`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:
```python
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 [`config`](https://github.com/open-mmlab/mmcv/blob/master/docs/en/understand_mmcv/config.md)s to build dataset instances for model training, validation and testing by
using [`build and registry`](https://github.com/open-mmlab/mmcv/blob/master/docs/en/understand_mmcv/registry.md) 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 trainig and `samples_per_gpu=4`, the `batch_size` is `8*4=16`.
If you would like to define `batch_size` for testing and validation, please use `test_dataloaser` and
`val_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 the
input 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_dataloaser` 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:
```python
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.
In this case, `ann_dir_1` and `ann_dir_2` are corresponding to `split_1.txt` and `split_2.txt`.
2. In case the dataset you want to concatenate is different, you can concatenate the dataset configs like the following.
```python
dataset_A_train = dict()
dataset_B_train = dict()
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
)
```
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.
