Customize Datasets — MMSegmentation 0.30.0 documentation (2024)

Data configuration¶

data in config file is the variable for data configuration, to define the arguments that are used in datasets and dataloaders.

Here is an example of data configuration:

data = dict( samples_per_gpu=4, workers_per_gpu=4, train=dict( type='ADE20KDataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/training', ann_dir='annotations/training', pipeline=train_pipeline), val=dict( type='ADE20KDataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/validation', ann_dir='annotations/validation', pipeline=test_pipeline), test=dict( type='ADE20KDataset', data_root='data/ade/ADEChallengeData2016', img_dir='images/validation', ann_dir='annotations/validation', pipeline=test_pipeline))

• train, val and test: The configs to build dataset instances for model training, validation and testing byusing build and registry mechanism.

• samples_per_gpu: How many samples per batch and per gpu to load during model training, and the batch_size of training is equal to samples_per_gpu times gpu number, e.g. when using 8 gpus for distributed data parallel training and samples_per_gpu=4, the batch_size is 8*4=32.If you would like to define batch_size for testing and validation, please use test_dataloader andval_dataloader with mmseg >=0.24.1.

• workers_per_gpu: How many subprocesses per gpu to use for data loading. 0 means that the data will be loaded in the main process.

Note: samples_per_gpu only works for model training, and the default setting of samples_per_gpu is 1 in mmseg when model testing and validation (DO NOT support batch inference yet).

Note: before v0.24.1, except train, val test, samples_per_gpu and workers_per_gpu, the other keys in data must be theinput keyword arguments for dataloader in pytorch, and the dataloaders used for model training, validation and testing have the same input arguments.In v0.24.1, mmseg supports to use train_dataloader, test_dataloader and val_dataloader to specify different keyword arguments, and still supports the overall arguments definition but the specific dataloader setting has a higher priority.

Here is an example for specific dataloader:

data = dict( samples_per_gpu=4, workers_per_gpu=4, shuffle=True, train=dict(type='xxx', ...), val=dict(type='xxx', ...), test=dict(type='xxx', ...), # Use different batch size during validation and testing. val_dataloader=dict(samples_per_gpu=1, workers_per_gpu=4, shuffle=False), test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=4, shuffle=False))

Assume only one gpu used for model training and testing, as the priority of the overall arguments definition is low, the batch_sizefor training is 4 and dataset will be shuffled, and batch_size for testing and validation is 1, and dataset will not be shuffled.

To make data configuration much clearer, we recommend use specific dataloader setting instead of overall dataloader setting after v0.24.1, just like:

data = dict( train=dict(type='xxx', ...), val=dict(type='xxx', ...), test=dict(type='xxx', ...), # Use specific dataloader setting train_dataloader=dict(samples_per_gpu=4, workers_per_gpu=4, shuffle=True), val_dataloader=dict(samples_per_gpu=1, workers_per_gpu=4, shuffle=False), test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=4, shuffle=False))

Note: in model training, default values in the script of mmseg for dataloader are shuffle=True, and drop_last=True,in model validation and testing, default values are shuffle=False, and drop_last=False

Customize datasets by reorganizing data¶

The simplest way is to convert your dataset to organize your data into folders.

An example of file structure is as followed.

├── data│ ├── my_dataset│ │ ├── img_dir│ │ │ ├── train│ │ │ │ ├── xxx{img_suffix}│ │ │ │ ├── yyy{img_suffix}│ │ │ │ ├── zzz{img_suffix}│ │ │ ├── val│ │ ├── ann_dir│ │ │ ├── train│ │ │ │ ├── xxx{seg_map_suffix}│ │ │ │ ├── yyy{seg_map_suffix}│ │ │ │ ├── zzz{seg_map_suffix}│ │ │ ├── val

A training pair will consist of the files with same suffix in img_dir/ann_dir.

If split argument is given, only part of the files in img_dir/ann_dir will be loaded.We may specify the prefix of files we would like to be included in the split txt.

More specifically, for a split txt like following,

xxxzzz

Onlydata/my_dataset/img_dir/train/xxx{img_suffix},data/my_dataset/img_dir/train/zzz{img_suffix},data/my_dataset/ann_dir/train/xxx{seg_map_suffix},data/my_dataset/ann_dir/train/zzz{seg_map_suffix} will be loaded.

Customize datasets by mixing dataset¶

MMSegmentation also supports to mix dataset for training.Currently it supports to concat, repeat and multi-image mix datasets.

dataset_A_train = dict( type='RepeatDataset', times=N, dataset=dict( # This is the original config of Dataset_A type='Dataset_A', ... pipeline=train_pipeline ) )

dataset_A_train = dict( type='RepeatDataset', times=N, dataset=dict( type='Dataset_A', ... pipeline=train_pipeline ))dataset_A_val = dict( ... pipeline=test_pipeline)dataset_A_test = dict( ... pipeline=test_pipeline)dataset_B_train = dict( type='RepeatDataset', times=M, dataset=dict( type='Dataset_B', ... pipeline=train_pipeline ))data = dict( imgs_per_gpu=2, workers_per_gpu=2, train = [ dataset_A_train, dataset_B_train ], val = dataset_A_val, test = dataset_A_test)

train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict(type='RandomMosaic', prob=1), dict(type='Resize', img_scale=(1024, 512), keep_ratio=True), dict(type='RandomFlip', prob=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_semantic_seg']),]train_dataset = dict( type='MultiImageMixDataset', dataset=dict( classes=classes, palette=palette, type=dataset_type, reduce_zero_label=False, img_dir=data_root + "images/train", ann_dir=data_root + "annotations/train", pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), ] ), pipeline=train_pipeline)