mirror of https://github.com/open-mmlab/mmocr.git
146 lines
12 KiB
Markdown
146 lines
12 KiB
Markdown
# Testing
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We introduce the way to test pretrained models on datasets here.
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## Testing on a Single GPU
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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)):
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```shell
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./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
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```
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And here is the full usage of the script:
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```shell
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python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [ARGS]
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```
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````{note}
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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**.
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```bash
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CUDA_VISIBLE_DEVICES= python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [ARGS]
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```
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````
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| ARGS | Type | Description |
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| ------------------ | -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `--out` | str | Output result file in pickle format. |
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| `--fuse-conv-bn` | bool | Path to the custom config of the selected det model. |
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| `--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. |
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| `--gpu-id` | int | GPU id to use. Only applicable to non-distributed training. |
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| `--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. |
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| `--show` | bool | Whether to show results. |
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| `--show-dir` | str | Directory where the output images will be saved. |
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| `--show-score-thr` | float | Score threshold (default: 0.3). |
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| `--gpu-collect` | bool | Whether to use gpu to collect results. |
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| `--tmpdir` | str | The tmp directory used for collecting results from multiple workers, available when gpu-collect is not specified. |
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| `--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. |
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| `--eval-options` | str | Custom options for evaluation, the key-value pair in xxx=yyy format will be kwargs for dataset.evaluate() function. |
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| `--launcher` | 'none', 'pytorch', 'slurm', 'mpi' | Options for job launcher. |
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## Testing on Multiple GPUs
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MMOCR implements **distributed** testing with `MMDistributedDataParallel`.
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You can use the following command to test a dataset with multiple GPUs.
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```shell
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[PORT={PORT}] ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [PY_ARGS]
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```
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| Arguments | Type | Description |
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| ----------------- | ---- | -------------------------------------------------------------------------------- |
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| `PORT` | int | The master port that will be used by the machine with rank 0. Defaults to 29500. |
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| `CONFIG_FILE` | str | The path to config. |
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| `CHECKPOINT_FILE` | str | The path to the checkpoint. |
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| `GPU_NUM` | int | The number of GPUs to be used per node. Defaults to 8. |
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| `PY_ARGS` | str | Arguments to be parsed by `tools/test.py`. |
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For example,
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```shell
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./tools/dist_test.sh configs/example_config.py work_dirs/example_exp/example_model_20200202.pth 1 --eval hmean-iou
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```
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## Testing on Multiple Machines
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You can launch a task on multiple machines connected to the same network.
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```shell
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NNODES=${NNODES} NODE_RANK=${NODE_RANK} PORT=${MASTER_PORT} MASTER_ADDR=${MASTER_ADDR} ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [PY_ARGS]
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```
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| Arguments | Type | Description |
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| ----------------- | ---- | -------------------------------------------------------------------- |
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| `NNODES` | int | The number of nodes. |
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| `NODE_RANK` | int | The rank of current node. |
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| `PORT` | int | The master port that will be used by rank 0 node. Defaults to 29500. |
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| `MASTER_ADDR` | str | The address of rank 0 node. Defaults to "127.0.0.1". |
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| `CONFIG_FILE` | str | The path to config. |
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| `CHECKPOINT_FILE` | str | The path to the checkpoint. |
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| `GPU_NUM` | int | The number of GPUs to be used per node. Defaults to 8. |
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| `PY_ARGS` | str | Arguments to be parsed by `tools/test.py`. |
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```{note}
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MMOCR relies on torch.distributed package for distributed testing. Find more information at PyTorch’s [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility).
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```
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Say that you want to launch a job on two machines. On the first machine:
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```shell
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NNODES=2 NODE_RANK=0 PORT=${MASTER_PORT} MASTER_ADDR=${MASTER_ADDR} ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [PY_ARGS]
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```
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On the second machine:
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```shell
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NNODES=2 NODE_RANK=1 PORT=${MASTER_PORT} MASTER_ADDR=${MASTER_ADDR} ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [PY_ARGS]
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```
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```{note}
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The speed of the network could be the bottleneck of testing.
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```
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## Testing with Slurm
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If you run MMOCR on a cluster managed with [Slurm](https://slurm.schedmd.com/), you can use the script `tools/slurm_test.sh`.
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```shell
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[GPUS=${GPUS}] [GPUS_PER_NODE=${GPUS_PER_NODE}] [SRUN_ARGS=${SRUN_ARGS}] ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${CHECKPOINT_FILE} [PY_ARGS]
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```
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| Arguments | Type | Description |
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| --------------- | ---- | ----------------------------------------------------------------------------------------------------------- |
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| `GPUS` | int | The number of GPUs to be used by this task. Defaults to 8. |
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| `GPUS_PER_NODE` | int | The number of GPUs to be allocated per node. Defaults to 8. |
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| `SRUN_ARGS` | str | Arguments to be parsed by srun. Available options can be found [here](https://slurm.schedmd.com/srun.html). |
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| `PY_ARGS` | str | Arguments to be parsed by `tools/test.py`. |
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Here is an example of using 8 GPUs to test an example model on the 'dev' partition with job name 'test_job'.
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```shell
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GPUS=8 ./tools/slurm_test.sh dev test_job configs/example_config.py work_dirs/example_exp/example_model_20200202.pth --eval hmean-iou
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```
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## Batch Testing
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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,
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```python
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data = dict(
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...
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val_dataloader=dict(samples_per_gpu=16),
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test_dataloader=dict(samples_per_gpu=16),
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...
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)
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```
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will test the model with 16 images in a batch.
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```{warning}
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Batch testing may incur performance decrease of the model due to the different behavior of the data preprocessing pipeline.
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```
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