This directory contains the training and evaluation scripts for stitching ResNet-18/50 and ResNet-18/Swin-Ti.
- PyTorch 1.10.1+
- CUDA 11.1+
- fvcore 0.1.5
Inside this directory, you can use the following commands to replicate our results on ImageNet.
To stitch a ResNet-18 with ResNet-50 with 8 GPUs on ImageNet.
./distributed_train.sh 8 \
[path/to/imagenet] \
-b 128 \
--stitch_config configs/resnet18_resnet50.json \
--sched cosine \
--epochs 30 \
--lr 0.05 \
--amp --remode pixel \
--reprob 0.6 \
--aa rand-m9-mstd0.5-inc1 \
--resplit --split-bn -j 10 --dist-bn reduceTo stitch a ResNet-18 with Swin-Ti with 8 GPUs on ImageNet.
./distributed_train.sh 8 \
[path/to/imagenet] \
-b 128 \
--stitch_config configs/resnet18_swin_ti.json \
--sched cosine \
--epochs 30 \
--lr 0.05 \
--amp --remode pixel \
--reprob 0.6 \
--aa rand-m9-mstd0.5-inc1 \
--resplit --split-bn -j 10 --dist-bn reduceDownload our trained wegihts
| Stitched Models | Checkpoint |
|---|---|
| ResNet-18, ResNet-50 | link |
| ResNet-18, Swin-Ti | link |
To evaluate,
./distributed_train.sh 1 \
[path/to/imagenet] \
-b 128 \
--stitch_config configs/resnet18_swin_ti.json \
--sched cosine \
--epochs 30 \
--lr 0.05 \
--amp --remode pixel \
--reprob 0.6 \
--aa rand-m9-mstd0.5-inc1 \
--resplit --split-bn -j 10 --dist-bn reduce \
--resume [path/to/weights] --evalAfter evaluation, you can find a stitches_res.txt under the outputs/[name]/default/ directory which contains the results for all stitches. You can find our evaluation results at results/
Please note that our experiments involving the stitching of ResNet-18/50 and ResNet-18/Swin-Ti did not extensively explore optimal experiment settings. Hyperparameters were barely tuned during these experiments. Therefore, if you encounter poor performance with some stitched models, selecting the networks on the Pareto frontier will again give you a smooth FLOPs-accuracy curve.
This implementation adopts code from timm. We thank the authors for their released code.