mmdetection2.6的config文件
mmdetection2.6的config文件
mmdetection合并了模块化与继承设计的思想来构成config系统，利用这种系统可以方便的执行多样化的实验。如果
想要检查配置文件，可以运行如下的代码来查看完整的配置文件：
python tools/print_config.py /PATH/TO/CONFIG
1. 配置文件的结构
在 config/_base_中存在4中基本的组件类型，分别为：dataset(数据集), model(模型）, schedule（学习率调整的粗略），default_runtime（默认运行时设置）。使用Faster R-CNN，Mask R-CNN，Cascade R-CNN，RPN，SSD中的一个可以轻松构造许多方法。来自__base__中的组件称为promitive.
对于同一文件夹下的所有配置，建议仅具有一个 *原始（promitive）配置。所有其他配置应从原始（promitive）*配置继承。这样，继承级别的最大值为3。
便于理解，构建爱你模型推荐从现有的方法中继承。例如，如果有一些修改是基于faster rcnn的， 那么使用者可能通过指定_base_ = ../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py首先继承基本的faster rcnn架构， 然后修改必要的config文件。
如果构建了一种没有基于任何架构的完全新方法，可以在configs文件夹下创建一个新的文件夹。
2. config文件命名风格
利用下面的文件命名方法命名：
{model}_[model setting]_{backbone}_{neck}_[norm setting]_[misc]_[gpu x batch_per_gpu]_{schedule}_{dataset}
{xxx}是必填字段，[yyy]是可选字段。
{model}：模型的类型， 例如faster_rcnn，mask_rcnn等。[model setting]：某些模型的特定设置，例如without_semanticforhtc，momentforreppoints等。{backbone}：骨干类型，例如r50（ResNet-50），x101（ResNeXt-101）。{neck}：neck的类型， 例如fpn，pafpn，nasfpn，c4。[norm_setting]：如果不特别指定，默认使用bn，否则使用其他标准图层类型可以是gn（Group Normalization），syncbn（Synchronized Batch Normalization）。gn-head/gn-neck表示GN仅应用于头部/颈部，而gn-all表示GN应用于整个模型，例如，骨架，颈部，头部。[misc]：其他的设置/模型的插件，例如dconv，gcb，attention，albu，mstrain。[gpu x batch_per_gpu]：GPU数目和每个GPU的样本数目， 默认使用8x2。{schedule}：训练进度表，可选的有1x，2x，20e，1x和2x默认12与24 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和11/22个epoch衰减10倍。对于20e，初始学习率在第16和19个epoch衰减10倍。{dataset}：数据集， 例如coco，cityscapes，voc_0712，wider_face。
3. mask rcnn的config文件
下边简单介绍maskrcnn + resnet50 + fpn的config文件介绍，文件存在于configs/__base__/models/mask_rcnn_r50_fpn.py。
model = dict(
type='MaskRCNN', # 检测器的名名称
pretrained=
'torchvision://resnet50', # 加载的ImageNet预训练权重。
backbone=dict( # backbone的cofig文件
type='ResNet', # backbone的类型, 更多的细节参考 //github.com/open-mmlab/mmdetection/blob/master/mmdet/models/backbones/resnet.py#L288。
depth=50, # backbone的深度, 通常情况下resnet采用50， resnext采用101
num_stages=4, # backbone的段的数目.
out_indices=(0, 1, 2, 3), # 每个stage所产生的feature map的索引
frozen_stages=1, # 冻结第一个stage不训练
norm_cfg=dict( # 正则化层的config参数
type='BN', # 正则化层
requires_grad=True), # 是否训练BN中的gamma与beta
norm_eval=True, # 是否冻结BN中的统计量
style='pytorch'), # backbone的风格，可选的有pytorch与caffe, 'pytorch' 参数表示stride为2的过程在3x3的卷积层中，而 'caffe' 表示stride 2的层在1x1的卷积层中）。
neck=dict(
type='FPN', # 检测器的neck是FPN. 支持 'NASFPN', 'PAFPN'等. 更多细节可参考 //github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/fpn.py#L10.
in_channels=[256, 512, 1024, 2048], # FPN的输入channels, 这与neck的输出通道一致
out_channels=256, # 金字塔特征映射的每个级别的输出通道
num_outs=5), # 输出尺度的数目
rpn_head=dict(
type='RPNHead', # RPN head is 'RPNHead', 框架同时支持 'GARPNHead'等. 更多细节参考//github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/rpn_head.py#L12
in_channels=256, # 每个输入特征图的通道数， 这与neck的输出通道数一致。
feat_channels=256, # 卷积层的特征通道
anchor_generator=dict( # 生成anchor的配置
type='AnchorGenerator', # 大多数方法使用AnchorGenerator, 而ssd检测器采用`SSDAnchorGenerator`. 更多细节参考//github.com/open-mmlab/mmdetection/blob/master/mmdet/core/anchor/anchor_generator.py#L10
scales=[8], # anchor的基本尺度，一个featuremap的anchor的面积通过公式计算： scale * base_sizes
ratios=[0.5, 1.0, 2.0], # anchor的宽高比
strides=[4, 8, 16, 32, 64]), # anchor生成器的步长，这与FPN特征的步长一致。 如果base_size没有设置，这个步长将会作为base_size.
bbox_coder=dict( # 边界框编码器的配置，在训练与测试时编码与解码边界框
type='DeltaXYWHBBoxCoder', # 边界框编码器的配置. 大多数的方法采用'DeltaXYWHBBoxCoder'. 更多的细节参考//github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py#L9
target_means=[0.0, 0.0, 0.0, 0.0], # 目标的均值，这个值用来编码与解码边界框
target_stds=[1.0, 1.0, 1.0, 1.0]), # 目标的方差，这个值用来编码与解码边界框
loss_cls=dict( # 分类分支的loss的配置
type='CrossEntropyLoss', # 分类分支loss的类型，除了交叉商损失外，也支持focal loss.
use_sigmoid=True, # RPN曾通常为二分类任务，用来区分前景与背景，采用sigmoid激活函数
loss_weight=1.0), # 分类分支loss的权重
loss_bbox=dict( # 回归分支lossfunction的config配置
type='L1Loss', # 回归损失的类型，除了l1 loss之外，也支持smooth l1 loss与各种IOU loss. 更多的细节参考//github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/smooth_l1_loss.py#L56
loss_weight=1.0)), # 回归分支的损失权重
roi_head=dict( # ROIHead封装了级联检测器或者两部法的第二个stage
type='StandardRoIHead', # ROIHead的类型.更多的细节参考//github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/standard_roi_head.py#L10.
bbox_roi_extractor=dict( # 用于bbox回归的ROI特征提取器config.
type='SingleRoIExtractor', # ROI特征提取其的类型,大多数方法采用SingleRoIExtractor. 更多的细节参考//github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/roi_extractors/single_level.py#L10
roi_layer=dict( # ROI层的config
type='RoIAlign', # ROIAlign的类型, 同时也支持DeformRoIPoolingPack and ModulatedDeformRoIPoolingPack. Refer to //github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/roi_align/roi_align.py#L79 for details.
output_size=7, # featuremap的输出尺寸.
sampling_ratio=0), # 当提取ROI特征时的采样率， 如果采样率为0， 表示自适应率
out_channels=256, # 提取得到的特征的输出通道.
featmap_strides=[4, 8, 16, 32]), # 多尺度特征图的步长， 应该与backbone的输出一致
bbox_head=dict( # 在ROI head中 的box headconfig
type='Shared2FCBBoxHead', # box head的步长, Refer to //github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py#L177 for implementation details.
in_channels=256, #bbox的输入通道，应该与roi特征提取器的输出一致.
fc_out_channels=1024, # 全链接层的输出通道.
roi_feat_size=7, # roi特征的尺寸
num_classes=80, # 分类的类别数目
bbox_coder=dict( # 在第二个stage中的bbox编码器的config
type='DeltaXYWHBBoxCoder', # bbox编码器的类型. 大多数方法采用'DeltaXYWHBBoxCoder' .
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]), # 编解码的方差，这个方差更小，因为此时的bbox已经西那个对比较准确. [0.1, 0.1, 0.2, 0.2] is a conventional setting.
reg_class_agnostic=False, # 是否回归分支是不知道类别的.
loss_cls=dict( # 分类分支的lossfunction
type='CrossEntropyLoss',
use_sigmoid=False, # 是否采用sigmoid
loss_weight=1.0), # 分类loss的权重
loss_bbox=dict(
type='L1Loss',
loss_weight=1.0)),
mask_roi_extractor=dict( # bbox回归的特征提取器.
type='SingleRoIExtractor', # ROI特征提取器的类型，大多数方法采用SingleRoIExtractor.
roi_layer=dict( # 用于实例分割的特征提取ROI层
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预测head
type='FCNMaskHead', # Type of mask head, refer to //github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py#L21 for implementation details.
num_convs=4, # mask head中卷积层的数目.
in_channels=256, # 输入通道数目， 应该与mask特征提取层的输出通道数目一致.
conv_out_channels=256, # 卷积层的输出通道数目.
num_classes=80, # 分割的类别数目.
loss_mask=dict( # Config of loss function for the mask branch.
type='CrossEntropyLoss', # Type of loss used for segmentation
use_mask=True, # 是否仅仅训练mask正确的位置
loss_weight=1.0)))) # Loss weight of mask branch.
train_cfg = dict( #rpn与rcnn的训练超参数设置。
rpn=dict( # rpn的训练config配置
assigner=dict( # assigner的config
type='MaxIoUAssigner', # assigner的类型, 大多数检测器采用MaxIoUAssigner. Refer to //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', # Tsampler的类型, 也支持其他的PseudoSampler and other samplers. Refer to //github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
num=256, # samples的数目
pos_fraction=0.5, # 所有的样本中正样本的比例
neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples.
add_gt_as_proposals=False), # 是否在采样后将GT作为正阳本加入samples
allowed_border=-1, # The border allowed after padding for valid anchors.
pos_weight=-1, # 训练过程中正样本的去
debug=False), # 是否设置debug模式
rpn_proposal=dict( # 训练过程中生成proposal的config。
nms_across_levels=False, # 是否跨层次执行nms
nms_pre=2000, # nms之前的proposal的数目
nms_post=1000, # nms保留的样本的数目
max_num=1000, # nms之后使用的proposal数目
nms_thr=0.7, # nms中使用的阈值
min_bbox_size=0), # 最小的box的尺寸
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, 现在所有的roi_head均使用MaxIoUAssigner. Refer to //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 //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
nms_pre=1000, # The number of boxes before NMS
nms_post=1000, # The number of boxes to be kept by NMS
max_num=1000, # The number of boxes to be used after NMS
nms_thr=0.7, # The threshold to be used during NMS
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' # 数据集的类型
data_root = 'data/coco/' # 数据的根目录
img_norm_cfg = dict( # 图像正则化config
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 supressed 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, # 每个gpu上的images， 这里乘以gpu的数目即为batch size
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 //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 //github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7 for more details.
interval=1, #每1个epoch评估一次，这里也可以修改
metric=['bbox', 'segm']) # 评估标准
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 //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 //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 //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
total_epochs = 12 # Total epochs to train the model
checkpoint_config = dict( # Config to set the checkpoint hook, Refer to //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 # 加载预训练模型权重，这里不是resume训练采用
resume_from = None # 这里resume训练采用
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' # 这个路径用来存储模型与日志文件
4. 一些问题：
在继承关系中有时可以设置 _delete_=True来忽视在base config中的一些键。在mmcv中可以看到具体的一些指导细节。
在mmdetection中，在mask rcnn的config文件中来修改backbone。
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 使用不同的键来构建
_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(...))
_delete_=True在backbone中利用新键来取代旧键。
4.1 在配置中使用中间变量
一些中间变量在配置文件中是非常重要的，例如，datasets中的train_pipeline与test_pipeline。值得注意的是，在子配置文件中修改中间变量时，用户需要再次将中间变量传递到相应的字段中。例如，我们想使用多尺度策略来训练Mask R-CNN。train_pipeline/test_pipeline是我们要修改的中间变量。
_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))
这里我们首先定义了新的train_pipeline与test_pipeline并且将他们传给data。
