This repository contains the implementation for the paper:
Yawar Siddiqui, Julien Valentin and Matthias Niessner, "ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation" (video)
train_active.py [-h] [--backbone {resnet,xception,drn,mobilenet}]
[--out-stride OUT_STRIDE]
[--dataset {scannet,scenenet-rgbd,matterport3d,scannet-sample}]
[--workers N] [--base-size BASE_SIZE]
[--sync-bn SYNC_BN] [--loss-type {ce,focal}]
[--epochs N] [--batch-size N] [--use-balanced-weights]
[--lr LR] [--lr-scheduler {step}]
[--optimizer {SGD,Adam}] [--step-size STEP_SIZE]
[--use-lr-scheduler] [--momentum M] [--weight-decay M]
[--nesterov] [--gpu-ids GPU_IDS] [--seed S]
[--checkname CHECKNAME] [--eval-interval EVAL_INTERVAL]
[--memory-hog]
[--max-iterations MAX_ITERATIONS]
[--active-selection-size ACTIVE_SELECTION_SIZE]
[--region-size REGION_SIZE]
[--region-selection-mode REGION_SELECTION_MODE]
[--view-entropy-mode {soft,vote,mc_dropout}]
[--active-selection-mode {random,viewentropy_region,voteentropy_soft,voteentropy_region,softmax_entropy,softmax_confidence,softmax_margin,coreset,voteentropy_max_repr,viewmc_kldiv_region,ceal}]
[--superpixel-dir SUPERPIXEL_DIR]
[--superpixel-coverage-dir SUPERPIXEL_COVERAGE_DIR]
[--superpixel-overlap SUPERPIXEL_OVERLAP]
[--start-entropy-threshold START_ENTROPY_THRESHOLD]
[--entropy-change-per-selection ENTROPY_CHANGE_PER_SELECTION]
Run --help for more details.
| Option | Method |
|---|---|
| random | RAND selection |
| voteentropy_max_repr | MAXRPR selection |
| voteentropy_soft | MCDR selection |
| voteentropy_region | RMCDR selection |
| softmax_entropy | ENT selection |
| softmax_confidence | CONF selection |
| softmax_margin | MAR selection |
| coreset | CSET selection |
| ceal | CEAL selection |
| viewmc_kldiv_region | ViewAL selection |
For a description of the methods, check out appendix section of the paper.
# sample dataset
python train_active.py --dataset scenenet-rgbd --workers 2 --epochs 50 --eval-interval 5 --batch-size=6 --lr 0.0004 --use-lr-scheduler --lr-scheduler step --step-size 35 --checkname regional_viewmckldiv_spx_1_7x2_lr-0.0004_bs-6_ep-60_wb-0_lrs-1_240x320 --base-size 240,320 --max-iterations 7 --active-selection-size 2 --active-selection-mode viewmc_kldiv_region --region-selection-mode superpixel
# scenenet-rgbd
python train_active.py --dataset scenenet-rgbd --workers 2 --epochs 50 --eval-interval 5 --batch-size=6 --lr 0.0004 --use-lr-scheduler --lr-scheduler step --step-size 35 --checkname regional_viewmckldiv_spx_1_7x1500_lr-0.0004_bs-6_ep-60_wb-0_lrs-1_240x320 --base-size 240,320 --max-iterations 7 --active-selection-size 1500 --active-selection-mode viewmc_kldiv_region --region-selection-mode superpixelpython train_active.py --dataset scenenet-rgbd --workers 2 --epochs 50 --eval-interval 5 --batch-size=6 --lr 0.0004 --use-lr-scheduler --lr-scheduler step --step-size 35 --checkname random_0_7x1500_lr-0.0004_bs-6_ep-60_wb-0_lrs-0_240x320 --base-size 240,320 --max-iterations 7 --active-selection-size 1500 --active-selection-mode randompython train_active.py --dataset scenenet-rgbd --workers 2 --epochs 50 --eval-interval 5 --batch-size=6 --lr 0.0004 --use-lr-scheduler --lr-scheduler step --step-size 35 --checkname softmax_entropy_0_7x1500_lr-0.0004_bs-6_ep-50_wb-0_lrs-1_240x320 --base-size 240,320 --max-iterations 7 --active-selection-size 1500 --active-selection-mode softmax_entropypython train_active.py --dataset scenenet-rgbd --workers 2 --epochs 50 --eval-interval 5 --batch-size=6 --lr 0.0004 --use-lr-scheduler --lr-scheduler step --step-size 35 --checkname regional_voteentropy_window_0_7x1500_lr-0.0004_bs-6_ep-60_wb-0_lrs-1_240x320 --base-size 240,320 --max-iterations 7 --active-selection-size 1500 --active-selection-mode voteentropy_region --region-selection-mode windowpython train_active.py --dataset scenenet-rgbd --workers 2 --epochs 50 --eval-interval 5 --batch-size=6 --lr 0.0004 --use-lr-scheduler --lr-scheduler step --step-size 35 --checkname ceal-0.00275_7x1500_lr-0.0005_bs-6_ep-50_wb-0_lrs-1_240x320 --max-iterations 7 --active-selection-size 1500 --base-size 240,320 --active-selection-mode ceal --start-entropy-threshold 0.0275 --entropy-change-per-selection 0.001815python train_active.py --dataset scenenet-rgbd --workers 2 --epochs 50 --eval-interval 5 --batch-size=6 --lr 0.0004 --use-lr-scheduler --lr-scheduler step --step-size 35 --checkname mcdropoutentropy_0_7x1500_lr-0.0004_bs-6_ep-50_wb-0_lrs-1_240x320 --base-size 240,320 --max-iterations 7 --active-selection-size 1500 --active-selection-mode voteentropy_softpython train.py --dataset scenenet-rgbd --workers 2 --epochs 70 --eval-interval 5 --batch-size=6 --lr 0.0004 --use-lr-scheduler --lr-scheduler step --step-size 40 --checkname full-run_0_lr-0.0004_bs-6_ep-60_wb-0_lrs-0_240x320 --base-size 240,320Overall code structure is as follows:
| File / Folder | Description |
|---|---|
| train_active.py | Training script for active learning methods |
| train.py | Training script for full dataset training |
| constants.py | Constants used across the code |
| argument_parser.py | Arguments parsing code |
| active_selection | Implementation of our method and other active learning methods for semantic segmentation |
| dataloader | Dataset classes |
| dataset | Train/test splits and raw files of datasets |
| model | DeeplabV3+ implementation (inspired from here) |
| utils | Misc utils |
The datasets must follow the following structure
dataset # root dataset directory
├── dataset-name
├── raw
├── selections
├── color # rgb frames
├── label # ground truth maps
├── depth # depth maps
├── pose # camera extrinsics for each frame
├── info # camera intrinsics
├── superpixel # superpixel maps
├── coverage_superpixel # coverage maps
├── selections
├── seedset_0_frames.txt # seed set
├── train_frames.txt
├── val_frames.txt
├── test_frames.txt
├── dataset.lmdb # rgb frames + labels in lmdb format
A small example dataset is provided with this repository in dataset/scannet-sample.
To use this repository datasets must be in the structure described in last section. For creating the lmdb database, seed set, train / test splits and superpixel maps check helper scripts in dataset/preprocessing-scripts. We use this SEEDS implementation for generating superpixels. Further, to generate superpixel coverage maps (coverage_superpixel) check utils/superpixel_projections.py.
If you use this code, please cite the paper: