一扫
评论列表（0条）