This repository reproduces the results of the following paper:
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
Bichen Wu1, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer (Fasebook Research)
Layers to Search are from a FacebookResearch repository Utils stuff is taken from DARTS repository
- Building blocks (searched layers) was taken from the FacebookResearch repository (Quick Note: their repo consists files with names fbnet*, but doesn't include any fbnet architecture from their paper)
- All hyperparameters exactly as in the original paper
- Latency Calculation Code
- Successfully Tested on Cifar10
- Logging. You can find all my logs and tensorboards into SAVED_LOGS/supernet_training (for supernet training) and SAVED_LOGS/architectures_training (for architectures training)
- Multi-GPU Support
- ImageNet Training. It will take 27 hours x 8 GPU
The architectures are not SOTAs: we search only for filters' sizes (these numbers are good for the simple architecture) and the goal is to Reduce Inference Time for Your Device
FacebookResearch didn't share latencies for their test machines, so, I couldn't prove their latencies results, but I have builded and trained theier proposed architectures:
| FBNet Architecture | top1 validation accuracy | top3 validation accuracy |
|---|---|---|
| FBNet-A | 78.8% | 95.4% |
| FBNet-B | 82% | 96% |
| FBNet-C | 79.9% | 95.6% |
| FBNet-s8 (for Samsung Galaxy S8) | 79.6% | 95.7% |
| FBNet-iPhoneX | 76.2% | 94.3% |
| ------ | ------ | ------ |
| fbnet_cpu_sample1 | 82.8% | 98.9% |
| fbnet_cpu_sample2 | 80.6% | 95.7% |
Note: be cautious! these numbers are just validation's the bests (without confidence intervals, measured in a single run). Do not use these numbers to make decisions. They are here to compliment the tensorboards in the SAVED_LOGS directory. The reason why I don't split data into validation and test is in the next note. I plan to test on Imagenet someday and calculate the final numbers
Note: as it was stated in the paper and according to my results, if we train with small images (as cifar's 32x32), we can see a lot of 'skip' layers into the resulting architecture. I feel, for cifar10 we should search for less number of layers
The repository consists of 2 Neural Net Models:
(1) FBNet Searched Architectures. All tools in the architecture_functions folder
(2) Stochastic SuperNet to search for new architectures. All tools in the supernet_functions folder
They use different functions and architectures specification. Functions used by both Nets are in the folders: general_functions (utilities) and fbnet_building_blocks (modified code of facebookresearch team)
I encourage you to visit TRAINIG_DETAILS.md in this folder for details and instructions.
I have tested the code with the following dockerfile: DOCKERFILE (Pytorch 0.4.1 Nightly)
btw I think it should work well with Pytorch 0.4.0+
MIT