Label-Efficient Semantic Segmentation with Diffusion Models

ICLR'2022 [Project page]

Official implementation of the paper Label-Efficient Semantic Segmentation with Diffusion Models

This code is based on datasetGAN and guided-diffusion.

Note: use --recurse-submodules when clone.

Overview

The paper investigates the representations learned by the state-of-the-art DDPMs and shows that they capture high-level semantic information valuable for downstream vision tasks. We design a simple semantic segmentation approach that exploits these representations and outperforms the alternatives in the few-shot operating point.

Updates

3/9/2022:

Improved performance of DDPM-based segmentation by changing:
Diffusion steps: [50,150,250,350] --> [50,150,250];
UNet blocks: [6,7,8,9] --> [5,6,7,8,12];
Trained a bit better DDPM on FFHQ-256;
Added MAE for comparison.

Datasets

The evaluation is performed on 6 collected datasets with a few annotated images in the training set: Bedroom-18, FFHQ-34, Cat-15, Horse-21, CelebA-19 and ADE-Bedroom-30. The number corresponds to the number of semantic classes.

datasets.tar.gz (~47Mb)

DDPM

Pretrained DDPMs

The models trained on LSUN are adopted from guided-diffusion. FFHQ-256 is trained by ourselves using the same model parameters as for the LSUN models.

LSUN-Bedroom: lsun_bedroom.pt
FFHQ-256: ffhq.pt (Updated 3/8/2022)
LSUN-Cat: lsun_cat.pt
LSUN-Horse: lsun_horse.pt

Run

Download the datasets:
bash datasets/download_datasets.sh
Download the DDPM checkpoint:
bash checkpoints/ddpm/download_checkpoint.sh <checkpoint_name>
Check paths in experiments/<dataset_name>/ddpm.json
Run: bash scripts/ddpm/train_interpreter.sh <dataset_name>

Available checkpoint names: lsun_bedroom, ffhq, lsun_cat, lsun_horse
Available dataset names: bedroom_28, ffhq_34, cat_15, horse_21, celeba_19, ade_bedroom_30

Note: train_interpreter.sh is RAM consuming since it keeps all training pixel representations in memory. For ex, it requires ~210Gb for 50 training images of 256x256. (See issue)

How to improve the performance

Tune for a particular task what diffusion steps and UNet blocks to use.

DatasetDDPM

Synthetic datasets

To download DDPM-produced synthetic datasets (50000 samples, ~7Gb) (updated 3/8/2022):
bash synthetic-datasets/ddpm/download_synthetic_dataset.sh <dataset_name>

Run | Option #1

Download the synthetic dataset:
bash synthetic-datasets/ddpm/download_synthetic_dataset.sh <dataset_name>
Check paths in experiments/<dataset_name>/datasetDDPM.json
Run: bash scripts/datasetDDPM/train_deeplab.sh <dataset_name>

Run | Option #2

Download the datasets:
bash datasets/download_datasets.sh
Download the DDPM checkpoint:
bash checkpoints/ddpm/download_checkpoint.sh <checkpoint_name>
Check paths in experiments/<dataset_name>/datasetDDPM.json
Train an interpreter on a few DDPM-produced annotated samples:
bash scripts/datasetDDPM/train_interpreter.sh <dataset_name>
Generate a synthetic dataset:
   bash scripts/datasetDDPM/generate_dataset.sh <dataset_name>
    Please specify the hyperparameters in this script for the available resources.
    On 8xA100 80Gb, it takes about 12 hours to generate 10000 samples.
Run: bash scripts/datasetDDPM/train_deeplab.sh <dataset_name>
One needs to specify the path to the generated data. See comments in the script.

Available checkpoint names: lsun_bedroom, ffhq, lsun_cat, lsun_horse
Available dataset names: bedroom_28, ffhq_34, cat_15, horse_21

MAE

Pretrained MAEs

We pretrain MAE models using the official implementation on the LSUN and FFHQ-256 datasets:

LSUN-Bedroom: lsun_bedroom.pth
FFHQ-256: ffhq.pth
LSUN-Cat: lsun_cat.pth
LSUN-Horse: lsun_horse.pth

Training setups:

Dataset	Backbone	epochs	batch-size	mask-ratio
LSUN Bedroom	ViT-L-8	150	1024	0.75
LSUN Cat	ViT-L-8	200	1024	0.75
LSUN Horse	ViT-L-8	200	1024	0.75
FFHQ-256	ViT-L-8	400	1024	0.75

Run

Download the datasets:
bash datasets/download_datasets.sh
Download the MAE checkpoint:
bash checkpoints/mae/download_checkpoint.sh <checkpoint_name>
Check paths in experiments/<dataset_name>/mae.json
Run: bash scripts/mae/train_interpreter.sh <dataset_name>

Available checkpoint names: lsun_bedroom, ffhq, lsun_cat, lsun_horse
Available dataset names: bedroom_28, ffhq_34, cat_15, horse_21, celeba_19, ade_bedroom_30

SwAV

Pretrained SwAVs

We pretrain SwAV models using the official implementation on the LSUN and FFHQ-256 datasets:

LSUN-Bedroom	FFHQ-256	LSUN-Cat	LSUN-Horse
SwAV	SwAV	SwAV	SwAV
SwAVw2	SwAVw2	SwAVw2	SwAVw2

Training setups:

Dataset	Backbone	epochs	batch-size	multi-crop	num-prototypes
LSUN	RN50	200	1792	2x256 + 6x108	1000
FFHQ-256	RN50	400	2048	2x224 + 6x96	200
LSUN	RN50w2	200	1920	2x256 + 4x108	1000
FFHQ-256	RN50w2	400	2048	2x224 + 4x96	200

Run

Download the datasets:
bash datasets/download_datasets.sh
Download the SwAV checkpoint:
bash checkpoints/{swav|swav_w2}/download_checkpoint.sh <checkpoint_name>
Check paths in experiments/<dataset_name>/{swav|swav_w2}.json
Run: bash scripts/{swav|swav_w2}/train_interpreter.sh <dataset_name>

Available checkpoint names: lsun_bedroom, ffhq, lsun_cat, lsun_horse
Available dataset names: bedroom_28, ffhq_34, cat_15, horse_21, celeba_19, ade_bedroom_30

DatasetGAN

Opposed to the official implementation, more recent StyleGAN2(-ADA) models are used.

Synthetic datasets

To download GAN-produced synthetic datasets (50000 samples):

bash synthetic-datasets/gan/download_synthetic_dataset.sh <dataset_name>

Run

Since we almost fully adopt the official implementation, we don't provide our reimplementation here. However, one can still reproduce our results:

Download the synthetic dataset:
bash synthetic-datasets/gan/download_synthetic_dataset.sh <dataset_name>
Change paths in experiments/<dataset_name>/datasetDDPM.json
Change paths and run: bash scripts/datasetDDPM/train_deeplab.sh <dataset_name>

Available dataset names: bedroom_28, ffhq_34, cat_15, horse_21

Results

Performance in terms of mean IoU:

Method	Bedroom-28	FFHQ-34	Cat-15	Horse-21	CelebA-19	ADE-Bedroom-30
ALAE	20.0 ± 1.0	48.1 ± 1.3	--	--	49.7 ± 0.7	15.0 ± 0.5
VDVAE	--	57.3 ± 1.1	--	--	54.1 ± 1.0	--
GAN Inversion	13.9 ± 0.6	51.7 ± 0.8	21.4 ± 1.7	17.7 ± 0.4	51.5 ± 2.3	11.1 ± 0.2
GAN Encoder	22.4 ± 1.6	53.9 ± 1.3	32.0 ± 1.8	26.7 ± 0.7	53.9 ± 0.8	15.7 ± 0.3
SwAV	41.0 ± 2.3	54.7 ± 1.4	44.1 ± 2.1	51.7 ± 0.5	53.2 ± 1.0	30.3 ± 1.5
SwAVw2	42.4 ± 1.7	56.9 ± 1.3	45.1 ± 2.1	54.0 ± 0.9	52.4 ± 1.3	30.6 ± 1.0
MAE	45.0 ± 2.0	58.8 ± 1.1	52.4 ± 2.3	63.4 ± 1.4	57.8 ± 0.4	31.7 ± 1.8
DatasetGAN	31.3 ± 2.7	57.0 ± 1.0	36.5 ± 2.3	45.4 ± 1.4	--	--
DatasetDDPM	47.9 ± 2.9	56.0 ± 0.9	47.6 ± 1.5	60.8 ± 1.0	--	--
DDPM	49.4 ± 1.9	59.1 ± 1.4	53.7 ± 3.3	65.0 ± 0.8	59.9 ± 1.0	34.6 ± 1.7

Examples of segmentation masks predicted by the DDPM-based method:

Cite

@misc{baranchuk2021labelefficient,
      title={Label-Efficient Semantic Segmentation with Diffusion Models}, 
      author={Dmitry Baranchuk and Ivan Rubachev and Andrey Voynov and Valentin Khrulkov and Artem Babenko},
      year={2021},
      eprint={2112.03126},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Selich/ddpm-segmentation

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