add readme

README.md

@@ -0,0 +1,134 @@
+# Universal Deep Image Compression via Content-Adaptive Optimization with Adapters
+Official implementation of "Universal Deep Image Compression via Content-Adaptive Optimization with Adapters" presented at WACV 23.
+
+## Environment
+Use Miniconda.
+
+```bash
+# python=3.8, 3.9, or 3.10 should be fine.
+conda create -n aof python=3.7
+conda activate aof
+# install 7z for [Lam+, ACM MM 20]
+conda install -c bioconda p7zip
+pip install -r requirements.txt
+```
+
+## Preparation
+### Dataset
+Prepare a dataset that consists of four domains: natural images, line drawings, comics, and vector arts.
+
+```bash
+# In 2022/08/31, four files have missing links.
+# the four files: (`156526161`, `99117125`, `15642096`, `158633139`)
+python scripts/download_dataset.py
+```
+
+### Pre-trained Weights
+Prepare the weights of WACNN pre-trained on natural images.
+
+Note: we cannot provide the `weights.zip` due to the storage limit.
+
+```bash
+unzip weights.zip
+```
+
+## Usage
+### Training of the Proposed Method
+Apply the demo code (`main.py`) to a dataset by running a script.
+Please specify `["vector", "natural", "line", "comic"]` as a dataset and `[1, 2, 3, 4, 5, 6]` as quality.
+
+```bash
+python run_main.py vector --out results/ours --quality 1
+```
+
+The refined latent representation is encoded and saved in `cache/`.
+The parameters of the adapters are encoded and saved in `results/ours/wacnn/q1/<image file name>/weights.pt`.
+
+### Evaluation of the Proposed Method
+Perform evaluation using the compressed data obtained in training with the command below.
+Please specify `--domain` from `["vector", "natural", "line", "comic"]`. The default is `"vector"`.
+
+```bash
+# Without adaptation
+python decode.py --stage 0th # --domain vector
+# Without adapters (= only refining the latent representation = [Yang+, NeurIPS 20])
+python decode.py --stage 1st # --domain vector
+# Ours
+python decode.py --stage 2nd # --domain vector
+```
+
+You can finally obtain the results in the csv format in `results/ours/wacnn/q{1,2,3,4,5,6}/<dataset name>_{0th,1st,2nd}.csv`.
+
+### Comparison with Other Adaptive Methods
+Run other adaptive methods in our framework.
+
+```bash
+# [Lam+, ACM MM 20]
+python run_main.py vector --out results/lam-mm20 --regex "'g_s\..*\.bias'" --n-dim 0 --width 0.0 --data_type float32 --quality 1
+python decode.py --weight_root results/lam-mm20 --n-dim-2 0 --width 0.0 --regex "g_s\..*\.bias" --data_type float64+7z
+
+# [Rozendaal+, ICLR 21]
+python run_main.py vector --out results/rozendaal-iclr21 --regex "'.*'" --n-dim 0 --width 0.005 --alpha 1000 --sigma 0.05 --distrib spike-and-slab --lr 3e-5 --opt-enc --quality 1
+python decode.py --weight_root results/rozendaal-iclr21 --rozendaal
+
+# [Zou+, ISM 21]
+python run_main.py vector --out results/zou-ism21 --n-dim -1 --groups 192 --width 0.0 --quality 1
+python decode.py --weight_root results/zou-ism21 --n-dim-2 -1 --groups 192 --width 0.0
+```
+
+You can compare these methods with the baseline and proposed methods using the command below.
+
+```bash
+python plot_rdcurve.py
+```
+
+### Ablation Studies
+(Optional) Perform ablation studies.
+
+* Optimization only in terms of distortion
+```bash
+# Distortion opt.
+python run_main.py vector --out results-abl/ours-dopt --width 0.0 --quality 1
+python decode.py --weight_root results-abl/ours-dopt --width 0.0
+```
+
+* Optimization of other parameters
+
+```bash
+# Biases
+python run_main.py vector --out results-abl/ours-bias --regex "'g_s\..*\.bias'" --n-dim 0 --lr_2 1e-5 --quality 1
+python decode.py --weight_root results-abl/ours-bias --regex "'g_s\..*\.bias'" --n-dim-2 0
+# OMPs
+python run_main.py vector --out results-abl/ours-omp --distrib logistic --n-dim -1 --groups 192 --quality 1
+python decode.py --weight_root results-abl/ours-omp --n-dim-2 -1 --groups 192
+```
+
+* Optimization Order
+
+```bash
+python run_main.py vector --out results-abl/ours-swap --swap --quality 1
+python decode.py --weight_root results-abl/ours-swap --swap
+```
+
+* Another Base Network Architecture
+
+```bash
+# Cheng20
+python run_main.py vector --out results/ours --model cheng2020-attn --regex "'g_s\.[8-8]\.adapter_1.*'" --quality 1
+python decode.py --model cheng2020-attn --n-dim-1 2 --n-dim-2 0
+```
+
+## Contact
+Feel free to contact me if there is any question: tsubota (a) hal.t.u-tokyo.ac.jp
+
+## License
+This code is licensed under MIT (if not specified in the code).
+
+The code contains modified and copied open source code.
+Thus, I describe the original license of the code.
+Please let me know if there is a license issue with code redistribution.
+If so, I will remove the code and provide the instructions to reproduce the work.
+
+As for the dataset, I do not have any right and provide only URLs.
+Please refer to the original datasets (Kodak and BAM) for the detailed license of the images.
+When the link of some URLs is missing, I will redistribute the corresponding images if they are licensed under Creative Commons.