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Update customize_datasets.md (#2249)

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Jinhee Lee 2022-11-01 20:14:36 +09:00 committed by GitHub
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docs/en/tutorials/customize_datasets.md
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@ -34,7 +34,7 @@ data = dict(
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 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_dataloaser` and
If you would like to define `batch_size` for testing and validation, please use `test_dataloader` 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.
@ -43,7 +43,7 @@ data = dict(
**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.
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: