By default, MMOCR prefers GPU to CPU. If you want to train a model on CPU, please empty `CUDA_VISIBLE_DEVICES` or set it to -1 to make GPU invisible to the program. Note that CPU training requires **MMCV >= 1.4.4**.
| `--deterministic` | bool | Whether to set deterministic options for CUDNN backend. |
| `--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. |
| `PORT` | int | The master port that will be used by the machine with rank 0. Defaults to 29500. **Note:** If you are launching multiple distrbuted training jobs on a single machine, you need to specify different ports for each job to avoid port conflicts. |
| `PY_ARGS` | str | Arguments to be parsed by `tools/train.py`. |
MMOCR relies on torch.distributed package for distributed training. Thus, as a basic usage, one can launch distributed training via PyTorch’s [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility).
If you are launching multiple training jobs on a single machine with Slurm, you may need to modify the port in configs to avoid communication conflicts.
# Note: User can configure general settings of train, val and test dataloader by specifying them here. However, their values can be overridden in dataloader's config.
evaluation = dict(interval=1, by_epoch=True) # Evaluate the model every epoch
# Saving and Logging
checkpoint_config = dict(interval=1) # Save a checkpoint every epoch
log_config = dict(
interval=5, # Print out the model's performance every 5 iterations
hooks=[
dict(type='TextLoggerHook')
])
# Optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) # Supports all optimizers in PyTorch and shares the same parameters
optimizer_config = dict(grad_clip=None) # Parameters for the optimizer hook. See https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py for implementation details
