Detecting the Unexpected via Image Resynthesis

Krzysztof Lis, Krishna Nakka, Pascal Fua, Mathieu Salzmann ICCV 2019

Installation

git clone https://github.com/cvlab-epfl/detecting-the-unexpected --recursive

Install libraries:

pip install numpy opencv-python-headless matplotlib h5py natsort imageio torch torchvision scipy tqdm tensorboard future ipython

To run notebook files:

pip install jupyterlab

Trained weights

Download weight files [2 GiB] and place them in detecting-the-unexpected/exp (or another location specified with env variable DIR_EXPERIMENTS).

Directory structure

detecting-the-unexpected

src
- a01_sem_seg - semantic segmentation
- a04_reconstruction - image synthesis from labels
- a05_differences - discrepancy detector
- a05_road_rec_baseline - the RBM autoencoder baseline method
exp - trained weights (override location with env variable DIR_EXPERIMENTS)
- 0120_BayesSegNet_BDD
- 0405_pix2pixHD512_nostyle_ctc_crop
- 0521_Diff_SwapFgd_ImgAndLabelVsGen_semGT
- ...
datasets - or another location specified by env variable DIR_DSETS
- dataset_RoadAnomaly - our dataset, download here
- dataset_LostAndFound/1024x512 - Lost and Found but downscaled by a factor of 2
data
- joint_pipeline_example - a few images from Lost and Found, to demonstrate the joint pipeline
- out - default output location of the joint pipeline
- discrepancy_dataset/cityscapes - synthetic discrepancy datasets
  - 051X_semGT__fakeSwapFgd__genNoSty - place our discrepancy dataset here. [052X_synthetic_discrepancy__fakeSwapFgd.7z (208MB)].

Running the pipeline

Please see the notebook Exec_Joint_Pipeline.ipynb:

# specify input dataset, for example a directory with images
from src.datasets.dataset import DatasetImageDir
dset = DatasetImageDir(dir_root='data/joint_pipeline_example')
dset.discover()

# load the networks
from src.a05_differences.E1_article_evaluation import DiscrepancyJointPipeline
joint_pipeline = DiscrepancyJointPipeline()
joint_pipeline.init_semseg()
joint_pipeline.init_gan()
joint_pipeline.init_discrepancy()

# run and show results in notebook
joint_pipeline.run_on_dset(dset, b_show=True)

The notebook Exec_Evaluations.ipynb can be used to the steps separately saving intermediate results.

Dataset processing

The Lost and Found and Cityscapes datasets were used in 1024x512 resolution, while the original downloads are 2048x1024. The conversion can be performed with the scripts in src/datasets/conversion_tools.

The script needs a webp encoder and imagemagick. On Windows the ImageMagick binary is called magick instead of convert, so there is a slightly different version of the scripts for this OS.

Lost and Found

Download the 2048x1024 dataset: leftImg8bit.zip (left 8-bit images - train and test set) and gtCoarse.zip (annotations for train and test sets) from the LAF site
Set $DIR_LAF to point to the directory of the original dataset
Set $DIR_LAF_SMALL to the place the compressed dataset should be written to
run compress_LAF_1024x512_webp.sh

Cityscapes

Download the 2048x1024 dataset (leftImg8bit and gtFine) from the Cityscapes site
Set $DIR_CITYSCAPES to point to the directory of the original dataset
Set $DIR_CITYSCAPES_SMALL to the place the compressed dataset should be written to
run compress_Cityscapes_1024x512_webp.sh

Discrepancy Network

Synthetic discrepancy dataset

The synthetic discrepancy dataset used in our experiments can be downloaded here: [052X_synthetic_discrepancy__fakeSwapFgd.7z (208MB)]. Please place it at data/discrepancy_dataset/cityscapes/051X_semGT__fakeSwapFgd__genNoSty.

To generate your own version of the synthetic discrepancy dataset, please follow the instructions in Discrepancy_GenerateDataset.ipynb.

Training

The Experiment class (src/pipeline/experiment.py) is used to train the networks (extract from src/a05_differences/experiments.py):

# First we add a new class for our experiment
class Exp0552_NewDiscrepancyTraining(Exp0521_SwapFgd_ImgAndLabelsVsGen_semGT):
	cfg = add_experiment(Exp0521_SwapFgd_ImgAndLabelsVsGen_semGT.cfg,
		name = '0552_NewDiscrepancyTraining',
		gen_name = '051X_semGT__fakeSwapFgd__genNoSty',
	)

# Execute the training process
# from src.a05_differences.experiments import MyExperimentVariant
Exp0552_NewDiscrepancyTraining.training_procedure()

Weights will be written to $DIR_EXP/0552_NewDiscrepancyTraining Checkpoints are saved every epoch:

chk_best.pth - checkpoint with the lowest loss on eval set
chk_last.pth - checkpoint after the most recent epoch
optimizer.pth - optimizer data (momentum etc) after the most recent epoch

The directory will also contain:

[date]_log - predictions for sample evaluation frames indexed by epoch
training.log - logs from the logging module, if the training procedure failed, the exception will be written there

The loss is written to tensorboard can can be displayed in the following way:

	tensorboard --logdir $DIR_EXP/0552_NewDiscrepancyTraining

Training scripts for variants of the discrepancy network:

0521_train_discrepancy__full.py
0516_train_discrepancy__gen_only.py
0517_train_discrepancy__labels_only.py
0552_train_discrepancy__example_new_variant.py

Contact

I am working to provide more examples and automated scripts.

For any additional information or requests, please contact Krzysztof Lis.

zhouhuan-hust / detecting-the-unexpected