To test nas method, you can use following command
python tools/${task}/test_${task}.py ${CONFIG_FILE} ${CHECKPOINT_PATH} --cfg-options algorithm.mutable_cfg=${MUTABLE_CFG_PATH} [optional arguments]
task
: one of mmcls
、mmdet
and mmseg
MUTABLE_CFG_PATH
: Path of mutable_cfg
. mutable_cfg
represents config for mutable of the subnet searched out, used to specify different subnets for testing. An example for mutable_cfg
can be found here.The usage of optional arguments are the same as corresponding tasks like mmclassification, mmdetection and mmsegmentation.
For example,
python tools/mmcls/test_mmcls.py \ configs/nas/spos/spos_subnet_shufflenetv2_8xb128_in1k.py \ your_subnet_checkpoint_path \ --cfg-options algorithm.mutable_cfg=configs/nas/spos/SPOS_SHUFFLENETV2_330M_IN1k_PAPER.yaml
If you train a slimmable model during retraining, checkpoints of different subnets are actually fused in only one checkpoint. You can split this checkpoint to multiple independent checkpoints by using the following command
python tools/model_converters/split_checkpoint.py ${CONFIG_FILE} ${CHECKPOINT_PATH} --channel-cfgs ${CHANNEL_CFG_PATH} [optional arguments]
CHANNEL_CFG_PATH
: A list of paths of channel_cfg
. For example, when you
retrain a slimmable model, your command will be like --cfg-options algorithm.channel_cfg=cfg1,cfg2,cfg3
.
And your command here should be --channel-cfgs cfg1 cfg2 cfg3
. The order of them should be the same.For example,
python tools/model_converters/split_checkpoint.py \ configs/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k.py \ your_retraining_checkpoint_path \ --channel-cfgs configs/pruning/autoslim/AUTOSLIM_MBV2_530M_OFFICIAL.yaml configs/pruning/autoslim/AUTOSLIM_MBV2_320M_OFFICIAL.yaml configs/pruning/autoslim/AUTOSLIM_MBV2_220M_OFFICIAL.yaml
To test pruning method, you can use following command
python tools/${task}/test_${task}.py ${CONFIG_FILE} ${CHECKPOINT_PATH} --cfg-options algorithm.channel_cfg=${CHANNEL_CFG_PATH} [optional arguments]
task
: one of mmcls
、mmdet
and mmseg
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.For example,
python ./tools/mmcls/test_mmcls.py \ configs/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k.py \ your_splitted_checkpoint_path --metrics accuracy \ --cfg-options algorithm.channel_cfg=configs/pruning/autoslim/AUTOSLIM_MBV2_530M_OFFICIAL.yaml
To test distillation method, you can use the following command
python tools/${task}/test_${task}.py ${CONFIG_FILE} ${CHECKPOINT_PATH} [optional arguments]
task
: one of mmcls
、mmdet
and mmseg
For example,
python ./tools/mmseg/test_mmseg.py \ configs/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k.py \ your_splitted_checkpoint_path --show
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