Skip to content
master
Go to file
Code

Files

Permalink
Failed to load latest commit information.

README.md

Build Status Try out our models in Google Colab on your own images!

This repo includes the semantic segmentation pre-trained models, training and inference code for the paper:

MSeg: A Composite Dataset for Multi-domain Semantic Segmentation (CVPR 2020, Official Repo) [PDF]
John Lambert*, Zhuang Liu*, Ozan Sener, James Hays, Vladlen Koltun
Presented at CVPR 2020. Link to MSeg Video (3min)

This repo is the second of 4 repos that introduce our work. It provides utilities to train semantic segmentation models, using a HRNet-W48 or PSPNet backbone, sufficient to train a winning entry on the WildDash benchmark.

  • mseg-api: utilities to download the MSeg dataset, prepare the data on disk in a unified taxonomy, on-the-fly mapping to a unified taxonomy during training.
  • mseg-mturk: utilities to perform large-scale Mechanical Turk re-labeling

One additional repo will be introduced in August 2020:

  • mseg-panoptic: provides Panoptic-FPN and Mask-RCNN training, based on Detectron2

Dependencies

First, install the mseg module from mseg-api Second, install the apex module. NVIDIA's apex is a library with Pytorch extensions. If your Pytorch version doesn't match the apex version, this is likely fine, and you can comment out the bare metal version check in their setup.py. Make sure your CUDA_HOME is also set by running echo $CUDA_HOME, it should be something like /usr/local/cuda-10.2.

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Install the MSeg-Semantic module:

  • mseg_semantic can be installed as a python package using

      pip install -e /path_to_root_directory_of_the_repo/
    

Make sure that you can run python -c "import mseg_semantic; print('hello world')" in python, and you are good to go!

MSeg Pre-trained Models

Each model is 528 MB in size. We provide download links and testing results (single-scale inference) below:

Abbreviated Dataset Names: VOC = PASCAL VOC, PC = PASCAL Context, WD = WildDash, SN = ScanNet

Model Training Set Training
Taxonomy
VOC
mIoU
PC
mIoU
CamVid
mIoU
WD
mIoU
KITTI
mIoU
SN
mIoU
h. mean Download
Link
MSeg (1M) MSeg train Universal 70.7 42.7 83.3 62.0 67.0 48.2 59.2 Google Drive
MSeg (3M)-480p MSeg
train
Universal 76.4 45.9 81.2 62.7 68.2 49.5 61.2 Google Drive
MSeg (3M)-720p MSeg
train
Universal 74.7 44.0 83.5 60.4 67.9 47.7 59.8 Google Drive
MSeg (3M)-1080p MSeg
train
Universal 72.0 44.0 84.5 59.9 66.5 49.5 59.8 Google Drive

Inference: Using our pre-trained models

We show how to perform inference here in our Google Colab.

Multi-scale inference greatly improves the smoothness of predictions, therefore our demos scripts use multi-scale config by default. While we train at 1080p, our predictions are often visually better when we feed in test images at 360p resolution.

If you have video input, and you would like to make predictions on each frame in the universal taxonomy, please set:

input_file=/path/to/my/video.mp4

If you have a set of images in a directory, and you would like to make a prediction in the universal taxonomy for each image, please set:

input_file=/path/to/my/directory

If you have as input a single image, and you would like to make a prediction in the universal taxonomy, please set:

input_file=/path/to/my/image

Now, run our demo script:

model_name=mseg-3m
model_path=/path/to/downloaded/model/from/google/drive
config=mseg_semantic/config/test/default_config_360_ms.yaml
python -u mseg_semantic/tool/universal_demo.py \
  --config=${config} model_name ${model_name} model_path ${model_path} input_file ${input_file}

Testing a Model Trained in the Universal Taxonomy

To compute mIoU scores on all train and test datasets, run the following (single-scale inference):

cd mseg_semantic/scripts
./eval_models.sh

This will launch several hundred SLURM jobs, each of the following form:

python mseg_semantic/tool/test_universal_tax.py --config=$config_path dataset $dataset_name model_path $model_path model_name $model_name

Please expect this to take many hours, depending upon your SLURM cluster capacity.

Testing a Model Trained in the Oracle Taxonomy

"Oracle" models are trained in a test dataset's taxonomy, on its train split. To compute mIoU scores on the test dataset's val or test set, please run the following:

This will launch 5 SLURM jobs, each of the following form

python mseg_semantic/tool/test_oracle_tax.py

Citing MSeg

If you find this code useful for your research, please cite:

@InProceedings{MSeg_2020_CVPR,
author = {Lambert, John and Liu, Zhuang and Sener, Ozan and Hays, James and Koltun, Vladlen},
title = {{MSeg}: A Composite Dataset for Multi-domain Semantic Segmentation},
booktitle = {Computer Vision and Pattern Recognition (CVPR)},
year = {2020}
}

Many thanks to Hengshuang Zhao for his semseg repo, which we've based much of this repository off of.

Other baseline models from our paper:

Below we report the performance of individually-trained models that serve as baselines. Inference is performed at single-scale below.

You can obtain the following table by running

python collect_results.py --regime zero_shot --scale ss --output_format markdown
python collect_results.py --regime oracle --scale ss --output_format markdown

after ./eval_models.sh:

Abbreviated Dataset Names: VOC = PASCAL VOC, PC = PASCAL Context, WD = WildDash, SN = ScanNet

Model Training Set Training
Tax-
onomy
VOC
mIoU
PC
mIoU
CamVid
mIoU
WD
mIoU
KITTI
mIoU
SN
mIoU
h. mean Download
Link
ADE20K (1M) ADE20K train Universal 35.4 23.9 52.6 38.6 41.6 42.9 36.9 Google Drive
BDD (1M) BDD train Universal 14.4 7.1 70.7 52.2 54.5 1.4 6.1 Google Drive
Cityscapes (1M ) Cityscapes train Universal 13.3 6.8 76.1 30.1 57.6 1.7 6.8 Google Drive
COCO (1M) COCO train Universal 73.4 43.3 58.7 38.2 47.6 33.4 45.8 Google Drive
IDD (1M) IDD train Universal 14.6 6.5 72.1 41.2 51.0 1.6 6.5 Google Drive
Mapillary (1M) Mapillary
train
Universal 22.5 13.6 82.1 55.4 67.7 2.1 9.3 Google Drive
SUN RGB-D (1M) SUN RGBD
train
Universal 10.0 4.3 0.1 1.9 1.1 42.6 0.3 Google Drive
MSeg (1M) MSeg
train.
Universal 70.7 42.7 83.3 62.0 67.0 48.2 59.2 Google Drive
MSeg Mix w/o relabeling (1M) MSeg train. Universal 70.2 42.7 82.0 62.7 65.5 43.2 57.6 Google Drive
MGDA Baseline (1M) MSeg train. Universal 68.5 41.7 82.9 61.1 66.7 46.7 58.0 Google Drive
MSeg (3M)-480p MSeg
train
Universal 76.4 45.9 81.2 62.7 68.2 49.5 61.2 Google Drive
MSeg (3M)-720p MSeg
train
Universal 74.7 44.0 83.5 60.4 67.9 47.7 59.8 Google Drive
MSeg (3M)-1080p MSeg
train
Universal 72.0 44.0 84.5 59.9 66.5 49.5 59.8 Google Drive
Naive Mix Baseline (1M) MSeg
train.
Naive Google Drive
Oracle (1M) 77.8 45.8 78.8 -** 58.4 62.3 -

**WildDash has no training set, so an "oracle" model cannot be trained.

Oracle Model Download Links

Note that the output number of classes for 7 of the models listed above will be identical (194 classes). These are the models that represent a single training dataset's performance -- ADE20K (1M), BDD (1M), Cityscapes (1M ), COCO (1M), IDD (1M), Mapillary (1M), SUN RGB-D (1M). When we train a baseline model on a single dataset, we train it in the universal taxonomy (w/ 194 classes). If we did not do so, we would need to specify 7*6=42 mappings (which would be unbelievably tedious and also fairly redundant) since we measure each's performance according to zero-shot cross-dataset generalization -- 7 training datasets with their own taxonomy, and each would need its own mapping to each of the 6 test sets.

By training each baseline that is trained on a single training dataset within the universal taxonomy, we are able to specify just 7+6=13 mappings in this table (each training dataset's taxonomy->universal taxonomy, and then universal taxonomy to each test dataset).

Results on the Training Datasets

You can obtain the following table by running

python collect_results.py --regime training_datasets --scale ss --output_format markdown

after ./eval_models.sh:

Model Name COCO ADE20k Mapill IDD BDD Citysca SUN-RGBD h. mean
COCO-1m 52.7 19.1 28.4 31.1 44.9 46.9 29.6 32.4
ADE20K-1m 14.6 45.6 24.2 26.8 40.7 44.3 36.0 28.7
Mapillary-1m 7.0 6.2 53.0 50.6 59.3 71.9 0.3 1.7
IDD-1m 3.2 3.0 24.6 64.9 42.4 48.0 0.4 2.3
BDD-1m 3.8 4.2 23.2 32.3 63.4 58.1 0.3 1.6
Cityscapes-1m 3.4 3.1 22.1 30.1 44.1 77.5 0.2 1.2
SUN RGBD-1m 3.4 7.0 1.1 1.0 2.2 2.6 43.0 2.1
MSeg-1m 50.7 45.7 53.1 65.3 68.5 80.4 50.3 57.1
MSeg-1m-w/o relabeling 50.4 45.4 53.1 65.1 66.5 79.5 49.9 56.6
MSeg-MGDA-1m 48.1 43.7 51.6 64.1 67.2 78.2 49.9 55.4
MSeg-3m-480p 56.1 49.6 53.5 64.5 67.8 79.9 49.2 58.5
MSeg-3m-720p 53.3 48.2 53.5 64.8 68.6 79.8 49.3 57.8
MSeg-3m-1080p 53.6 49.2 54.9 66.3 69.1 81.5 50.1 58.8

Experiment Settings

We use the HRNetV2-W48 architecture. All images are resized to 1080p (shorter size=1080) at training time before a crop is taken.

We run inference with the shorter side of each test image at three resolutions (360p, 720p, 1080p), and take the max among these 3 possible resolutions. Note that in the original semseg repo, the author specifies the longer side of an image, whereas we specify the shorter side. Batch size is set to 35.

We generally follow the recommendations of Zhao et al.: Our data augmentation consists of random scaling in the range [0.5,2.0], random rotation in the range [-10,10] degrees. We use SGD with momentum 0.9, weight decay of 1e-4. We use a polynomial learning rate with power 0.9. Base learning rate is set to 1e-2. An auxiliary cross-entropy (CE) loss is added to intermediate activations, a linear combination with weight 0.4. In our data, we use 255 as an ignore/unlabeled flag for the CE loss. We use Pytorch's Distributed Data Parallel (DDP) package for multiprocessing, with the NCCL backend. We use apex opt_level: 'O0' and use a crop size of 713x713, with synchronized BN.

Training Instructions

Download the HRNet Backbone Model here from the original authors' OneDrive. We use 8 Quadro RTX 6000 cards, each w/ 24 GB of RAM, for training.

Running unit tests and integration tests

To run the unit tests, execute

pytest tests

All should pass. To run the integration tests, follow the instructions in the following 3 files, then run:

python test_test_oracle_tax.py
python test_test_universal_tax.py
python test_universal_demo.py

All should also pass.

Frequently Asked Questions (FAQ) (identical to FAQ on mseg-api page)

Q: Do the weights include the model structure or it's just the weights? If the latter, which model do these weights refer to? Under the models directory, there are several model implementations.

A: The pre-trained models follow the HRNet-W48 architecture. The model structure is defined in the code here. The saved weights provide a dictionary between keys (unique IDs for each weight identifying the corresponding layer/layer type) and values (the floating point weights).

Q: How is testing performed on the test datasets? In the paper you talk about "zero-shot transfer" -- how this is performed? Are the test dataset labels also mapped or included in the unified taxonomy? If you remapped the test dataset labels to the unified taxonomy, are the reported results the performances on the unified label space, or on each test dataset's original label space? How did you you obtain results on the WildDash dataset - which is evaluated by the server - when the MSeg taxonomy may be different from the WildDash dataset.

A: Regarding "zero-shot transfer", please refer to section "Using the MSeg taxonomy on a held-out dataset" on page 6 of our paper. This section describes how we hand-specify mappings from the unified taxonomy to each test dataset's taxonomy as a linear mapping (implemented here in mseg-api). All results are in the test dataset's original label space (i.e. if WildDash expects class indices in the range [0,18] per our names_list, our testing script uses the TaxonomyConverter transform_predictions_test() functionality to produce indices in that range, remapping probabilities.

Q: Why don't indices in MSeg_master.tsv match the training indices in individual datasets? For example, for the road class: In idd-39, road has index 0, but in idd-39-relabeled, road has index 19. It is index 7 in cityscapes-34. The cityscapes-19-relabeled index road is 11. As far as I can tell, ultimately the 'MSeg_Master.tsv' file provides the final mapping to the MSeg label space. But here, the road class seems to have an index of 98, which is neither 19 nor 11.

A: Indeed, unified taxonomy class index 98 represents "road". But we use the TaxonomyConverter to accomplish the mapping on the fly from idd-39-relabeled to the unified/universal taxonomy (we use the terms "unified" and "universal" interchangeably). This is done by adding a transform in the training loop that calls TaxonomyConverter.transform_label() on the fly. You can see how that transform is implemented here in mseg-semantic.

Q: When testing, but there are test classes that are not in the unified taxonomy (e.g. Parking, railtrack, bridge etc. in WildDash), how do you produce predictions for that class? I understand you map the predictions with a binary matrix. But what do you do when there's no one-to-one correspondence?

A: WildDash v1 uses the 19-class taxonomy for evaluation, just like Cityscapes. So we use the following script to remap the 34-class taxonomy to 19-class taxonomy for WildDash for testing inference and submission. You can see how Cityscapes evaluates just 19 of the 34 classes here in the evaluation script and in the taxonomy definition. However, bridge and rail track are actually included in our unified taxonomy, as you’ll see in MSeg_master.tsv.

Q: How are datasets images read in for training/inference? Should I use the dataset_apis from mseg-api?

A: The dataset_apis from mseg-api are not for training or inference. They are purely for generating the MSeg dataset labels on disk. We read in the datasets using mseg_semantic/utils/dataset.py and then remap them to the universal space on the fly.

About

An Official Repo of CVPR '20 "MSeg: A Composite Dataset for Multi-Domain Segmentation"

Resources

License

Packages

No packages published
You can’t perform that action at this time.