SqueezeNAS: Fast Neural Architecture Search for Faster Semantic Segmentation

This repository contains the trained deep neural network architectures and weights as well as evaluation code for the SqueezeNAS paper.

If you find this useful, or if you use it in your commercial work, please cite:

@inproceedings{2019_SqueezeNAS,
    author = {Albert Shaw and Daniel Hunter and Forrest Iandola and Sammy Sidhu},
    title = {{SqueezeNAS}: Fast Neural Architecture Search for Faster Semantic Segmentation},
    booktitle = {ICCV Neural Architects Workshop},
    year = {2019}
}

Requirements

Python >= 3.7.0
PyTorch >= 1.0.1
torchvision >= 0.2.2
tabulate == 0.8.3
numpy >= 1.15.4
Pillow

Cityscapes Dataset

The dataset can be downloaded from the cityscapes website.

1. Extract the annotations and copy the gtFine directory to data/gtFine.
2. Extract the left camera images and copy the leftImg8bit directory to data/leftImg8bit.

Instructions

1. Install the required packages.
2. Clone this repository.
3. Download and extract the Cityscapes dataset to the data folder as described above.
4. Initialize the cityscapesScripts submodule: git submodule init && git submodule update.

(Alternatively download a zip of the cityscapesScripts repo and extract the cityscapesscripts folder to cityscapesScripts/cityscapesscripts.)

Evaluation

Use the eval.py script to evaluate the models. Results are saved into the results folder.

To evaluate the squeezenas_lat_small network, run:
python3 eval.py --net squeezenas_lat_small

By default the networks are evaluated using the GPU. Use the --use_cpu option to evaluate only on CPU.
python3 eval.py --net squeezenas_lat_small --use_cpu

The other networks can be evaluated by running the specifying the other networks for the --net option.

bash evaluate_all_models.sh will evaluate all the models.

There is some minor variation to results reported in the paper which is the maximum validation accuracy chosen from the last 20 epochs of training due to the models which were saved.

Expected results:

eval.py command line options

eval.py uses a command line interface. For help, please run: python3 eval.py --help

The following flags are available:

-h, --help            show this help message and exit
-v, --verbose         if this option is supplied, a full layer by layer
                      GigaMAC summary of the model will be printed. If this
                      option is not supplied, only the total GigaMACs will
                      be printed.
-m, --only_macs       if this option is supplied, no inference is run, only
                      MAC count is printed.
-c, --use_cpu         If this option supplied, the network will be evaluated
                      using the cpu. Otherwise the gpu will be used to run
                      evaluation.
--net {squeezenas_lat_large,squeezenas_lat_small,squeezenas_lat_xlarge,squeezenas_mac_large,
  squeezenas_mac_small,squeezenas_mac_xlarge}