You can use `tools/test.py` to perform single CPU/GPU inference. For example, to evaluate DBNet on IC15: (You can download pretrained models from [Model Zoo](modelzoo.md)):
By default, MMOCR prefers GPU(s) to CPU. If you want to test a model on CPU, please empty `CUDA_VISIBLE_DEVICES` or set it to -1 to make GPU(s) invisible to the program. Note that running CPU tests requires **MMCV >= 1.4.4**.
| `--out` | str | Output result file in pickle format. |
| `--fuse-conv-bn` | bool | Path to the custom config of the selected det model. |
| `--format-only` | bool | Format the output results without performing evaluation. It is useful when you want to format the results to a specific format and submit them to the test server. |
| `--gpu-id` | int | GPU id to use. Only applicable to non-distributed training. |
| `--eval` | 'hmean-ic13', 'hmean-iou', 'acc', 'macro-f1' | The evaluation metrics. Options: 'hmean-ic13', 'hmean-iou' for text detection tasks, 'acc' for text recognition tasks, and 'macro-f1' for key information extraction tasks. |
| `--show` | bool | Whether to show results. |
| `--show-dir` | str | Directory where the output images will be saved. |
| `--gpu-collect` | bool | Whether to use gpu to collect results. |
| `--tmpdir` | str | The tmp directory used for collecting results from multiple workers, available when gpu-collect is not specified. |
| `--cfg-options` | str | Override some settings in the used config, the key-value pair in xxx=yyy format will be merged into the config file. If the value to be overwritten is a list, it should be of the form of either key="[a,b]" or key=a,b. The argument also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]". Note that the quotation marks are necessary and that no white space is allowed. |
| `--eval-options` | str | Custom options for evaluation, the key-value pair in xxx=yyy format will be kwargs for dataset.evaluate() function. |
MMOCR relies on torch.distributed package for distributed testing. Find more information at PyTorch’s [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility).
:::
Say that you want to launch a job on two machines. On the first machine:
Here is an example of using 8 GPUs to test an example model on the 'dev' partition with job name 'test_job'.
```shell
GPUS=8 ./tools/slurm_test.sh dev test_job configs/example_config.py work_dirs/example_exp/example_model_20200202.pth --eval hmean-iou
```
## Batch Testing
By default, MMOCR tests the model image by image. For faster inference, you may change `data.val_dataloader.samples_per_gpu` and `data.test_dataloader.samples_per_gpu` in the config. For example,
