mmocr/docs/en/testing.md

Testing

We introduce the way to test pretrained models on datasets here.

Testing on a 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):

./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:

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [ARGS]

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', '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.
--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 on Multiple GPUs

MMOCR implements distributed testing with MMDistributedDataParallel.

You can use the following command to test a dataset with multiple GPUs.

[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.
CONFIG_FILE	str	The path to config.
CHECKPOINT_FILE	str	The path to the checkpoint.
GPU_NUM	int	The number of GPUs to be used per node. Defaults to 8.
PY_ARGS	str	Arguments to be parsed by tools/test.py.

For example,

./tools/dist_test.sh configs/example_config.py work_dirs/example_exp/example_model_20200202.pth 1 --eval hmean-iou

Testing on Multiple Machines

You can launch a task on multiple machines connected to the same network.

NNODES=${NNODES} NODE_RANK=${NODE_RANK} PORT=${MASTER_PORT} MASTER_ADDR=${MASTER_ADDR} ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [PY_ARGS]

Arguments	Type	Description
NNODES	int	The number of nodes.
NODE_RANK	int	The rank of current node.
PORT	int	The master port that will be used by rank 0 node. Defaults to 29500.
MASTER_ADDR	str	The address of rank 0 node. Defaults to "127.0.0.1".
CONFIG_FILE	str	The path to config.
CHECKPOINT_FILE	str	The path to the checkpoint.
GPU_NUM	int	The number of GPUs to be used per node. Defaults to 8.
PY_ARGS	str	Arguments to be parsed by tools/test.py.

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:

NNODES=2 NODE_RANK=0 PORT=${MASTER_PORT} MASTER_ADDR=${MASTER_ADDR} ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [PY_ARGS]

On the second machine:

NNODES=2 NODE_RANK=1 PORT=${MASTER_PORT} MASTER_ADDR=${MASTER_ADDR} ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [PY_ARGS]

The speed of the network could be the bottleneck of testing.

Testing with Slurm

If you run MMOCR on a cluster managed with Slurm, you can use the script tools/slurm_test.sh.

[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.
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'.

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.

Batch testing may incur performance decrease of the model due to the different behavior of the data preprocessing pipeline.