157 lines
4.3 KiB
Markdown
157 lines
4.3 KiB
Markdown
# Add New Datasets
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## Customize datasets by reorganizing data
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The simplest way is to convert your dataset to organize your data into folders.
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An example of file structure is as followed.
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```none
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├── data
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│ ├── my_dataset
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│ │ ├── img_dir
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│ │ │ ├── train
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│ │ │ │ ├── xxx{img_suffix}
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│ │ │ │ ├── yyy{img_suffix}
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│ │ │ │ ├── zzz{img_suffix}
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│ │ │ ├── val
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│ │ ├── ann_dir
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│ │ │ ├── train
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│ │ │ │ ├── xxx{seg_map_suffix}
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│ │ │ │ ├── yyy{seg_map_suffix}
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│ │ │ │ ├── zzz{seg_map_suffix}
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│ │ │ ├── val
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```
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A training pair will consist of the files with same suffix in img_dir/ann_dir.
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If `split` argument is given, only part of the files in img_dir/ann_dir will be loaded.
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We may specify the prefix of files we would like to be included in the split txt.
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More specifically, for a split txt like following,
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```none
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xxx
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zzz
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```
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Only
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`data/my_dataset/img_dir/train/xxx{img_suffix}`,
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`data/my_dataset/img_dir/train/zzz{img_suffix}`,
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`data/my_dataset/ann_dir/train/xxx{seg_map_suffix}`,
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`data/my_dataset/ann_dir/train/zzz{seg_map_suffix}` will be loaded.
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:::{note}
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The annotations are images of shape (H, W), the value pixel should fall in range `[0, num_classes - 1]`.
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You may use `'P'` mode of [pillow](https://pillow.readthedocs.io/en/stable/handbook/concepts.html#palette) to create your annotation image with color.
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:::
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## Customize datasets by mixing dataset
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MMSegmentation also supports to mix dataset for training.
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Currently it supports to concat, repeat and multi-image mix datasets.
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### Repeat dataset
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We use `RepeatDataset` as wrapper to repeat the dataset.
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For example, suppose the original dataset is `Dataset_A`, to repeat it, the config looks like the following
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```python
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dataset_A_train = dict(
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type='RepeatDataset',
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times=N,
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dataset=dict( # This is the original config of Dataset_A
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type='Dataset_A',
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...
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pipeline=train_pipeline
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)
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)
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```
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### Concatenate dataset
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In case the dataset you want to concatenate is different, you can concatenate the dataset configs like the following.
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```python
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dataset_A_train = dict()
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dataset_B_train = dict()
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concatenate_dataset = dict(
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type='ConcatDataset',
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datasets=[dataset_A_train, dataset_B_train])
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```
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A more complex example that repeats `Dataset_A` and `Dataset_B` by N and M times, respectively, and then concatenates the repeated datasets is as the following.
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```python
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dataset_A_train = dict(
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type='RepeatDataset',
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times=N,
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dataset=dict(
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type='Dataset_A',
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...
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pipeline=train_pipeline
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)
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)
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dataset_A_val = dict(
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...
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pipeline=test_pipeline
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)
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dataset_A_test = dict(
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...
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pipeline=test_pipeline
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)
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dataset_B_train = dict(
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type='RepeatDataset',
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times=M,
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dataset=dict(
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type='Dataset_B',
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...
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pipeline=train_pipeline
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)
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)
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train_dataloader = dict(
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dataset=dict('ConcatDataset', datasets=[dataset_A_train, dataset_B_train]))
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val_dataloader = dict(dataset=dataset_A_val)
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test_dataloader = dict(dataset=dataset_A_test)
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```
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You can refer base dataset [tutorial](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/basedataset.html) from mmengine for more details
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### Multi-image Mix Dataset
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We use `MultiImageMixDataset` as a wrapper to mix images from multiple datasets.
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`MultiImageMixDataset` can be used by multiple images mixed data augmentation
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like mosaic and mixup.
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An example of using `MultiImageMixDataset` with `Mosaic` data augmentation:
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```python
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train_pipeline = [
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dict(type='RandomMosaic', prob=1),
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dict(type='Resize', img_scale=(1024, 512), keep_ratio=True),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PackSegInputs')
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]
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train_dataset = dict(
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type='MultiImageMixDataset',
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dataset=dict(
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classes=classes,
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palette=palette,
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type=dataset_type,
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reduce_zero_label=False,
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img_dir=data_root + "images/train",
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ann_dir=data_root + "annotations/train",
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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]
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),
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pipeline=train_pipeline
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)
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```
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