Trained a couple of nets for (fun) comparisons, using identical hyperparams and early stopping on validation accuracy plateau schedule.
All experiments can be reproduced with the code from this repo using the default hyperparameters defined in src/main.py.
Check out the ReZero paper by the authors: https://arxiv.org/pdf/2003.04887.pdf
Neat idea which seems to improve ResNet convergence speed, especially at the beggining of training (see figures).
| Model | # params | runtime | epochs | Valid error (%) | Test error (%) |
|---|---|---|---|---|---|
| ResNet-20 | 272,474 | 70m3s | 398 | 7.63 | 7.98 |
| ResNet-56 | 855,770 | 127m41s | 281 | 6.04 | 6.44 |
| ResNet-110 | 1,730,768 | 240m53s | 313 | 6 | 6.39 |
| ReZero ResNet-20 | 272,483 | 63m9s | 327 | 7.44 | 7.94 |
| ReZero ResNet-56 | 855,797 | 134m44s | 303 | 6.31 | 6.55 |
| ReZero ResNet-110 | 1,730,714 | 301m19s | 410 | 5.84 | 5.88 |
ResNet-20:
ResNet-56:
ResNet-110:
This repo vs. original ResNet paper:
| Model | (paper) Test error (%) | (this repo) Test error (%) |
|---|---|---|
| ResNet-20 | 8.75 | 7.98 |
| ResNet-56 | 6.97 | 6.44 |
| ResNet-110 | 6.43 | 6.39 |
You can launch Distributed training from src/ using:
python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=2 --use_env main.py
This will train on a single machine (nnodes=1), assigning 1 process per GPU where nproc_per_node=2 refers to training on 2 GPUs. To train on N GPUs simply launch N processes by setting nproc_per_node=N.
The number of CPU threads to use per process is hard coded to torch.set_num_threads(1) for safety, and can be changed to your # cpu threads / nproc_per_node for better performance.
For more info on multi-node and multi-gpu distributed training refer to https://github.com/hgrover/pytorchdistr/blob/master/README.md
To train normally using nn.DataParallel or using the CPU:
python main.py --no_distributed
- Author: Fabio De Sousa Ribeiro
- Email: fdesosuaribeiro@lincoln.ac.uk