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# Testing
We introduce the way to test pretrained models on datasets here.
## Testing with Single GPU
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)):
```shell
./tools/dist_test.sh configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py dbnet_r18_fpnc_sbn_1200e_icdar2015_20210329-ba3ab597.pth --eval hmean-iou
```
And here is the full usage of the script:
```shell
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [ARGS]
```
:::{note}
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**.
```bash
CUDA_VISIBLE_DEVICES= python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [ARGS]
```
:::
| ARGS | Type | Description |
| ------------------ | --------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `--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' | The evaluation metrics, which depends on the task. For text detection, the metric should be either 'hmean-ic13' or 'hmean-iou'. For text recognition, the metric should be 'acc'. |
| `--show` | bool | Whether to show results. |
| `--show-dir` | str | Directory where the output images will be saved. |
| `--show-score-thr` | float | Score threshold (default: 0.3). |
| `--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. |
| `--launcher` | 'none', 'pytorch', 'slurm', 'mpi' | Options for job launcher. |
## Testing with Multiple GPUs
MMOCR implements **distributed** testing with `MMDistributedDataParallel`.
You can use the following command to test a dataset with multiple GPUs.
```shell
[PORT={PORT}] ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [PY_ARGS]
```
| Arguments | Type | Description |
| --------- | ---- | -------------------------------------------------------------------------------- |
| `PORT` | int | The master port that will be used by the machine with rank 0. Defaults to 29500. |
| `PY_ARGS` | str | Arguments to be parsed by `tools/test.py`. |
For example,
```shell
./tools/dist_test.sh configs/example_config.py work_dirs/example_exp/example_model_20200202.pth 1 --eval hmean-iou
```
## Testing with Slurm
If you run MMOCR on a cluster managed with [Slurm](https://slurm.schedmd.com/), you can use the script `tools/slurm_test.sh`.
```shell
[GPUS=${GPUS}] [GPUS_PER_NODE=${GPUS_PER_NODE}] [SRUN_ARGS=${SRUN_ARGS}] ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${CHECKPOINT_FILE} [PY_ARGS]
```
| Arguments | Type | Description |
| --------------- | ---- | ----------------------------------------------------------------------------------------------------------- |
| `GPUS` | int | The number of GPUs to be used by this task. Defaults to 8. |
| `GPUS_PER_NODE` | int | The number of GPUs to be allocated per node. Defaults to 8. |
| `SRUN_ARGS` | str | Arguments to be parsed by srun. Available options can be found [here](https://slurm.schedmd.com/srun.html). |
| `PY_ARGS` | str | Arguments to be parsed by `tools/test.py`. |
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,
```
data = dict(
...
val_dataloader=dict(samples_per_gpu=16),
test_dataloader=dict(samples_per_gpu=16),
...
)
```
will test the model with 16 images in a batch.
:::{warning}
Batch testing may incur performance decrease of the model due to the different behavior of the data preprocessing pipeline.
:::
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