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Official implementation of "Energy-Based Models for Deep Probabilistic Regression" (ECCV 2020) and "How to Train Your Energy-Based Model for Regression" (BMVC 2020).

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ebms_regression

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Official implementation (PyTorch) of the papers:

  • Energy-Based Models for Deep Probabilistic Regression, ECCV 2020 [arXiv] [project].
    Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat, Thomas B. Schön.
    We propose a general and conceptually simple regression method with a clear probabilistic interpretation. We create an energy-based model of the conditional target density p(y|x), using a deep neural network to predict the un-normalized density from the input-target pair (x,y). This model of p(y|x) is trained by directly minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. Notably, our model achieves a 2.2% AP improvement over Faster-RCNN for object detection on the COCO dataset, and sets a new state-of-the-art on visual tracking when applied for bounding box regression.

  • How to Train Your Energy-Based Model for Regression, BMVC 2020 [arXiv] [project].
    Fredrik K. Gustafsson, Martin Danelljan, Radu Timofte, Thomas B. Schön.
    We propose a simple yet highly effective extension of noise contrastive estimation (NCE) to train energy-based models p(y|x; theta) for regression tasks. Our proposed method NCE+ can be understood as a direct generalization of NCE, accounting for noise in the annotation process of real-world datasets. We provide a detailed comparison of NCE+ and six popular methods from literature, the results of which suggest that NCE+ should be considered the go-to training method. We also apply NCE+ to the task of visual tracking, achieving state-of-the-art performance on five commonly used datasets. Notably, our tracker achieves 63.7% AUC on LaSOT and 78.7% Success on TrackingNet.

This repository contains code for the experiments on object detection, age estimation (TODO!), head-pose estimation (TODO!) and 1D regression. Code for the visual tracking experiments is available at pytracking.

If you find this work useful, please consider citing:

@inproceedings{gustafsson2020energy,
  author={Gustafsson, Fredrik K and Danelljan, Martin and Bhat, Goutam and Sch{\"o}n, Thomas B},
  title = {Energy-Based Models for Deep Probabilistic Regression},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  month = {August},
  year = {2020}
}

@inproceedings{gustafsson2020train,
  author={Gustafsson, Fredrik K and Danelljan, Martin and Timofte, Radu and Sch{\"o}n, Thomas B},
  title = {How to Train Your Energy-Based Model for Regression},
  booktitle = {Proceedings of the British Machine Vision Conference (BMVC)},
  month = {September},
  year = {2020}
}

Acknowledgements

Index

Usage

The code has been tested on Ubuntu 16.04. A docker image is provided (see below).

1dregression

  • $ docker pull fregu856/ebms_regression:ufoym_deepo_pytorch-py36-cu90_ebms_regression
  • Create start_docker_image_ebms_regression.sh containing (My username on the server is fregu482, i.e., my home folder is /home/fregu482. You will have to modify this accordingly):
#!/bin/bash

# DEFAULT VALUES
GPUIDS="0"
NAME="ebms_regression_GPU"

NV_GPU="$GPUIDS" nvidia-docker run -it --rm --shm-size 12G \
        -p 7200:7200\
        --name "$NAME""0" \
        -v /home/fregu482:/root/ \
        fregu856/ebms_regression:ufoym_deepo_pytorch-py36-cu90_ebms_regression bash
  • (Inside the image, /root/ will now be mapped to /home/fregu482, i.e., $ cd -- takes you to the regular home folder)
  • (To create more containers, change the lines GPUIDS="0", --name "$NAME""0" and -p 7200:7200)
  • General Docker usage:
    • To start the image:
      • $ sh start_docker_image_ebms_regression.sh
    • To commit changes to the image:
      • Open a new terminal window.
      • $ docker commit ebms_regression_GPU0 fregu856/ebms_regression:ufoym_deepo_pytorch-py36-cu90_ebms_regression
    • To exit the image without killing running code:
      • Ctrl + P + Q
    • To get back into a running image:
      • $ docker attach ebms_regression_GPU0
  • Example usage:
$ sh start_docker_image_ebms_regression.sh
$ cd --
$ python ebms_regression/1dregression/1/nce+_train.py

detection

  • $ docker pull fregu856/ebms_regression:ufoym_deepo_pytorch-py36-cu90_ebms_regression
  • Create start_docker_image_ebms_regression.sh containing (My username on the server is fregu482, i.e., my home folder is /home/fregu482. You will have to modify this accordingly):
#!/bin/bash

# DEFAULT VALUES
GPUIDS="0"
NAME="ebms_regression_GPU"

NV_GPU="$GPUIDS" nvidia-docker run -it --rm --shm-size 12G \
        -p 7200:7200\
        --name "$NAME""0" \
        -v /home/fregu482:/root/ \
        fregu856/ebms_regression:ufoym_deepo_pytorch-py36-cu90_ebms_regression bash
  • (Inside the image, /root/ will now be mapped to /home/fregu482, i.e., $ cd -- takes you to the regular home folder)
  • (To create more containers, change the lines GPUIDS="0", --name "$NAME""0" and -p 7200:7200)
  • General Docker usage:
    • To start the image:
      • $ sh start_docker_image_ebms_regression.sh
    • To commit changes to the image:
      • Open a new terminal window.
      • $ docker commit ebms_regression_GPU0 fregu856/ebms_regression:ufoym_deepo_pytorch-py36-cu90_ebms_regression
    • To exit the image without killing running code:
      • Ctrl + P + Q
    • To get back into a running image:
      • $ docker attach ebms_regression_GPU0
  • $ docker attach ebms_regression_GPU0
  • $ cd ebms_regression
  • $ git clone https://github.com/cocodataset/cocoapi.git
  • $ cd cocoapi/PythonAPI
  • $ python setup.py build_ext install
  • $ cd ebms_regression
  • $ git clone https://github.com/NVIDIA/apex.git
  • $ cd apex
  • $ python setup.py install --cuda_ext --cpp_ext
  • $ cd ebms_regression/detection
  • $ python setup.py build develop
  • Ctrl + P + Q
  • $ docker commit ebms_regression_GPU0 fregu856/ebms_regression:ufoym_deepo_pytorch-py36-cu90_ebms_regression
  • Example usage:
$ sh start_docker_image_ebms_regression.sh
$ cd --
$ cd ebms_regression/detection
$ python tools/train_net.py --config-file "configs/nce+_train.yaml"
$ python tools/test_net.py --config-file "configs/nce+_eval_val.yaml"
$ python tools/test_net.py --config-file "configs/nce+_eval_test-dev.yaml"

Documentation

Documentation/1dregression

  • Example usage:
$ sh start_docker_image_ebms_regression.sh
$ cd --
$ python ebms_regression/1dregression/1/nce+_train.py
  • 1dregression/1 contains all code for the first dataset, 1dregression/2 all code for the second dataset.
  • 1dregression/1/model.py: Definition of the feed-forward DNN f_\theta(x, y). Identical to 1dregression/2/model.py.
  • 1dregression/{1, 2}/datasets.py: Definition of the {first, second} dataset.
  • 1dregression/{1, 2}/{{mlis, mlmcmcL16, kldis, nce, sm, dsm, nce+}}_train.py: Train 20 models on the {first, second} dataset using {{ML-IS, ML-MCMC-16, KLD-IS, NCE, SM, DSM, NCE+}}.
  • 1dregression/{1, 2}/{{mlis, mlmcmcL16, kldis, nce, sm, dsm, nce}}_eval.py: Evaluate the KL divergence to the true p(y | x) for all 20 trained models, compute the mean for the 5 best models.
  • 1dregression/{1, 2}/{{mlis, mlmcmcL16, kldis, nce, sm, dsm, nce}}_viz.py: Visualize p(y | x; \theta) for one of the 20 trained models (example plot for NCE+).

Documentation/detection

  • Example usage:
$ sh start_docker_image_ebms_regression.sh
$ cd --
$ cd ebms_regression/detection
$ python tools/train_net.py --config-file "configs/nce+_train.yaml"
$ python tools/test_net.py --config-file "configs/nce+_eval_val.yaml"
$ python tools/test_net.py --config-file "configs/nce+_eval_test-dev.yaml"
  • detection/configs contains all config files needed to train a model using ML-IS, ML-MCMC-8, KLD-IS, NCE, DSM or NCE+. It also contains all config files needed to evaluate such a trained model on 2017 val or 2017 test-dev.
  • detection/maskrcnn_benchmark/modeling/roi_heads/iou_head/iou_head.py: Definition of the training and prediction procedures.
  • detection/maskrcnn_benchmark/modeling/roi_heads/iou_head/loss.py: Definition of the loss for all training methods.

Pretrained model

  • Evaluate pretrained model on 2017 val:
    • Download the file nce+_model_0060000.pth from above and place in detection/pretrained_models.
    • $ sh start_docker_image_ebms_regression.sh
    • $ cd --
    • $ cd ebms_regression/detection
    • $ python tools/test_net.py --config-file "configs/nce+_eval_pretrained_val.yaml"
    • Expected output:
AP, AP50, AP75, APs, APm, APl
0.3936, 0.5799, 0.4263, 0.2220, 0.4257, 0.5188
  • Evaluate pretrained model on 2017 test-dev:
    • Download the file nce+_model_0060000.pth from above and place in detection/pretrained_models.
    • $ sh start_docker_image_ebms_regression.sh
    • $ cd --
    • $ cd ebms_regression/detection
    • $ python tools/test_net.py --config-file "configs/nce+_eval_pretrained_test-dev.yaml"
    • Download the file detection/checkpoints/nce+_eval_pretrained_test-dev/inference/coco_2017_test-dev/bbox.json (105.2 MB).
    • Rename this file to detections_test-dev2017_nce+_pretrained_results.json.
    • Compress this file to create detections_test-dev2017_nce+_pretrained_results.zip.
    • Go to https://competitions.codalab.org/competitions/20794. Click "Participate". Mark "test-dev2019 (bbox)". Choose a team name. Method name: nce+_pretrained. Upload the zip file (nothing happens for 1-2 mins after you upload the zip file, but then it appears in the table).
    • Wait for the evaluation to complete on the server (click on "Refresh status" until the status is "Finished").
    • Click on "Download output from scoring step".
    • scores.txt in the downloaded output_file.zip contains the results.
    • Expected output:
AP: 0.397
AP_50: 0.587
AP_75: 0.427
AP_small: 0.221
AP_medium: 0.420
AP_large: 0.505
AR_max_1: 0.331
AR_max_10: 0.534
AR_max_100: 0.564
AR_small: 0.353
AR_medium: 0.597
AR_large: 0.717

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Official implementation of "Energy-Based Models for Deep Probabilistic Regression" (ECCV 2020) and "How to Train Your Energy-Based Model for Regression" (BMVC 2020).

www.fregu856.com

Topics

tracking machine-learning computer-vision deep-learning pytorch object-detection visual-tracking energy-based-model

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