mmrazor/docs/en/train.md

Train a model with our algorithms

Currently our algorithms support mmclassification, mmdetection and mmsegmentation. Before running our algorithms, you may need to prepare the datasets according to the instructions in the corresponding document.

Note:

• Since our algorithms have the same interface for all three tasks, in the following introduction, we use ${task} to represent one of mmcls、mmdet and mmseg.
• We dynamically pass arguments cfg-options (e.g., mutable_cfg in nas algorithm or channel_cfg in pruning algorithm) to avoid the need for a config for each subnet or checkpoint. If you want to specify different subnets for retraining or testing, you just need to change this arguments.

NAS

There are three steps to start neural network search(NAS), including supernet pre-training, search for subnet on the trained supernet and subnet retraining.

Supernet Pre-training

python tools/${task}/train_${task}.py ${CONFIG_FILE} [optional arguments]

The usage of optional arguments are the same as corresponding tasks like mmclassification, mmdetection and mmsegmentation.

Search for Subnet on The Trained Supernet

python tools/${task}/search_${task}.py ${CONFIG_FILE} ${CHECKPOINT_PATH} [optional arguments]

Subnet Retraining

python tools/${task}/train_${task}.py ${CONFIG_FILE} --cfg-options algorithm.mutable_cfg=${MUTABLE_CFG_PATH} [optional arguments]

• MUTABLE_CFG_PATH: Path of mutable_cfg. mutable_cfg represents config for mutable of the subnet searched out, used to specify different subnets for retraining. An example for mutable_cfg can be found here, and the usage can be found here.

Pruning

Pruning has four steps, including supernet pre-training, search for subnet on the trained supernet, subnet retraining and split checkpoint. The commands of the first two steps are similar to NAS, except that we need to use CONFIG_FILE of Pruning here. The commands of the two other steps are as follows.

Subnet Retraining

python tools/${task}/train_${task}.py ${CONFIG_FILE} --cfg-options algorithm.channel_cfg=${CHANNEL_CFG_PATH} [optional arguments]

Different from NAS, the argument that needs to be specified here is channel_cfg instead of mutable_cfg.

• CHANNEL_CFG_PATH: Path of channel_cfg. channel_cfg represents config for channel of the subnet searched out, used to specify different subnets for testing. An example for channel_cfg can be found here, and the usage can be found here.

Distillation

There is only one step to start knowledge distillation.

python tools/${task}/train_${task}.py ${CONFIG_FILE} --cfg-options algorithm.distiller.teacher.init_cfg.type=Pretrained algorithm.distiller.teacher.init_cfg.checkpoint=${TEACHER_CHECKPOINT_PATH} [optional arguments]

• TEACHER_CHECKPOINT_PATH: Path of teacher_checkpoint. teacher_checkpoint represents checkpoint of teacher model, used to specify different checkpoints for distillation.