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structure_knowledge_distillation

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The official code for the paper 'Structured Knowledge Distillation for Semantic Segmentation'. (CVPR 2019 ORAL) and extension to other tasks.

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- __init__.py
- datasets.py
- output_base_esp.gif
- output_esp_c_base.gif
- output_esp_c_sd.gif
- output_sd_esp.gif
- - __init__.py
- - bn.cu
  - bn.h
  - bn.o
  - common.h
  - lib_cffi.cpp
  - lib_cffi.h
- __init__.py
- bn.py
- build.py
- build.sh
- dense.py
- functions.py
- misc.py
- residual.py
- __init__.py
- evaluate.py
- kd_model.py
- pspnet_combine.py
- sagan_models.py
- spectral.py
- berlin_000001_000019_leftImg8bit.png
- __init__.py
- criterion.py
- parallel.py
- train_options.py
- utils.py
.gitignore
LICENSE
README.md
run_test.sh
run_train_val.sh
test.py
train_and_eval.py

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#  Structured Knowledge Distillation for Dense Prediction

This repository contains the source code of our paper [Structured Knowledge Distillation for Dense Prediction](https://arxiv.org/pdf/1903.04197.pdf).
It is an extension of our paper [Structured Knowledge Distillation for Semantic Segmentation](https://www.zpascal.net/cvpr2019/Liu_Structured_Knowledge_Distillation_for_Semantic_Segmentation_CVPR_2019_paper.pdf) (accepted for publication in [CVPR'19](http://cvpr2019.thecvf.com/), oral).

We have update a more stable version of training the GAN part in the master branch.

If you want to transfer our pair-wise distilaltion and pixel-wise distillation in your own work or you want to use our trained models in the conference version, you can checkout to the old branck 'cvpr_19'.

## Sample results

Demo video for the student net (ESPNet) on Camvid

After distillation with mIoU 65.1:
![image](https://github.com/irfanICMLL/structure_knowledge_distillation/blob/master/demo/output_sd_esp.gif)

Before distillation with mIoU 57.8:
![image](https://github.com/irfanICMLL/structure_knowledge_distillation/blob/master/demo/output_base_esp.gif)
 
## Structure of this repository
This repository is organized as:
* [libs](/libs/) This directory contains the inplaceABNSync modes.
* [dataset](/dataset/) This directory contains the dataloader for different datasets.
* [network](/network/) This directory contains a model zoo for network models.
* [utils](/utils/) This directory contains api for calculating the distillation loss.

## Performance on the Cityscape dataset
We apply the distillation method to training the [PSPNet](https://arxiv.org/abs/1612.01105). We used the dataset splits (train/val/test) provided [here](https://github.com/speedinghzl/pytorch-segmentation-toolbox). We trained the models at a resolution of 512x512.
Pi: Pixel-wise distillation PA: Pair-wise distillation HO: holistic distillation

| Model | Average |
| -- | -- |
| baseline | 69.10 |
| +Pi | 70.51 |
| +Pi+Pa | 71.78 |
| +Pi+Pa+Ho | 74.08 |

## Pre-trained model and Performance on other tasks
Pretrain models for three tasks can be found here:

| Task |Dataset| Network |Method | Evaluation Metric|Link|
| -- | -- |-- | -- |-- |-- |
| Semantic Segmentation |Cityscapes| ResNet18|Baseline|miou: 69.10 |-|
| Semantic Segmentation |Cityscapes| ResNet18|+ our distillation|miou: 75.3 |[link](https://cloudstor.aarnet.edu.au/plus/s/uL3qO51A4qxY6Eu) |
| Object Detection |COCO| [FCOS-MV2-C128](https://github.com/tianzhi0549/FCOS.git)|Baseline|mAP: 30.9 |-|
| Object Detection |COCO|  [FCOS-MV2-C128](https://github.com/tianzhi0549/FCOS.git)|+ our distillation|mAP: 34.0 |[link](https://cloudstor.aarnet.edu.au/plus/s/Hqq9HSI8GXrR0b0) |
| Depth estimation |nyudv2| [VNL](https://github.com/YvanYin/VNL_Monocular_Depth_Prediction.git)|baseline|rel: 13.5 |-|
| Depth estimation | nyudv2|[VNL](https://github.com/YvanYin/VNL_Monocular_Depth_Prediction.git)|+ our distillation|rel: 13.0 |[link](https://cloudstor.aarnet.edu.au/plus/s/IXk0i0cJaibgJAr)|

Note: Other chcekpoints can be obtained by email: yifan.liu04@adelaide.edu.au if needed.

## Requirement
python3.5

pytorch0.4.1

ninja

numpy

cv2

Pillow

We recommend to use [Anaconda](https://conda.io/docs/user-guide/install/linux.html).

We have tested our code on Ubuntu 16.04.

### Compiling

Some parts of InPlace-ABN have a native CUDA implementation, which must be compiled with the following commands:
```bash
cd libs
sh build.sh
python build.py
``` 
The `build.sh` script assumes that the `nvcc` compiler is available in the current system search path.
The CUDA kernels are compiled for `sm_50`, `sm_52` and `sm_61` by default.
To change this (_e.g._ if you are using a Kepler GPU), please edit the `CUDA_GENCODE` variable in `build.sh`.

## Quick start to test the model
1. download the [Cityscape dataset](https://www.cityscapes-dataset.com/)
2. sh run_test.sh [you should change the data-dir to your own]. By using our distilled student model, which can be gotten in [ckpt], an mIoU of 73.05 is achieved on the Cityscape test set, and 75.3 on validation set.

| Model | Average | roda | sidewalk | building|	wall | fence | pole | trafficlight | trafficsign | vegetation | terrain | sky | person | rider | car | truck | bus | train | motorcycle | bicycle |
| -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
| IoU | 73.05 | 97.57 | 78.80 | 91.42 | 50.76 | 50.88 | 60.77 | 67.93 | 73.18 | 92.49 | 70.36 | 94.56 | 82.81 | 61.64 | 94.89 | 60.14 | 66.62 | 59.93 | 61.50 | 71.71 |

Note: Depth estimation task and object detection task can be test through the original projects of VNL and FCOS using our checkpoints.
## Train script
Download the pre-trained [teacher weight](https://cloudstor.aarnet.edu.au/plus/s/tFjYfBJiarVi0pG):

If you want to reproduce the ablation study in our paper, please modify is_pi_use/is_pa_use/is_ho_use in the run_train_eval.sh.
sh run_train_eval.sh

## Test script
If you want to test your method on the cityscape test set, please modify the data-dir and resume-from path to your own, then run the test.sh and submit your results to www.cityscapes-dataset.net/submit/ 
sh test.sh

## License
For academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact [Yifan Liu](yifan.liu04@adelaide.edu.au) and [Chunhua Shen](chunhua.shen@adelaide.edu.au).