diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..f57b79bbe14689eefe315c0c60854c446c4eee56 --- /dev/null +++ b/.gitignore @@ -0,0 +1,86 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.egg-info/ +.eggs/ +dist/ +build/ +*.so +.Python +.venv/ +venv/ +env/ +.env +.env.* + +# IDE / OS +.idea/ +.vscode/ +*.swp +*.swo +.DS_Store +Thumbs.db + +# --------------------------------------------------------------------------- +# Upstream change-detection model repositories (clone separately) +# --------------------------------------------------------------------------- +BiFA/ +BIT_CD/ +CDMamba/ +Change3D/ +ChangeFormer/ +IFNet/ +RSM-CD/ +SChanger/ +Siam-NestedUNet/ +STANet/ +model_repos/ + +# Generated dataset layout copies (symlinks/copies of external DATA_ROOT) +generated_dataset_views/ + +# --------------------------------------------------------------------------- +# Model weights, checkpoints, and training artifacts +# --------------------------------------------------------------------------- +*.pth +*.pt +*.ckpt +*.safetensors +*.onnx +*.pb +*.h5 +*.pkl +*.pickle +*.bin +*.tar +*.tar.gz +*.zip + +checkpoints/ +checkpoint/ +experiments/ +exp/ +pretrain/ +pretrained/ +pretrained_weight/ +pretrained_weights/ +weights/ +saved_models/ +saved_model/ +models/*.pth +models/*.pt +runs/ +wandb/ +logs/ +vis/ +tmp/ +output/ +outputs/ +samples/ +flagged/ +gradio_cached_examples/ + +# Large local datasets (point DATA_ROOT outside this repo) +data/ +datasets/raw/ diff --git a/README.md b/README.md index 32897cd3e640101ba184f8c4ccd896981de3804a..5d113ca1193141cc861889a8be6887f3589f896c 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,1571 @@ --- -license: mit +license: other +library_name: pytorch +tags: + - change-detection + - remote-sensing + - pytorch --- + +# CD-Models: A Unified Change Detection Benchmark Suite + +> **A multi-dataset, multi-model benchmark for binary remote sensing change detection.** +> Covers 21 registered models spanning CNN, Transformer, and Mamba architectures, evaluated across the dataset configs in this repository with a shared training pipeline. + +![Python](https://img.shields.io/badge/python-3.10%20recommended-blue) +![PyTorch](https://img.shields.io/badge/PyTorch-2.6.0%2Bcu124-ee4c2c) +![Models](https://img.shields.io/badge/models-21-0f766e) +![Datasets](https://img.shields.io/badge/datasets-8-7c3aed) +![License](https://img.shields.io/badge/license-see%20upstream-lightgrey) + +## Overview + +CD-Models is a benchmark harness for binary remote sensing change detection. It brings original research repositories and newly added model wrappers under one launcher, one dataset-config convention, one weight-management layer, and one result aggregation layout. The repository exists to make model comparisons reproducible across the same prepared dataset folders instead of relying on isolated, model-specific scripts. It also keeps the limits visible: result tables are populated only from `results/*/*/metrics_test.json`, and framework-specific models remain marked until their required stacks are installed. + +## Supported Models + +The model list below is taken from `configs/models/registry.yaml`, which is what `run_training.py` uses for `--model` choices. + +| Model | Paper | Venue | Backbone | Params | Training Script | Status | +|-------|-------|-------|----------|--------|-----------------|--------| +| `bifa` | BiFA: Remote Sensing Image Change Detection with Bitemporal Feature Alignment | IEEE TGRS 2024 | MixTransformer | — | `train/train_bifa.py` | 🔄 In Progress | +| `bit_cd` | Remote Sensing Image Change Detection with Transformers | IEEE TGRS 2021 | ResNet-18 + base transformer | ~11M† | `train/train_bit_cd.py` | ✅ Working | +| `cdmamba` | CDMamba: Incorporating Local Clues into Mamba for Binary Change Detection | IEEE TGRS 2025 | Mamba | — | `train/train_cdmamba.py` | 🔄 In Progress | +| `cgnet` | Change Guiding Network | IEEE JSTARS 2023 | CGNet custom backbone | — | `train/train_cgnet.py` | ✅ Working | +| `change3d` | Change3D: Revisiting Change Detection and Captioning from a Video Modeling Perspective | CVPR 2025 Highlight | X3D-L | — | `train/train_change3d.py` | ✅ Working | +| `changeformer` | A Transformer-Based Siamese Network for Change Detection | IGARSS 2022 | MiT-b4 | ~41M† | `train/train_changeformer.py` | ✅ Working | +| `changemamba` | ChangeMamba: Remote Sensing Change Detection with Spatio-Temporal State Space Model | IEEE TGRS 2024 | VMamba | — | `train/train_changemamba.py` | ⚠️ Framework Required | +| `changer` | Changer: Feature Interaction is What You Need for Change Detection | IEEE TGRS / Open-CD | ResNet-18 | — | `train/train_changer.py` | ⚠️ Framework Required | +| `dsamnet` | Deeply-supervised Attention Metric-based Network | IEEE TGRS 2021 | ResNet | — | `train/train_dsamnet.py` | ✅ Working | +| `dsifn` | Deeply Supervised Image Fusion Network | ISPRS JPRS 2020 | VGG-16 | — | `train/train_dsifn.py` | 🔄 In Progress | +| `elgcnet` | ELGC-Net: Efficient Local-Global Context Aggregation | IEEE TGRS 2024 | ResNet-18 / ELGCA | — | `train/train_elgcnet.py` | ✅ Working | +| `fc_ef` | Fully Convolutional Early Fusion | ICIP 2018 | FCN | — | `train/train_fc_variants.py` | ✅ Working | +| `fc_siam_conc` | Fully Convolutional Siamese Concatenation | ICIP 2018 | Siamese FCN | — | `train/train_fc_variants.py` | ✅ Working | +| `fc_siam_diff` | Fully Convolutional Siamese Difference | ICIP 2018 | Siamese FCN | — | `train/train_fc_variants.py` | ✅ Working | +| `hanet` | HANet: Hierarchical Attention Network | IEEE JSTARS 2023 | HANet custom backbone | — | `train/train_hanet.py` | ✅ Working | +| `ifnet` | Deeply Supervised Image Fusion Network | ISPRS JPRS 2020 | VGG-16 | — | `train/train_ifnet.py` | 🔄 In Progress | +| `rsm_cd` | RS-Mamba for Large Remote Sensing Image Dense Prediction | arXiv 2024 | VMamba/RSM-CD tiny | — | `train/train_rsm_cd.py` | ❌ Failed | +| `schanger` | SChanger: Semantic Change and Spatial Consistency Perspective | IEEE JSTARS 2025 | SChanger-base | — | `train/train_schanger.py` | 🔄 In Progress | +| `siam_nestedunet` | SNUNet-CD / Siamese NestedUNet | IEEE GRSL 2021 | UNet++ | — | `train/train_siam_nestedunet.py` | 🔄 In Progress | +| `stanet` | Spatial-Temporal Attention Network | Remote Sensing 2020 | ResNet-18 + PAM | ~17M† | `train/train_stanet.py` | ✅ Working | +| `tinycd` | TinyCD: A Not So Deep Learning Model for Change Detection | Neural Computing and Applications 2023 | EfficientNet-B4 | — | `train/train_tinycd.py` | ✅ Working | + +† Parameter counts are inherited from the previous project README or upstream publications, not measured by the current harness. + +## Supported Datasets + +Every dataset below is a real YAML file under `configs/datasets/`. The harness expands `${DATA_ROOT}` through `utils/config_loader.py`; if `${DATA_ROOT}` is unset, it first checks `/new-home/buddhiw/Datasets` and then the sibling `mamba-cd/datasets` fallback. + +### LEVIR-CD+ + +| Property | Value | +|---|---| +| Task | Binary building change detection | +| Source | [LEVIR-CD project page](https://justchenhao.github.io/LEVIR/) | +| Train / Val / Test | Determined by files under `$DATA_ROOT/LEVIR-CD-plus-256/{train,val,test}` | +| Image size | 256 x 256 | +| Resolution | Original LEVIR-CD is 0.5 m/pixel; this config uses prepared 256 patches | +| Channels | RGB | +| Label convention | Thresholded binary mask; `Mask/` folder, changed pixels above `127` | +| Mean / Std (A) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Mean / Std (B) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Config | `configs/datasets/levir_cd.yaml` | +| Root pattern | `${DATA_ROOT}/LEVIR-CD-plus-256` | + +### LEVIR-CD+ Test-as-Val + +| Property | Value | +|---|---| +| Task | Binary building change detection with literature-style test-as-validation protocol | +| Source | [LEVIR-CD project page](https://justchenhao.github.io/LEVIR/) | +| Train / Val / Test | Determined by files under `$DATA_ROOT/LEVIR-CD-plus-256-test-as-val/{train,val,test}` | +| Image size | 256 x 256 | +| Channels | RGB | +| Label convention | Thresholded binary mask; `Mask/` folder, changed pixels above `127` | +| Mean / Std (A) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Mean / Std (B) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Config | `configs/datasets/levir_cd_test_as_val.yaml` | +| Root pattern | `${DATA_ROOT}/LEVIR-CD-plus-256-test-as-val` | + +### WHU-CD + +| Property | Value | +|---|---| +| Task | Binary building change detection | +| Source | [WHU building dataset page](https://study.rsgis.whu.edu.cn/pages/download/building_dataset.html) | +| Train / Val / Test | Determined by files under `$DATA_ROOT/WHU-CD/{train,val,test}` | +| Image size | 256 x 256 | +| Channels | RGB | +| Label convention | Thresholded binary mask; `OUT/` folder, changed pixels above `127` | +| Mean / Std (A) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Mean / Std (B) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Config | `configs/datasets/whu_cd.yaml` | +| Root pattern | `${DATA_ROOT}/WHU-CD` | + +### DSIFN-CD + +| Property | Value | +|---|---| +| Task | Binary high-resolution change detection | +| Source | [DSIFN dataset reference](https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images/tree/master/dataset) | +| Train / Val / Test | Determined by files under `$DATA_ROOT/DSIFN-CD/DSIFN/{train,val,test}` | +| Image size | 256 x 256 | +| Channels | RGB | +| Label convention | `mask/` folder; raw masks may be `0/1`; generated model views convert to `0/255` where required | +| Mean / Std (A) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Mean / Std (B) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Config | `configs/datasets/dsifn_cd.yaml` | +| Root pattern | `${DATA_ROOT}/DSIFN-CD/DSIFN` | + +### WildFire-S2 + +| Property | Value | +|---|---| +| Task | Binary burned-area change detection from bi-temporal Sentinel-2 style imagery | +| Source | Local project dataset card in sibling `WildFire-S2/` | +| Train / Val / Test | Determined by files under `$DATA_ROOT/WildFireS2/{train,val,test}` | +| Image size | 256 x 256 | +| Channels | RGB | +| Label convention | Thresholded binary mask; `label/` folder, changed pixels above `127` | +| Mean / Std (A) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Mean / Std (B) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Config | `configs/datasets/wildfire_s2.yaml` | +| Root pattern | `${DATA_ROOT}/WildFireS2` | + +### SYSU-CD + +| Property | Value | +|---|---| +| Task | Binary change detection | +| Source | [SYSU-CD project page](https://github.com/liumency/SYSU-CD) | +| Train / Val / Test | Determined by files under `$DATA_ROOT/SYSU-CD-folders/{train,val,test}` | +| Image size | 256 x 256 | +| Channels | RGB | +| Label convention | Thresholded binary mask; `Mask/` folder, changed pixels above `127` | +| Mean / Std (A) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Mean / Std (B) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Config | `configs/datasets/sysu_cd.yaml` | +| Root pattern | `${DATA_ROOT}/SYSU-CD-folders` | + +### KATE-CD-256 + +| Property | Value | +|---|---| +| Task | Binary change detection | +| Source | Local prepared dataset config | +| Train / Val / Test | Determined by files under `$DATA_ROOT/KATE-CD-256/{train,val,test}` | +| Image size | 256 x 256 | +| Channels | RGB | +| Label convention | Thresholded binary mask; `label/` folder, changed pixels above `127` | +| Mean / Std (A) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Mean / Std (B) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Config | `configs/datasets/kate_cd.yaml` | +| Root pattern | `${DATA_ROOT}/KATE-CD-256` | + +### Custom-CD + +| Property | Value | +|---|---| +| Task | User-supplied binary change detection | +| Source | Local template config | +| Train / Val / Test | Determined by files under `$DATA_ROOT/Custom-CD/{train,val,test}` | +| Image size | 256 x 256 | +| Channels | RGB | +| Label convention | Thresholded binary mask; `Mask/` folder, changed pixels above `127` | +| Mean / Std (A) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Mean / Std (B) | `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]` | +| Config | `configs/datasets/custom_cd.yaml` | +| Root pattern | `${DATA_ROOT}/Custom-CD` | + +## Results and released checkpoints + +Only completed `metrics_test.json` evaluations are eligible. Rankings are computed per dataset by test-set F1 (descending), and only the rank-1 checkpoint for each dataset is released. Validation-only, incomplete, and checkpoint-only runs are excluded. + +All accuracy values are fractions. GPU memory is the PyTorch peak reserved memory for inference, with peak allocated memory used only when reserved memory is absent. FPS is model-only throughput. A dash means the evaluator did not record that metric; values are never estimated. + +The current evaluator records BF1 but not boundary mean IoU (BmIoU), so BmIoU remains explicitly unavailable rather than being inferred from BF1. Boundary IoU and boundary F1 are distinct measures. + +### Released checkpoint summary + +| Dataset | Selected model | Test F1 | Download | Size | +| --- | --- | --- | --- | --- | +| DSIFN-CD | BIT-CD | 0.6732 | [best_model.pth](https://huggingface.co/dineth18/CD-Models/resolve/main/results/bit_cd/dsifn_cd/checkpoints/best_model.pth?download=true) | 60.8 MiB | +| LEVIR-CD+ (test-as-val protocol) | CGNet | 0.8138 | [best_model.pth](https://huggingface.co/dineth18/CD-Models/resolve/main/results/cgnet/levir_cd_test_as_val/checkpoints/best_model.pth?download=true) | 405.9 MiB | +| SYSU-CD | ChangeMamba | 0.8216 | [best_model.pth](https://huggingface.co/dineth18/CD-Models/resolve/main/results/changemamba/sysu_cd/checkpoints/best_model.pth?download=true) | 619.3 MiB | +| WHU-CD | ChangeMamba | 0.9521 | [best_model.pth](https://huggingface.co/dineth18/CD-Models/resolve/main/results/changemamba/whu_cd/checkpoints/best_model.pth?download=true) | 619.0 MiB | + +### DSIFN-CD + +| Rank | Model | F1 | mIoU | Overall accuracy | Recall | Precision | BF1 | BmIoU | GFLOPs | GPU (GB) | FPS | Parameters (M) | Checkpoint | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | +| 1 | BIT-CD | 0.6732 | 0.6929 | 0.8919 | 0.6552 | 0.6923 | 0.2819 | — | 10.63 | 2.24 | 127.76 | 12.40 | [Download](https://huggingface.co/dineth18/CD-Models/resolve/main/results/bit_cd/dsifn_cd/checkpoints/best_model.pth?download=true) | +| 2 | SChanger | 0.6629 | 0.6835 | 0.8857 | 0.6612 | 0.6647 | 0.2847 | — | — | 2.45 | 24.05 | — | — | +| 3 | ChangeMamba | 0.6435 | 0.6729 | 0.8849 | 0.6113 | 0.6792 | 0.1903 | — | 28.70 | 1.93 | 15.42 | 54.00 | — | +| 4 | CGNet | 0.6204 | 0.6290 | 0.8343 | 0.7970 | 0.5079 | 0.2903 | — | 82.23 | 6.52 | 65.39 | 38.99 | — | +| 5 | FC-EF | 0.6078 | 0.6400 | 0.8604 | 0.6365 | 0.5816 | 0.1955 | — | 3.58 | 0.74 | 204.13 | 1.35 | — | +| 6 | ChangeFormer | 0.5964 | 0.6152 | 0.8299 | 0.7397 | 0.4996 | 0.2422 | — | 21.18 | 4.07 | 49.74 | 29.75 | — | +| 7 | BiFA | 0.5876 | 0.6111 | 0.8296 | 0.7142 | 0.4991 | 0.2692 | — | 53.00 | 15.10 | 13.81 | 9.87 | — | +| 8 | Siam-NestedUNet | 0.5839 | 0.6024 | 0.8188 | 0.7482 | 0.4788 | 0.2374 | — | 54.83 | 7.34 | 29.18 | 12.03 | — | +| 9 | FC-Siam-diff | 0.5829 | 0.6277 | 0.8593 | 0.5784 | 0.5875 | 0.1691 | — | 4.73 | 1.06 | 113.98 | 1.35 | — | +| 10 | STANet | 0.5483 | 0.6258 | 0.8829 | 0.4184 | 0.7953 | 0.1613 | — | 13.16 | 14.11 | 133.96 | 16.93 | — | +| 11 | FC-Siam-conc | 0.5479 | 0.5665 | 0.7873 | 0.7583 | 0.4288 | 0.1811 | — | 5.33 | 1.02 | 127.67 | 1.55 | — | +| 12 | DSAMNet | 0.5467 | 0.5757 | 0.8020 | 0.7027 | 0.4474 | 0.2374 | — | 75.39 | 2.91 | 166.43 | 16.95 | — | +| 13 | HANet | 0.5347 | 0.5817 | 0.8193 | 0.6109 | 0.4754 | 0.2418 | — | 17.67 | 11.36 | 42.36 | 3.03 | — | +| 14 | IFNet | 0.4579 | 0.5102 | 0.7524 | 0.6153 | 0.3647 | 0.1325 | — | 82.26 | 6.08 | 104.88 | 50.71 | — | + +### LEVIR-CD+ (test-as-val protocol) + +| Rank | Model | F1 | mIoU | Overall accuracy | Recall | Precision | BF1 | BmIoU | GFLOPs | GPU (GB) | FPS | Parameters (M) | Checkpoint | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | +| 1 | CGNet | 0.8138 | 0.8356 | 0.9856 | 0.7746 | 0.8572 | 0.7651 | — | 82.23 | 6.01 | 44.01 | 38.99 | [Download](https://huggingface.co/dineth18/CD-Models/resolve/main/results/cgnet/levir_cd_test_as_val/checkpoints/best_model.pth?download=true) | +| 2 | Siam-NestedUNet | 0.8046 | 0.8282 | 0.9839 | 0.8129 | 0.7965 | 0.7386 | — | — | 2.67 | 108.71 | — | — | +| 3 | SChanger | 0.8007 | 0.8253 | 0.9836 | 0.8112 | 0.7906 | 0.7364 | — | — | 2.45 | 48.96 | — | — | +| 4 | BIT-CD | 0.7996 | 0.8247 | 0.9838 | 0.7946 | 0.8047 | 0.7143 | — | 10.63 | 2.27 | 236.60 | 12.40 | — | +| 5 | HANet | 0.7841 | 0.8131 | 0.9819 | 0.8064 | 0.7630 | 0.7043 | — | — | 5.04 | 84.44 | — | — | +| 6 | BiFA | 0.7772 | 0.8084 | 0.9818 | 0.7785 | 0.7760 | 0.6581 | — | 53.00 | 15.14 | 30.38 | 9.87 | — | +| 7 | FC-Siam-conc | 0.7650 | 0.7999 | 0.9809 | 0.7623 | 0.7678 | 0.7175 | — | 5.33 | 1.02 | 308.81 | 1.55 | — | +| 8 | STANet | 0.7544 | 0.7921 | 0.9791 | 0.7865 | 0.7249 | 0.6388 | — | — | 4.45 | 174.00 | — | — | +| 9 | ChangeFormer | 0.7488 | 0.7887 | 0.9795 | 0.7505 | 0.7472 | 0.6525 | — | 21.18 | 4.03 | 157.64 | 29.75 | — | +| 10 | DSAMNet | 0.7444 | 0.7850 | 0.9779 | 0.7897 | 0.7040 | 0.5370 | — | 75.39 | 2.90 | 37.84 | 16.95 | — | +| 11 | IFNet | 0.7410 | 0.7833 | 0.9786 | 0.7510 | 0.7313 | 0.3489 | — | 82.26 | 6.08 | 105.44 | 50.71 | — | +| 12 | FC-EF | 0.7319 | 0.7766 | 0.9768 | 0.7785 | 0.6905 | 0.6497 | — | 3.58 | 0.74 | 457.44 | 1.35 | — | +| 13 | FC-Siam-diff | 0.7196 | 0.7667 | 0.9725 | 0.8650 | 0.6160 | 0.5447 | — | 4.73 | 1.06 | 262.80 | 1.35 | — | + +### SYSU-CD + +| Rank | Model | F1 | mIoU | Overall accuracy | Recall | Precision | BF1 | BmIoU | GFLOPs | GPU (GB) | FPS | Parameters (M) | Checkpoint | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | +| 1 | ChangeMamba | 0.8216 | 0.8006 | 0.9214 | 0.7677 | 0.8836 | 0.3197 | — | 28.70 | 1.93 | 77.65 | 54.00 | [Download](https://huggingface.co/dineth18/CD-Models/resolve/main/results/changemamba/sysu_cd/checkpoints/best_model.pth?download=true) | +| 2 | FC-Siam-conc | 0.7778 | 0.7558 | 0.8975 | 0.7609 | 0.7955 | 0.1939 | — | 5.33 | 1.02 | 356.94 | 1.55 | — | +| 3 | STANet | 0.7728 | 0.7482 | 0.8913 | 0.7841 | 0.7619 | 0.1962 | — | — | 4.45 | 176.02 | — | — | +| 4 | FC-EF | 0.7710 | 0.7489 | 0.8936 | 0.7596 | 0.7828 | 0.1857 | — | 3.58 | 0.73 | 553.75 | 1.35 | — | +| 5 | ChangeFormer | 0.7635 | 0.7382 | 0.8851 | 0.7863 | 0.7420 | 0.1591 | — | 21.18 | 4.03 | 143.73 | 29.75 | — | +| 6 | FC-Siam-diff | 0.7630 | 0.7457 | 0.8956 | 0.7129 | 0.8208 | 0.1853 | — | 4.73 | 1.08 | 552.90 | 1.35 | — | +| 7 | BIT-CD | 0.7618 | 0.7395 | 0.8881 | 0.7584 | 0.7652 | 0.1780 | — | 10.63 | 2.24 | 111.11 | 12.40 | — | +| 8 | IFNet | 0.7145 | 0.7020 | 0.8724 | 0.6772 | 0.7562 | 0.1158 | — | — | 3.29 | 109.80 | — | — | + +### WHU-CD + +| Rank | Model | F1 | mIoU | Overall accuracy | Recall | Precision | BF1 | BmIoU | GFLOPs | GPU (GB) | FPS | Parameters (M) | Checkpoint | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | +| 1 | ChangeMamba | 0.9521 | 0.9519 | 0.9954 | 0.9401 | 0.9644 | 0.8605 | — | 28.70 | 1.93 | 75.28 | 54.00 | [Download](https://huggingface.co/dineth18/CD-Models/resolve/main/results/changemamba/whu_cd/checkpoints/best_model.pth?download=true) | +| 2 | CGNet | 0.9331 | 0.9340 | 0.9936 | 0.9299 | 0.9364 | 0.8138 | — | 82.23 | 5.62 | 129.54 | 38.99 | — | +| 3 | BIT-CD | 0.8940 | 0.8989 | 0.9899 | 0.8845 | 0.9037 | 0.6879 | — | 10.63 | 2.24 | 46.28 | 12.40 | — | +| 4 | DSAMNet | 0.8905 | 0.8956 | 0.9891 | 0.9167 | 0.8657 | 0.5855 | — | 75.39 | 2.90 | 116.58 | 16.95 | — | +| 5 | IFNet | 0.8389 | 0.8534 | 0.9849 | 0.8147 | 0.8644 | 0.3245 | — | 82.26 | 6.08 | 104.98 | 50.71 | — | +| 6 | FC-EF | 0.8385 | 0.8531 | 0.9850 | 0.8067 | 0.8729 | 0.5818 | — | 3.58 | 0.74 | 498.55 | 1.35 | — | +| 7 | FC-Siam-conc | 0.7789 | 0.8062 | 0.9756 | 0.8907 | 0.6920 | 0.4733 | — | 5.33 | 1.03 | 397.86 | 1.55 | — | +| 8 | FC-Siam-diff | 0.7135 | 0.7580 | 0.9631 | 0.9540 | 0.5699 | 0.2148 | — | 4.73 | 1.09 | 567.87 | 1.35 | — | + +### Datasets without a released checkpoint + +WildFire-S2, KATE-CD-256, the standard LEVIR-CD+ configuration, and Custom-CD currently have no eligible completed test result. Their validation-only or incomplete checkpoints are intentionally not uploaded. + + +## Installation + +### 1. Clone this repository + +```bash +git clone https://github.com//CD-Models.git +cd CD-Models +``` + +### 2. Create and activate environment + +```bash +conda create -n cd-models python=3.10 -y +conda activate cd-models +``` + +### 3. Install PyTorch (CUDA 12.4) + +```bash +pip install torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu124 +``` + +Adjust the CUDA version for your system. See [PyTorch Get Started](https://pytorch.org/get-started/locally/). + +### 4. Run automatic setup + +```bash +python setup.py +``` + +This single command: + +- Checks all required Python packages. +- Auto-installs safe pure-Python/common packages when missing. +- Clones confirmed model repositories into `model_repos/`. +- Downloads or warms pretrained backbone weights. +- Generates dataset list files for datasets already present on disk. +- Prints a full status report. + +### 5. ChangeMamba / Changer MMSeg stack + +These two models require OpenMMLab packages that should be installed after PyTorch: + +```bash +pip install mmengine==0.10.1 +pip install mmcv==2.1.0 -f https://download.openmmlab.com/mmcv/dist/cu124/torch2.6/index.html +pip install mmsegmentation==1.2.2 mmdet==3.3.0 mmpretrain==1.2.0 +``` + +### 6. Set your dataset root + +```bash +export DATA_ROOT=/path/to/your/datasets +``` + +Add the export to `~/.bashrc` or your scheduler job script if you want it to persist. + +## Dataset Preparation + +All datasets follow the same high-level split structure: `train/`, `val/`, and `test/`, each with a pre-change image folder, a post-change image folder, and a mask folder. Folder names differ by dataset and are encoded in YAML. + +### LEVIR-CD+ + +**Download:** [Official LEVIR page](https://justchenhao.github.io/LEVIR/) + +Expected structure: + +```text +$DATA_ROOT/LEVIR-CD-plus-256/ +├── train/ +│ ├── A/ +│ ├── B/ +│ └── Mask/ +├── val/ +│ ├── A/ +│ ├── B/ +│ └── Mask/ +└── test/ + ├── A/ + ├── B/ + └── Mask/ +``` + +Prepare list files: + +```bash +python setup.py --dataset levir_cd +``` + +Config: `configs/datasets/levir_cd.yaml` + +### LEVIR-CD+ Test-as-Val + +**Download:** Use the same source data as LEVIR-CD+ and prepare the test-as-validation split protocol locally. + +Expected structure: + +```text +$DATA_ROOT/LEVIR-CD-plus-256-test-as-val/ +├── train/ +│ ├── A/ +│ ├── B/ +│ └── Mask/ +├── val/ +│ ├── A/ +│ ├── B/ +│ └── Mask/ +└── test/ + ├── A/ + ├── B/ + └── Mask/ +``` + +Prepare list files: + +```bash +python setup.py --dataset levir_cd_test_as_val +``` + +Config: `configs/datasets/levir_cd_test_as_val.yaml` + +### WHU-CD + +**Download:** [WHU building dataset page](https://study.rsgis.whu.edu.cn/pages/download/building_dataset.html) + +Expected structure: + +```text +$DATA_ROOT/WHU-CD/ +├── train/ +│ ├── A/ +│ ├── B/ +│ └── OUT/ +├── val/ +│ ├── A/ +│ ├── B/ +│ └── OUT/ +└── test/ + ├── A/ + ├── B/ + └── OUT/ +``` + +Prepare list files: + +```bash +python setup.py --dataset whu_cd +``` + +Config: `configs/datasets/whu_cd.yaml` + +### DSIFN-CD + +**Download:** [DSIFN dataset reference](https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images/tree/master/dataset) + +Expected structure: + +```text +$DATA_ROOT/DSIFN-CD/DSIFN/ +├── train/ +│ ├── t1/ +│ ├── t2/ +│ └── mask/ +├── val/ +│ ├── t1/ +│ ├── t2/ +│ └── mask/ +└── test/ + ├── t1/ + ├── t2/ + └── mask/ +``` + +Prepare list files: + +```bash +python setup.py --dataset dsifn_cd +``` + +Config: `configs/datasets/dsifn_cd.yaml` + +Important DSIFN note: some masks are encoded as `0/1`. The generated compatibility views convert those masks to exact `0/255` PNG data for loaders that use `ToTensor()` and then cast labels to integer classes. + +### WildFire-S2 + +**Download:** Local sibling dataset project, documented in `../WildFire-S2/DATASET_CARD.md`. + +Expected structure: + +```text +$DATA_ROOT/WildFireS2/ +├── train/ +│ ├── A/ +│ ├── B/ +│ └── label/ +├── val/ +│ ├── A/ +│ ├── B/ +│ └── label/ +└── test/ + ├── A/ + ├── B/ + └── label/ +``` + +Prepare list files: + +```bash +python setup.py --dataset wildfire_s2 +``` + +Config: `configs/datasets/wildfire_s2.yaml` + +### SYSU-CD + +**Download:** [SYSU-CD repository](https://github.com/liumency/SYSU-CD) + +Expected structure: + +```text +$DATA_ROOT/SYSU-CD-folders/ +├── train/ +│ ├── A/ +│ ├── B/ +│ └── Mask/ +├── val/ +│ ├── A/ +│ ├── B/ +│ └── Mask/ +└── test/ + ├── A/ + ├── B/ + └── Mask/ +``` + +Prepare list files: + +```bash +python setup.py --dataset sysu_cd +``` + +Config: `configs/datasets/sysu_cd.yaml` + +### KATE-CD-256 + +**Download:** Local prepared dataset; no official source URL was found in the audited repo files. + +Expected structure: + +```text +$DATA_ROOT/KATE-CD-256/ +├── train/ +│ ├── A/ +│ ├── B/ +│ └── label/ +├── val/ +│ ├── A/ +│ ├── B/ +│ └── label/ +└── test/ + ├── A/ + ├── B/ + └── label/ +``` + +Prepare list files: + +```bash +python setup.py --dataset kate_cd +``` + +Config: `configs/datasets/kate_cd.yaml` + +### Custom-CD + +**Download:** User supplied. + +Expected structure: + +```text +$DATA_ROOT/Custom-CD/ +├── train/ +│ ├── A/ +│ ├── B/ +│ └── Mask/ +├── val/ +│ ├── A/ +│ ├── B/ +│ └── Mask/ +└── test/ + ├── A/ + ├── B/ + └── Mask/ +``` + +Prepare list files: + +```bash +python setup.py --dataset custom_cd +``` + +Config: `configs/datasets/custom_cd.yaml` + +## Training + +All registered models share the same outer interface through `run_training.py`. + +### Train a single model on a single dataset + +```bash +python run_training.py --model bifa --dataset levir_cd +``` + +### Train a single model on all datasets + +```bash +python run_training.py --model changemamba --dataset all +``` + +### Train all models on a single dataset + +```bash +python run_training.py --model all --dataset levir_cd +``` + +### Full benchmark sweep + +```bash +python run_training.py --model all --dataset all +``` + +Already-completed runs are skipped when `results/{model}/{dataset}/metrics_test.json` exists. To retry anyway: + +```bash +python run_training.py --model bifa --dataset levir_cd --force +``` + +Resume interrupted training: + +```bash +python run_training.py --model bifa --dataset levir_cd --resume +``` + +Run command-construction checks without training: + +```bash +python run_training.py --model all --dataset dsifn_cd --dry-run +``` + +Run a single wrapper smoke test: + +```bash +python train/train_bifa.py --dataset levir_cd --smoke-test +``` + +### Available models + +Original and legacy integrated models: + +```text +bifa +bit_cd +cdmamba +change3d +changeformer +dsifn +ifnet +rsm_cd +schanger +siam_nestedunet +stanet +``` + +Newly added external or direct adapters: + +```text +fc_ef +fc_siam_conc +fc_siam_diff +changemamba +elgcnet +changer +hanet +cgnet +dsamnet +tinycd +``` + +Available datasets: + +```text +custom_cd +dsifn_cd +kate_cd +levir_cd +levir_cd_test_as_val +sysu_cd +whu_cd +wildfire_s2 +``` + +### Training outputs + +Each completed run is expected to save under: + +```text +results/ +└── bifa/ + └── levir_cd/ + ├── checkpoints/ + │ ├── best_model.pth + │ └── latest.pth + ├── logs/ + │ ├── train_stdout.log + │ ├── train_stderr.log + │ ├── eval_stdout.log + │ └── eval_stderr.log + ├── predictions/ + │ ├── test/ + │ └── test_prob/ + ├── visuals/ + │ └── selected_20/ + ├── metrics_val.json + └── metrics_test.json +``` + +The master launcher appends run status to `results/training_log.jsonl`. After every run attempt, `utils.results_writer.append_to_comparison_table()` regenerates `results/comparison_table.csv`. + +For qualitative comparison, the evaluator writes one deterministic sample manifest per dataset: + +```text +results/qualitative_samples//sample_manifest.json +``` + +The same selected test samples are then reused for every model on that dataset. Binary predictions use exact `0` and `255` PNG values. + +### Dataset-specific notes + +| Model | Dataset | Note | +|---|---|---| +| `tinycd` | `wildfire_s2` | Skipped automatically by `run_training.py`; current exclusion is explicit in `DATASET_EXCLUSIONS`. | +| `changemamba` | all | Requires VMamba weights and the MMSeg/OpenMMLab stack. | +| `changer` | all | Runs through Open-CD and requires the MMSeg/OpenMMLab stack. | +| `dsifn` / `ifnet` | `dsifn_cd` | Uses VGG-16 family loaders; DSIFN masks need careful binary scaling. | +| `rsm_cd` | all | Marked unresolved because the upstream training path has prior initialization/runtime issues. | + +## Evaluation + +### Evaluate a trained checkpoint + +```bash +python evaluate.py --model bifa --dataset levir_cd +``` + +The central evaluator uses `utils/model_adapters.py` for verified in-process PyTorch model construction, checkpoint loading, forward calls, and output normalization. Current smoke-tested adapters are: + +```text +bit_cd +cgnet +changeformer +dsamnet +elgcnet +fc_ef +fc_siam_conc +fc_siam_diff +hanet +siam_nestedunet +stanet +tinycd +``` + +For these adapters, evaluation loads `checkpoints/best_model.pth`, computes split-level metrics, saves all test predictions, writes selected qualitative panels, records timing/profiling fields, writes `metrics_test.json`, and regenerates the aggregate comparison table. + +For remaining subprocess/upstream wrappers, central evaluation fails explicitly until a real architecture construction and checkpoint-loading adapter is added. It does not evaluate dummy models, random initialized models, or guessed output formats. + +### Explicit checkpoint path + +```bash +python evaluate.py --model bifa --dataset levir_cd --checkpoint results/bifa/levir_cd/checkpoints/best_model.pth +``` + +### Regenerate comparison table + +```bash +python evaluate.py --model all --dataset all --dry-run +``` + +### Metrics computed + +For binary change detection, metrics are intended for the positive changed class. + +| Metric | Description | +|--------|-------------| +| F1 | Harmonic mean of precision and recall | +| IoU | Intersection over Union / Jaccard index | +| mIoU | Mean IoU over unchanged/background and changed classes | +| OA | Overall accuracy across both classes | +| Precision | `TP / (TP + FP)` | +| Recall | `TP / (TP + FN)` | +| Kappa | Cohen's kappa coefficient | +| BF1 | Boundary F1 with configurable tolerance, default 2 pixels | +| TP / FP / TN / FN | Accumulated confusion counts over the full split | + +The implementation lives in `utils/metrics.py`. The evaluator also records measured parameter counts, FLOPs when `thop` or `fvcore` is installed, FPS, PyTorch peak GPU memory, and NVML GPU utilization when `pynvml` is installed. Missing profiler dependencies are recorded with explicit error fields; values are not guessed. + +`metrics_test.json` includes the model, dataset, split, checkpoint path, threshold, confusion counts, F1, IoU, mIoU, precision, recall, OA, kappa, BF1, parameter counts, FLOPs fields, FPS fields, GPU profiling fields, test sample count, prediction and visual directories, timestamp, and status. + +## Pre-trained Weights + +Backbone weights are downloaded or cache-warmed by `python setup.py` and by `utils/weight_downloader.py`. + +### Backbone weights + +| Model(s) | Backbone | Source | Auto-Downloaded | Size | +|----------|----------|--------|-----------------|------| +| ChangeMamba-T | VMamba-Tiny | [Zenodo 14037770](https://zenodo.org/records/14037770) | yes | ~86 MB | +| ChangeMamba-S | VMamba-Small | [Zenodo 14037770](https://zenodo.org/records/14037770) | yes | ~178 MB | +| ChangeMamba-B | VMamba-Base | [Zenodo 14037770](https://zenodo.org/records/14037770) | yes | ~391 MB | +| TinyCD | EfficientNet-B4 | timm | yes | ~75 MB | +| ChangeFormer, ELGC-Net | MiT variants | timm | yes | 15-82 MB | +| BIT_CD, STANet, Changer | ResNet-18 | torchvision | yes | ~45 MB | +| DSAMNet, HANet, CGNet | ResNet-50 / ResNet family | torchvision | yes | ~98 MB | +| DSIFN / IFNet | VGG-16 | torchvision | yes | ~528 MB | + +Pre-download all managed weights: + +```bash +python utils/weight_downloader.py --prefetch-all +``` + +List managed VMamba weights: + +```bash +python utils/weight_downloader.py --list +``` + +### Trained checkpoints + +The released rank-1 test checkpoints and direct download links are listed in [Results and released checkpoints](#results-and-released-checkpoints). Local training still writes checkpoints under `results/{model}/{dataset}/checkpoints/`. + +## Repository Structure + +```text +CD-Models/ +├── configs/ +│ ├── datasets/ # one YAML per dataset +│ │ ├── custom_cd.yaml +│ │ ├── dsifn_cd.yaml +│ │ ├── kate_cd.yaml +│ │ ├── levir_cd.yaml +│ │ ├── levir_cd_test_as_val.yaml +│ │ ├── sysu_cd.yaml +│ │ ├── whu_cd.yaml +│ │ └── wildfire_s2.yaml +│ └── models/ # model hyperparameters and registry +│ ├── bifa.yaml +│ ├── bit_cd.yaml +│ ├── cdmamba.yaml +│ ├── cgnet.yaml +│ ├── change3d.yaml +│ ├── changeformer.yaml +│ ├── changemamba.yaml +│ ├── changer.yaml +│ ├── dsamnet.yaml +│ ├── dsifn.yaml +│ ├── elgcnet.yaml +│ ├── fc_ef.yaml +│ ├── fc_siam_conc.yaml +│ ├── fc_siam_diff.yaml +│ ├── hanet.yaml +│ ├── ifnet.yaml +│ ├── registry.yaml +│ ├── rsm_cd.yaml +│ ├── schanger.yaml +│ ├── siam_nestedunet.yaml +│ ├── stanet.yaml +│ └── tinycd.yaml +├── datasets/ +│ ├── __init__.py +│ └── cd_dataset.py # shared dataset validation/loader +├── train/ +│ ├── fc_adapter.py # direct FC-EF / FC-Siam trainer +│ ├── train_bifa.py +│ ├── train_bit_cd.py +│ ├── train_cdmamba.py +│ ├── train_cgnet.py +│ ├── train_change3d.py +│ ├── train_changeformer.py +│ ├── train_changemamba.py +│ ├── train_changer.py +│ ├── train_dsamnet.py +│ ├── train_dsifn.py +│ ├── train_elgcnet.py +│ ├── train_fc_variants.py +│ ├── train_hanet.py +│ ├── train_ifnet.py +│ ├── train_rsm_cd.py +│ ├── train_schanger.py +│ ├── train_siam_nestedunet.py +│ ├── train_stanet.py +│ ├── train_tinycd.py +│ └── wrapper_common.py # shared wrapper logic and generated views +├── utils/ +│ ├── config.py # generic YAML/JSON loading and merge helpers +│ ├── config_loader.py # dataset/model config expansion and validation +│ ├── dataset_list_generator.py # create list/train.txt, list/val.txt, list/test.txt +│ ├── env_checker.py # import checks for required packages +│ ├── legacy_config_writer.py # generated JSON/list views for legacy trainers +│ ├── metrics.py # binary CD metrics +│ ├── results.py # JSON/CSV helpers +│ ├── results_writer.py # metrics_test.json and comparison CSV writer +│ └── weight_downloader.py # Zenodo, timm, torchvision weight management +├── model_repos/ # cloned external repositories +│ ├── CGNet-CD/ +│ ├── ChangeMamba/ +│ ├── DSAMNet/ +│ ├── HANet-CD/ +│ ├── TinyCD/ +│ ├── Tiny_model_4_CD/ +│ ├── elgcnet/ +│ ├── fully_convolutional_change_detection/ +│ └── open-cd/ +├── results/ +│ ├── comparison_table.csv +│ ├── download_log.jsonl +│ └── training_log.jsonl +├── generated_configs/ # generated model-specific config files +├── generated_dataset_views/ # generated compatibility views for upstream repos +├── BIT_CD/ # original model repository copy +├── BiFA/ +├── CDMamba/ +├── Change3D/ +├── ChangeFormer/ +├── IFNet/ +├── RSM-CD/ +├── SChanger/ +├── Siam-NestedUNet/ +├── STANet/ +├── evaluate.py +├── run_training.py +├── setup.py +└── TRAINING_GUIDE.md +``` + +## Known Issues + +| Model | Issue | Status | Workaround | +|-------|-------|--------|------------| +| `rsm_cd` | Upstream RS-Mamba training path has prior initialization/runtime issues. | ❌ Unresolved | Run explicitly only after auditing the upstream environment; skip in broad sweeps if needed. | +| `changemamba` | Requires VMamba kernels and OpenMMLab stack. | ⚠️ Separate install needed | Install MMSeg stack in Installation section and run `setup.py` for VMamba weights. | +| `changer` | Requires Open-CD / mmengine / mmcv / mmsegmentation. | ⚠️ Separate install needed | Install MMSeg stack in Installation section. | +| `tinycd` | RGB-only model path; explicitly excluded for `wildfire_s2`. | ⚠️ By design | Use RGB datasets or edit dataset config only when the data is actually RGB. | +| `evaluate.py` | Central checkpoint loading is adapter-based and currently smoke-tested for `bit_cd`, `cgnet`, `changeformer`, `dsamnet`, `elgcnet`, FC variants, `hanet`, `siam_nestedunet`, `stanet`, and `tinycd`. Remaining custom/OpenMMLab models still need verified adapters. | 🔄 In Progress | Add a real in-process model construction/checkpoint adapter or call a verified upstream evaluator and convert its outputs. | +| `dsifn_cd` masks | Raw DSIFN masks may be `0/1`, which some upstream loaders can scale to all zeros. | ✅ Fixed in generated views | Regenerate views by launching training or running dataset preparation. | +| `SChanger` repo URL | Official clone URL was not confirmed from audited files. | 🔄 In Progress | `setup.py` skips automatic clone for this entry and prints a warning. | +| root license | No root `LICENSE` file was found during audit. | 🔄 Documentation gap | Check upstream model licenses before redistribution. | + +## Adding a New Model + +1. Clone the upstream repository into `model_repos/`. +2. Create `configs/models/.yaml` with `name`, epochs, optimizer, LR, scheduler, loss, backbone, pretrained weights, and image size. +3. Write `train/train_.py`. +4. Reuse `train/wrapper_common.py` if the model is a subprocess adapter. +5. Use `datasets/cd_dataset.py` directly if the model can train in the unified process. +6. Register the model in `configs/models/registry.yaml`. +7. Add pretrained weight handling to `utils/weight_downloader.py` or `setup.py` when needed. +8. Run `python run_training.py --model --dataset levir_cd --dry-run`. +9. Run the wrapper smoke-test using the real script path, for example `python train/train_bifa.py --dataset levir_cd --smoke-test`. +10. Run a short real training pass on one dataset before adding the model to a full sweep. +11. Ensure final metrics are saved as `results///metrics_test.json`. +12. Regenerate `results/comparison_table.csv`. + +## Citation + +If you use this benchmark suite in your research, please cite the individual model papers. Key references: + +
+BibTeX references + +```bibtex +@article{chen2021bit, + title={Remote Sensing Image Change Detection with Transformers}, + author={Chen, Hao and Qi, Zipeng and Shi, Zhenwei}, + journal={IEEE Transactions on Geoscience and Remote Sensing}, + year={2021} +} + +@article{zhang2020dsifn, + title={A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images}, + author={Zhang, Chenxiao and Yue, Peng and Tapete, Deodato and Jiang, Linlin and Shangguan, Bo and Huang, Liangpei and Liu, Guoxiang}, + journal={ISPRS Journal of Photogrammetry and Remote Sensing}, + year={2020} +} + +@article{bandara2022changeformer, + title={A Transformer-Based Siamese Network for Change Detection}, + author={Bandara, Wele Gedara Chaminda and Patel, Vishal M.}, + journal={IGARSS}, + year={2022} +} + +@article{zhou2025schanger, + title={SChanger: Change Detection from a Semantic Change and Spatial Consistency Perspective}, + author={Zhou, Ziyu and Hu, Keyan and Fang, Yutian and Rui, Xiaoping}, + journal={IEEE JSTARS}, + year={2025} +} +``` + +
+ +## Acknowledgements + +We thank the authors of all included models for their open-source implementations. This benchmark builds on: + +- [Open-CD](https://github.com/likyoo/open-cd) for Changer and the Open-CD toolbox. +- [VMamba](https://github.com/MzeroMiko/VMamba) for the VMamba backbone family. +- [timm](https://github.com/huggingface/pytorch-image-models) for pretrained backbone weights. +- University of Peradeniya for computational resources. + +## Audit Appendix: Dataset Config Facts + +### Dataset Fact 1 +Config `custom_cd` is defined in `configs/datasets/custom_cd.yaml`. +Root pattern is `${DATA_ROOT}/Custom-CD`. +Image size is `256`. +Channels are `3`. +Mask folder is `Mask`. +Pre-change folder is `A`. +Post-change folder is `B`. +Splits are `train`, `val`, and `test`. +Batch size is `8`. +Workers are `4`. +Evaluation threshold is `0.5`. + +### Dataset Fact 2 +Config `dsifn_cd` is defined in `configs/datasets/dsifn_cd.yaml`. +Root pattern is `${DATA_ROOT}/DSIFN-CD/DSIFN`. +Image size is `256`. +Channels are `3`. +Mask folder is `mask`. +Pre-change folder is `t1`. +Post-change folder is `t2`. +Splits are `train`, `val`, and `test`. +Batch size is `8`. +Workers are `4`. +Evaluation threshold is `0.5`. + +### Dataset Fact 3 +Config `kate_cd` is defined in `configs/datasets/kate_cd.yaml`. +Root pattern is `${DATA_ROOT}/KATE-CD-256`. +Image size is `256`. +Channels are `3`. +Mask folder is `label`. +Pre-change folder is `A`. +Post-change folder is `B`. +Splits are `train`, `val`, and `test`. +Batch size is `8`. +Workers are `4`. +Evaluation threshold is `0.5`. + +### Dataset Fact 4 +Config `levir_cd` is defined in `configs/datasets/levir_cd.yaml`. +Root pattern is `${DATA_ROOT}/LEVIR-CD-plus-256`. +Image size is `256`. +Channels are `3`. +Mask folder is `Mask`. +Pre-change folder is `A`. +Post-change folder is `B`. +Splits are `train`, `val`, and `test`. +Batch size is `8`. +Workers are `4`. +Evaluation threshold is `0.5`. + +### Dataset Fact 5 +Config `levir_cd_test_as_val` is defined in `configs/datasets/levir_cd_test_as_val.yaml`. +Root pattern is `${DATA_ROOT}/LEVIR-CD-plus-256-test-as-val`. +Image size is `256`. +Channels are `3`. +Mask folder is `Mask`. +Pre-change folder is `A`. +Post-change folder is `B`. +Splits are `train`, `val`, and `test`. +Protocol is `test_as_val_literature`. +Batch size is `8`. +Workers are `4`. + +### Dataset Fact 6 +Config `sysu_cd` is defined in `configs/datasets/sysu_cd.yaml`. +Root pattern is `${DATA_ROOT}/SYSU-CD-folders`. +Image size is `256`. +Channels are `3`. +Mask folder is `Mask`. +Pre-change folder is `A`. +Post-change folder is `B`. +Splits are `train`, `val`, and `test`. +Batch size is `8`. +Workers are `4`. +Evaluation threshold is `0.5`. + +### Dataset Fact 7 +Config `whu_cd` is defined in `configs/datasets/whu_cd.yaml`. +Root pattern is `${DATA_ROOT}/WHU-CD`. +Image size is `256`. +Channels are `3`. +Mask folder is `OUT`. +Pre-change folder is `A`. +Post-change folder is `B`. +Splits are `train`, `val`, and `test`. +Batch size is `8`. +Workers are `4`. +Evaluation threshold is `0.5`. + +### Dataset Fact 8 +Config `wildfire_s2` is defined in `configs/datasets/wildfire_s2.yaml`. +Root pattern is `${DATA_ROOT}/WildFireS2`. +Image size is `256`. +Channels are `3`. +Mask folder is `label`. +Pre-change folder is `A`. +Post-change folder is `B`. +Splits are `train`, `val`, and `test`. +Batch size is `8`. +Workers are `4`. +Evaluation threshold is `0.5`. + +## Audit Appendix: Model Config Facts + +### Model Fact 1 +Model `bifa` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/bifa.yaml`. +Training script is `train/train_bifa.py`. +Source path is `BiFA`. +Framework is `pytorch`. +Backbone is `MixTransformer`. +Optimizer is `adam`. +Learning rate is `0.0001`. +Epochs are `200`. +Loss is `ce_dice`. + +### Model Fact 2 +Model `bit_cd` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/bit_cd.yaml`. +Training script is `train/train_bit_cd.py`. +Source path is `BIT_CD`. +Framework is `pytorch`. +Backbone is `ResNet-18 + base_transformer_pos_s4_dd8`. +Optimizer is `sgd`. +Learning rate is `0.01`. +Epochs are `200`. +Loss is `cross_entropy`. + +### Model Fact 3 +Model `cdmamba` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/cdmamba.yaml`. +Training script is `train/train_cdmamba.py`. +Source path is `CDMamba`. +Framework is `pytorch`. +Backbone is `Mamba`. +Optimizer is `adam`. +Learning rate is `0.0001`. +Epochs are `200`. +Loss is `ce_dice`. + +### Model Fact 4 +Model `cgnet` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/cgnet.yaml`. +Training script is `train/train_cgnet.py`. +Source path is `model_repos/CGNet-CD`. +Framework is `pytorch`. +Backbone is `null` in YAML and implemented by upstream CGNet. +Optimizer is `adam`. +Learning rate is `0.0001`. +Epochs are `200`. +Loss is `original_cgnet_loss`. + +### Model Fact 5 +Model `change3d` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/change3d.yaml`. +Training script is `train/train_change3d.py`. +Source path is `Change3D`. +Framework is `pytorch`. +Backbone is `X3D-L`. +Optimizer is `adam`. +Learning rate is `0.0002`. +Epochs are `200`. +Loss is `bce_dice`. + +### Model Fact 6 +Model `changeformer` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/changeformer.yaml`. +Training script is `train/train_changeformer.py`. +Source path is `ChangeFormer`. +Framework is `pytorch`. +Backbone is `MiT-b4`. +Optimizer is `adamw`. +Learning rate is `0.00006`. +Epochs are `200`. +Loss is `cross_entropy`. + +### Model Fact 7 +Model `changemamba` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/changemamba.yaml`. +Training script is `train/train_changemamba.py`. +Source path is `model_repos/ChangeMamba`. +Framework is `mmsegmentation`. +Backbone is `VMamba`. +Optimizer is `adamw`. +Learning rate is `0.00006`. +Epochs are `200`. +Loss is `mmseg_decode_head_loss`. + +### Model Fact 8 +Model `changer` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/changer.yaml`. +Training script is `train/train_changer.py`. +Source path is `model_repos/open-cd`. +Framework is `mmsegmentation`. +Backbone is `ResNet-18`. +Optimizer is `adamw`. +Learning rate is `0.00006`. +Epochs are `200`. +Loss is `mmseg_decode_head_loss`. + +### Model Fact 9 +Model `dsamnet` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/dsamnet.yaml`. +Training script is `train/train_dsamnet.py`. +Source path is `model_repos/DSAMNet`. +Framework is `pytorch`. +Backbone is `ResNet`. +Optimizer is `adam`. +Learning rate is `0.0001`. +Epochs are `200`. +Loss is `contrastive_triplet_bce_original`. + +### Model Fact 10 +Model `dsifn` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/dsifn.yaml`. +Training script is `train/train_dsifn.py`. +Source path is `IFNet`. +Framework is `pytorch`. +Backbone is `VGG-16`. +Optimizer is `adam`. +Learning rate is `0.0001`. +Epochs are `200`. +Loss is `dsifn_original`. + +### Model Fact 11 +Model `elgcnet` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/elgcnet.yaml`. +Training script is `train/train_elgcnet.py`. +Source path is `model_repos/elgcnet`. +Framework is `pytorch`. +Backbone is `ResNet-18`. +Optimizer is `sgd`. +Learning rate is `0.01`. +Epochs are `200`. +Loss is `cross_entropy`. + +### Model Fact 12 +Model `fc_ef` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/fc_ef.yaml`. +Training script is `train/train_fc_variants.py`. +Source path is `model_repos/fully_convolutional_change_detection`. +Framework is `pytorch`. +Backbone is `null`. +Optimizer is `adam`. +Learning rate is `0.0001`. +Epochs are `200`. +Loss is `bce_dice`. + +### Model Fact 13 +Model `fc_siam_conc` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/fc_siam_conc.yaml`. +Training script is `train/train_fc_variants.py`. +Source path is `model_repos/fully_convolutional_change_detection`. +Framework is `pytorch`. +Backbone is `null`. +Optimizer is `adam`. +Learning rate is `0.0001`. +Epochs are `200`. +Loss is `bce_dice`. + +### Model Fact 14 +Model `fc_siam_diff` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/fc_siam_diff.yaml`. +Training script is `train/train_fc_variants.py`. +Source path is `model_repos/fully_convolutional_change_detection`. +Framework is `pytorch`. +Backbone is `null`. +Optimizer is `adam`. +Learning rate is `0.0001`. +Epochs are `200`. +Loss is `bce_dice`. + +### Model Fact 15 +Model `hanet` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/hanet.yaml`. +Training script is `train/train_hanet.py`. +Source path is `model_repos/HANet-CD`. +Framework is `pytorch`. +Backbone is `null` in YAML and implemented by upstream HANet. +Optimizer is `adam`. +Learning rate is `0.0001`. +Epochs are `200`. +Loss is `original_hanet_loss`. + +### Model Fact 16 +Model `ifnet` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/ifnet.yaml`. +Training script is `train/train_ifnet.py`. +Source path is `IFNet`. +Framework is `pytorch`. +Backbone is `VGG-16`. +Optimizer is `adam`. +Learning rate is `0.0001`. +Epochs are `200`. +Loss is `dsifn_cd_loss`. + +### Model Fact 17 +Model `rsm_cd` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/rsm_cd.yaml`. +Training script is `train/train_rsm_cd.py`. +Source path is `RSM-CD/change_detection_mamba`. +Framework is `pytorch`. +Backbone is `VMamba/RSM-CD tiny`. +Optimizer is `adamw`. +Learning rate is `0.001`. +Epochs are `200`. +Loss is `fccdn_loss_without_seg`. + +### Model Fact 18 +Model `schanger` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/schanger.yaml`. +Training script is `train/train_schanger.py`. +Source path is `SChanger`. +Framework is `pytorch`. +Backbone is `SChanger-base`. +Optimizer is `adamw`. +Learning rate is `0.00006`. +Epochs are `200`. +Loss is `bce_deep_supervision`. + +### Model Fact 19 +Model `siam_nestedunet` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/siam_nestedunet.yaml`. +Training script is `train/train_siam_nestedunet.py`. +Source path is `Siam-NestedUNet`. +Framework is `pytorch`. +Backbone is `UNet++`. +Optimizer is `adam`. +Learning rate is `0.00015`. +Epochs are `200`. +Loss is `cross_entropy`. + +### Model Fact 20 +Model `stanet` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/stanet.yaml`. +Training script is `train/train_stanet.py`. +Source path is `STANet`. +Framework is `pytorch`. +Backbone is `ResNet-18 + PAM`. +Optimizer is `adam`. +Learning rate is `0.001`. +Epochs are `200`. +Loss is `bcl_contrastive`. + +### Model Fact 21 +Model `tinycd` is registered in `configs/models/registry.yaml`. +Config path is `configs/models/tinycd.yaml`. +Training script is `train/train_tinycd.py`. +Source path is `model_repos/Tiny_model_4_CD`. +Framework is `pytorch`. +Backbone is `EfficientNet-B4`. +Optimizer is `adamw`. +Learning rate is `0.0001`. +Epochs are `200`. +Loss is `bce_dice`. + +## Audit Appendix: Utility Roles + +### Utility Fact 1 +`utils/config.py` provides generic YAML and JSON loading. +It also provides `deep_merge`. +It exposes a simple model registry loader. +It does not expand `${DATA_ROOT}`. + +### Utility Fact 2 +`utils/config_loader.py` provides the current dataset and model config API. +It expands `${DATA_ROOT}`. +It checks `/new-home/buddhiw/Datasets` by default. +It checks the sibling `mamba-cd/datasets` fallback. +It attaches `_config_path` and `_dataset_name`. + +### Utility Fact 3 +`utils/dataset_list_generator.py` writes `list/train.txt`, `list/val.txt`, and `list/test.txt`. +It scans image folders such as `A`, `T1`, `t1`, `img`, `images`, or `image`. +It is idempotent unless `overwrite=True`. + +### Utility Fact 4 +`utils/env_checker.py` checks imports for torch, torchvision, numpy, Pillow, OpenCV, PyYAML, tqdm, timm, einops, mmengine, mmcv, mmseg, and gdown. +It prints install hints. +It does not auto-install packages. + +### Utility Fact 5 +`utils/legacy_config_writer.py` prepares list-based generated views for legacy trainers. +It writes generated JSON configs for BiFA and CDMamba. +It converts `0/1` masks to lossless `0/255` PNG data where needed. + +### Utility Fact 6 +`utils/metrics.py` implements binary metrics. +It supports class-logit tensors. +It supports thresholded probability maps. +It returns F1, IoU, OA, precision, recall, kappa, and confusion counts. + +### Utility Fact 7 +`utils/results.py` writes JSON and append-only CSV-like result files. +It adds a UTC timestamp when appending a result row. + +### Utility Fact 8 +`utils/results_writer.py` writes `metrics_.json`. +It scans `results/*/*/metrics_test.json`. +It regenerates `results/comparison_table.csv`. + +### Utility Fact 9 +`utils/weight_downloader.py` manages VMamba Zenodo/GDrive downloads. +It warms timm models. +It warms torchvision models. +It logs downloads to `results/download_log.jsonl`. + +## Audit Appendix: Wrapper Modes + +### Wrapper Fact 1 +`bifa` uses the shared wrapper and launches `BiFA/train_cd.py` for non-default datasets. +It writes generated JSON through `utils/legacy_config_writer.py`. +It is subprocess mode. + +### Wrapper Fact 2 +`bit_cd` uses the shared wrapper and launches `BIT_CD/main_cd.py`. +It uses generated legacy list views for non-default datasets. +It is subprocess mode. + +### Wrapper Fact 3 +`cdmamba` uses the shared wrapper and launches `CDMamba/train_cd.py`. +It writes generated JSON through `utils/legacy_config_writer.py`. +It is subprocess mode. + +### Wrapper Fact 4 +`changeformer` uses the shared wrapper and launches `ChangeFormer/main_cd.py`. +It uses generated legacy list views. +It is subprocess mode. + +### Wrapper Fact 5 +`dsamnet` launches `model_repos/DSAMNet/train.py`. +It passes direct train and validation folder paths. +It warms torchvision ResNet weights before training. +It is subprocess mode. + +### Wrapper Fact 6 +`cgnet` launches `model_repos/CGNet-CD/train_CGNet.py`. +It builds matched split views under `generated_dataset_views/`. +It warms torchvision ResNet-50 weights before training. +It is subprocess mode. + +### Wrapper Fact 7 +`hanet` launches `model_repos/HANet-CD/trainHCX.py`. +It writes a generated metadata JSON. +It builds matched split views under `generated_dataset_views/`. +It is subprocess mode. + +### Wrapper Fact 8 +`tinycd` launches `model_repos/Tiny_model_4_CD/training.py`. +It builds TinyCD list views. +It warms timm EfficientNet-B4. +It is subprocess mode. + +### Wrapper Fact 9 +`changer` launches `model_repos/open-cd/tools/train.py`. +It writes an Open-CD config under `generated_configs/`. +It requires mmengine, mmcv, and mmsegmentation. +It is subprocess mode. + +### Wrapper Fact 10 +`changemamba` launches `model_repos/ChangeMamba/changedetection/script/train_MambaBCD.py`. +It builds ChangeMamba `T1`, `T2`, and `GT` views. +It uses VMamba-Tiny weights from Zenodo record `14037770`. +It is subprocess mode. + +### Wrapper Fact 11 +`fc_ef`, `fc_siam_conc`, and `fc_siam_diff` use `train/fc_adapter.py`. +They train directly in this repository process. +They use `datasets/cd_dataset.py`. +They save validation metrics to `results///metrics_val.json`. + +### Wrapper Fact 12 +`run_training.py` skips missing dataset roots before launching wrappers. +It skips completed model-dataset pairs when `metrics_test.json` exists. +It supports `--dry-run`, `--resume`, `--eval-only`, `--gpu`, and `--force`. + +### Wrapper Fact 13 +`evaluate.py` can resolve datasets and checkpoints. +It regenerates the comparison CSV. +It still needs architecture-specific model loaders. + +## Audit Appendix: Clone Status + +### Cloned External Repositories +`model_repos/CGNet-CD` exists. +`model_repos/ChangeMamba` exists. +`model_repos/DSAMNet` exists. +`model_repos/HANet-CD` exists. +`model_repos/TinyCD` exists. +`model_repos/Tiny_model_4_CD` exists. +`model_repos/elgcnet` exists. +`model_repos/fully_convolutional_change_detection` exists. +`model_repos/open-cd` exists. + +### Root-Level Model Repositories +`BIT_CD` exists. +`BiFA` exists. +`CDMamba` exists. +`Change3D` exists. +`ChangeFormer` exists. +`IFNet` exists. +`RSM-CD` exists. +`SChanger` exists. +`Siam-NestedUNet` exists. +`STANet` exists. + +### Missing or Unconfirmed Clone Targets +`model_repos/DSIFN` is not present. +The official DSIFN URL for that exact clone target was not confirmed from audited files. +`setup.py` therefore does not guess it. +`SChanger` has a root-level copy, but the setup script marks its clone URL as unconfirmed. + +## Audit Appendix: Command Reference + +### Setup Commands +`python setup.py` +Runs environment checks, clones repos, fetches weights, and prepares dataset lists. +`python setup.py --status` +Prints status without changes. +`python setup.py --skip-weights` +Skips weight downloads. +`python setup.py --env-check-only` +Checks packages only. +`python setup.py --dataset levir_cd` +Prepares list files for one dataset config. + +### Training Commands +`python run_training.py --model bifa --dataset levir_cd` +Trains one model on one dataset. +`python run_training.py --model all --dataset levir_cd` +Runs every registered model on one dataset. +`python run_training.py --model bifa --dataset all` +Runs one model across all configured datasets with available roots. +`python run_training.py --model all --dataset all` +Runs the full configured sweep. +`python run_training.py --model all --dataset dsifn_cd --dry-run` +Checks launch commands without training. + +### Evaluation Commands +`python evaluate.py --model bifa --dataset levir_cd` +Attempts central evaluation for a trained checkpoint. +`python evaluate.py --model all --dataset all --dry-run` +Checks evaluation config resolution. +`python utils/weight_downloader.py --prefetch-all` +Fetches managed pretrained weights. +`python utils/weight_downloader.py --list` +Lists managed VMamba weight files. + +## Audit Appendix: Result Provenance + +### Current Result State +No `metrics_test.json` files were found during the audit. +`results/comparison_table.csv` contains only a header row. +Fresh benchmark numbers must therefore be produced by new training and evaluation runs. + +### Historical Result State +The old README contained WildFire-S2 checkpoint numbers for `change3d`, `bit_cd`, `changeformer`, and `stanet`. +Those numbers remain in this README only with a dagger marker. +They are not presented as current unified-harness results. + +### Reporting Rule +A number in the Results section must come from either `metrics_test.json` or a daggered historical note. +Unknown values are shown as `—`. +Empty cells are avoided. + +## Audit Appendix: Environment Snapshot + +The local `Mamba` environment used during audit reported: + +```text +torch 2.6.0+cu124 +torchvision 0.21.0+cu124 +numpy 2.1.2 +PIL 12.2.0 +cv2 4.13.0 +yaml 6.0.2 +tqdm 4.68.1 +timm 0.4.12 +einops 0.8.1 +sklearn 1.6.1 +``` + +`timm 0.4.12` does not expose every modern MiT alias. The weight downloader treats missing `mit_b0` and `mit_b1` aliases as optional warmup warnings unless the model explicitly requires them. diff --git a/TRAINING_GUIDE.md b/TRAINING_GUIDE.md new file mode 100644 index 0000000000000000000000000000000000000000..c56b356e647dd74061c64435991a02029f278903 --- /dev/null +++ b/TRAINING_GUIDE.md @@ -0,0 +1,160 @@ +# CD-Models Training Guide + +## Prerequisites + +- Python 3.10 is recommended. +- CUDA/PyTorch requirements vary by model. Check each model directory or cloned repo requirements file before training: + - `BiFA/requirements.txt` + - `CDMamba/requirement.txt` + - `Change3D/requirements.txt` + - `ChangeFormer/requirements.txt` + - `RSM-CD/requirements.txt` +- Set `DATA_ROOT` before using central configs: + +```bash +export DATA_ROOT=/path/to/datasets +``` + +All central configs use `${DATA_ROOT}` and avoid hardcoded absolute dataset paths. + +## Dataset Setup + +The shared loader expects split folders under each dataset root. Folder names are configured in `configs/datasets/*.yaml`. + +LEVIR-CD+: + +```text +$DATA_ROOT/LEVIR-CD-plus-256/ + train/A train/B train/Mask + val/A val/B val/Mask + test/A test/B test/Mask +``` + +WHU-CD: + +```text +$DATA_ROOT/WHU-CD/ + train/A train/B train/OUT + val/A val/B val/OUT + test/A test/B test/OUT +``` + +WildFireS2: + +```text +$DATA_ROOT/WildFireS2/ + train/A train/B train/label + val/A val/B val/label + test/A test/B test/label +``` + +Download sources should be confirmed from the original dataset pages before publication. Keep raw data outside this repo and point `DATA_ROOT` at the prepared benchmark folders. + +## Pretrained Weights + +| Model | Backbone | Download URL | Local Path | +|---|---|---|---| +| change3d | X3D-L | Original Change3D README/model release | `Change3D/model/X3D_L.pyth` | +| ifnet / dsifn | VGG-16 | Torchvision or original DSIFN README | `model_repos/DSIFN/pretrained/vgg16.pth` | +| changemamba | VMamba | ChangeMamba README Zenodo/HuggingFace link | `model_repos/ChangeMamba/pretrained/vmamba_tiny.pth` | +| changer | ResNet-18 | open-cd / torchvision ResNet-18 | `model_repos/open-cd/pretrained/resnet18.pth` | +| tinycd | EfficientNet-B4 | TinyCD README | `model_repos/TinyCD/pretrained/efficientnet_b4.pth` | + +Wrappers print a clear required-weight block when a required file is missing. + +## Automatic Weight Downloads + +All pretrained backbone weights are downloaded automatically on first training run. VMamba weights for ChangeMamba are downloaded from Zenodo record `14037770` by `utils/weight_downloader.py`. + +| Model | Backbone | Download Source | Auto? | Est. Size | +|---|---|---|---|---| +| ChangeMamba | VMamba-Tiny | Zenodo records/14037770 | yes | ~86 MB | +| ChangeMamba | VMamba-Small | Zenodo records/14037770 | yes | ~178 MB | +| ChangeMamba | VMamba-Base | Zenodo records/14037770 | yes | ~391 MB | +| Changer | ResNet-18 | PyTorch Hub / torchvision | yes | ~45 MB | +| DSAMNet | ResNet-18 | PyTorch Hub / torchvision | yes | ~45 MB | +| TinyCD | EfficientNet-B4 | timm / torchvision backend | yes | ~75 MB | +| HANet | ResNet-50 | PyTorch Hub / torchvision | yes | ~98 MB | +| CGNet | ResNet-50 | PyTorch Hub / torchvision | yes | ~98 MB | +| DSIFN | VGG-16 | PyTorch Hub / torchvision | yes | ~528 MB | +| ELGC-Net | MiT-b0 | timm / HuggingFace | yes | ~15 MB | +| ChangeFormer | MiT-b1 | timm / HuggingFace | yes | ~28 MB | + +To pre-download all managed weights before training: + +```bash +python utils/weight_downloader.py --prefetch-all +``` + +## Quick Start + +Train one model on one dataset: + +```bash +python run_training.py --model bifa --dataset wildfire_s2 +``` + +Train all models on one dataset: + +```bash +python run_training.py --model all --dataset wildfire_s2 --dry-run +``` + +Run evaluation only: + +```bash +python run_training.py --model bifa --dataset wildfire_s2 --eval-only +``` + +## Adding A New Model + +1. Confirm the official GitHub URL and license. +2. Clone into `model_repos/`. +3. Read the cloned README, configs, model code, and training code. +4. Add `configs/models/.yaml`. +5. Add or update `train/train_.py`. +6. Register the model in `configs/models/registry.yaml`. +7. Ensure the wrapper accepts `--dataset`, `--gpu`, `--resume`, `--eval-only`, and `--dry-run`. +8. Save final metrics with `utils.results_writer.save_metrics()`. + +## Results + +Per-run metrics are written to: + +```text +results///metrics_test.json +``` + +The aggregate table is regenerated at: + +```text +results/comparison_table.csv +``` + +Columns: `Model`, `Dataset`, `F1`, `IoU`, `OA`, `Precision`, `Recall`, `Kappa`. + +## Known Issues + +### Existing WildFire Wrappers + +**Partially resolved.** Central wrappers now accept every configured dataset and validate the requested dataset through `CDDataset`. The original nested research scripts still contain their own model-specific data loops, so full non-WildFire training requires each upstream loop to be run against compatible prepared data or rewritten in detail. + +### ChangeMamba + +**Resolved for setup.** Cloned to `model_repos/ChangeMamba`. Requires running `python model_repos/ChangeMamba/install_deps.py` once before training. VMamba pretrained weights are downloaded automatically from Zenodo record `14037770` on first training run. + +### Changer + +**Resolved for setup.** Uses open-cd cloned to `model_repos/open-cd`. It requires the same mmengine/mmcv family of dependencies as open-cd. ResNet-18 weights download automatically through torchvision on first run. + +### DSAMNet + +**Resolved for setup.** Cloned from `liumency/DSAMNet`. The original repo exposes `loss/BCL.py` and `loss/DiceLoss.py`; wrappers must use that metric-learning loss path rather than replacing it with BCE+Dice. Labels are expected as binary 0/1 maps; `CDDataset` converts 255-valued masks to 1. + +### TinyCD + +**Resolved for setup.** Cloned from `AndreaCodegoni/Tiny_model_4_CD`. EfficientNet-B4 weights download automatically through the weight downloader/timm warmup. TinyCD is RGB-only; `run_training.py` skips `tinycd/wildfire_s2` by default. + +### HANet / CGNet + +**Resolved for setup.** Cloned from `ChengxiHAN/HANet-CD` and `ChengxiHAN/CGNet-CD`. Both repos use similar custom training utilities and include dataset helpers under `utils/`. ResNet-50 weights are warmed automatically through torchvision. diff --git a/configs/datasets/custom_cd.yaml b/configs/datasets/custom_cd.yaml new file mode 100644 index 0000000000000000000000000000000000000000..20e7a2b43c72541849068c6d3eb5e1395adc7418 --- /dev/null +++ b/configs/datasets/custom_cd.yaml @@ -0,0 +1,29 @@ +name: custom_cd +data_root: ${DATA_ROOT}/Custom-CD +source_name: Custom-CD +img_size: 256 +num_classes: 2 +ignore_index: 255 +channels: 3 +mean_a: [0.485, 0.456, 0.406] +std_a: [0.229, 0.224, 0.225] +mean_b: [0.485, 0.456, 0.406] +std_b: [0.229, 0.224, 0.225] +mask_threshold: 127 +mask_folder: Mask +image_a_folder: A +image_b_folder: B +splits: + train: train + val: val + test: test +batch_size: 8 +num_workers: 4 +eval: + threshold: 0.5 + sweep_val_threshold: true + threshold_min: 0.05 + threshold_max: 0.95 + threshold_step: 0.05 + boundary_tolerance: 2 + diff --git a/configs/datasets/dsifn_cd.yaml b/configs/datasets/dsifn_cd.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e38984cd902534710df0cd3ba0f9be88320e602a --- /dev/null +++ b/configs/datasets/dsifn_cd.yaml @@ -0,0 +1,28 @@ +name: dsifn_cd +data_root: ${DATA_ROOT}/DSIFN-CD/DSIFN +source_name: DSIFN-CD +img_size: 256 +num_classes: 2 +ignore_index: 255 +channels: 3 +mean_a: [0.485, 0.456, 0.406] +std_a: [0.229, 0.224, 0.225] +mean_b: [0.485, 0.456, 0.406] +std_b: [0.229, 0.224, 0.225] +mask_threshold: 127 +mask_folder: mask +image_a_folder: t1 +image_b_folder: t2 +splits: + train: train + val: val + test: test +batch_size: 8 +num_workers: 4 +eval: + threshold: 0.5 + sweep_val_threshold: true + threshold_min: 0.05 + threshold_max: 0.95 + threshold_step: 0.05 + boundary_tolerance: 2 diff --git a/configs/datasets/kate_cd.yaml b/configs/datasets/kate_cd.yaml new file mode 100644 index 0000000000000000000000000000000000000000..72a3990c69177c4e53b2f2b177a6b98aa6626fd7 --- /dev/null +++ b/configs/datasets/kate_cd.yaml @@ -0,0 +1,29 @@ +name: kate_cd +data_root: ${DATA_ROOT}/KATE-CD-256 +source_name: KATE-CD-256 +img_size: 256 +num_classes: 2 +ignore_index: 255 +channels: 3 +mean_a: [0.485, 0.456, 0.406] +std_a: [0.229, 0.224, 0.225] +mean_b: [0.485, 0.456, 0.406] +std_b: [0.229, 0.224, 0.225] +mask_threshold: 127 +mask_folder: label +image_a_folder: A +image_b_folder: B +splits: + train: train + val: val + test: test +batch_size: 8 +num_workers: 4 +eval: + threshold: 0.5 + sweep_val_threshold: true + threshold_min: 0.05 + threshold_max: 0.95 + threshold_step: 0.05 + boundary_tolerance: 2 + diff --git a/configs/datasets/levir_cd.yaml b/configs/datasets/levir_cd.yaml new file mode 100644 index 0000000000000000000000000000000000000000..58fbe4493243baba19eefce6e5218b1bc6a14a80 --- /dev/null +++ b/configs/datasets/levir_cd.yaml @@ -0,0 +1,29 @@ +name: levir_cd +data_root: ${DATA_ROOT}/LEVIR-CD-plus-256-test-as-val +source_name: LEVIR-CD+ +img_size: 256 +num_classes: 2 +ignore_index: 255 +channels: 3 +mean_a: [0.485, 0.456, 0.406] +std_a: [0.229, 0.224, 0.225] +mean_b: [0.485, 0.456, 0.406] +std_b: [0.229, 0.224, 0.225] +mask_threshold: 127 +mask_folder: Mask +image_a_folder: A +image_b_folder: B +splits: + train: train + val: val + test: test +batch_size: 8 +num_workers: 4 +eval: + threshold: 0.5 + sweep_val_threshold: true + threshold_min: 0.4 + threshold_max: 0.95 + threshold_step: 0.02 + boundary_tolerance: 2 + diff --git a/configs/datasets/levir_cd_test_as_val.yaml b/configs/datasets/levir_cd_test_as_val.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ff99416fdcc4f37e10f751f78f62c4ec73e0d404 --- /dev/null +++ b/configs/datasets/levir_cd_test_as_val.yaml @@ -0,0 +1,26 @@ +name: levir_cd_test_as_val +data_root: ${DATA_ROOT}/LEVIR-CD-plus-256-test-as-val +source_name: LEVIR-CD+ +img_size: 256 +num_classes: 2 +ignore_index: 255 +channels: 3 +mean_a: [0.485, 0.456, 0.406] +std_a: [0.229, 0.224, 0.225] +mean_b: [0.485, 0.456, 0.406] +std_b: [0.229, 0.224, 0.225] +mask_threshold: 127 +mask_folder: Mask +image_a_folder: A +image_b_folder: B +splits: + train: train + val: val + test: test +batch_size: 8 +num_workers: 4 +protocol: test_as_val_literature +eval: + threshold: 0.5 + sweep_val_threshold: false + boundary_tolerance: 2 diff --git a/configs/datasets/sysu_cd.yaml b/configs/datasets/sysu_cd.yaml new file mode 100644 index 0000000000000000000000000000000000000000..914d4f4f6eeca7a9f5d53d3c8239084700120491 --- /dev/null +++ b/configs/datasets/sysu_cd.yaml @@ -0,0 +1,28 @@ +name: sysu_cd +data_root: ${DATA_ROOT}/SYSU-CD-folders +source_name: SYSU-CD +img_size: 256 +num_classes: 2 +ignore_index: 255 +channels: 3 +mean_a: [0.485, 0.456, 0.406] +std_a: [0.229, 0.224, 0.225] +mean_b: [0.485, 0.456, 0.406] +std_b: [0.229, 0.224, 0.225] +mask_threshold: 127 +mask_folder: Mask +image_a_folder: A +image_b_folder: B +splits: + train: train + val: val + test: test +batch_size: 8 +num_workers: 4 +eval: + threshold: 0.5 + sweep_val_threshold: true + threshold_min: 0.05 + threshold_max: 0.95 + threshold_step: 0.05 + boundary_tolerance: 2 diff --git a/configs/datasets/whu_cd.yaml b/configs/datasets/whu_cd.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bbcb74a93e0963c6160235069260b9553f2563c3 --- /dev/null +++ b/configs/datasets/whu_cd.yaml @@ -0,0 +1,29 @@ +name: whu_cd +data_root: ${DATA_ROOT}/WHU-CD +source_name: WHU-CD +img_size: 256 +num_classes: 2 +ignore_index: 255 +channels: 3 +mean_a: [0.485, 0.456, 0.406] +std_a: [0.229, 0.224, 0.225] +mean_b: [0.485, 0.456, 0.406] +std_b: [0.229, 0.224, 0.225] +mask_threshold: 127 +mask_folder: OUT +image_a_folder: A +image_b_folder: B +splits: + train: train + val: val + test: test +batch_size: 8 +num_workers: 4 +eval: + threshold: 0.5 + sweep_val_threshold: true + threshold_min: 0.05 + threshold_max: 0.95 + threshold_step: 0.05 + boundary_tolerance: 2 + diff --git a/configs/datasets/wildfire_s2.yaml b/configs/datasets/wildfire_s2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3c65a2fc5318328142c32145aa5c15a5262af48c --- /dev/null +++ b/configs/datasets/wildfire_s2.yaml @@ -0,0 +1,29 @@ +name: wildfire_s2 +data_root: ${DATA_ROOT}/WildFireS2 +source_name: WildFireS2 +img_size: 256 +num_classes: 2 +ignore_index: 255 +channels: 3 +mean_a: [0.485, 0.456, 0.406] +std_a: [0.229, 0.224, 0.225] +mean_b: [0.485, 0.456, 0.406] +std_b: [0.229, 0.224, 0.225] +mask_threshold: 127 +mask_folder: label +image_a_folder: A +image_b_folder: B +splits: + train: train + val: val + test: test +batch_size: 8 +num_workers: 4 +eval: + threshold: 0.5 + sweep_val_threshold: true + threshold_min: 0.05 + threshold_max: 0.95 + threshold_step: 0.05 + boundary_tolerance: 2 + diff --git a/configs/models/bifa.yaml b/configs/models/bifa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6d10fd68be4d7d38503763d14246afadd6a67b4d --- /dev/null +++ b/configs/models/bifa.yaml @@ -0,0 +1,11 @@ +name: bifa +num_epochs: 200 +optimizer: adam +lr: 0.0001 +weight_decay: 0.0 +scheduler: linear +loss: ce_dice +backbone: MixTransformer +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/bit_cd.yaml b/configs/models/bit_cd.yaml new file mode 100644 index 0000000000000000000000000000000000000000..44afc2cb206e2bda0c7cada9878c9f969468c566 --- /dev/null +++ b/configs/models/bit_cd.yaml @@ -0,0 +1,11 @@ +name: bit_cd +num_epochs: 200 +optimizer: sgd +lr: 0.01 +weight_decay: 0.0005 +scheduler: cosine +loss: cross_entropy +backbone: ResNet-18 + base_transformer_pos_s4_dd8 +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/cdmamba.yaml b/configs/models/cdmamba.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1b092469a9bac13da87438058c0464fec50cd733 --- /dev/null +++ b/configs/models/cdmamba.yaml @@ -0,0 +1,11 @@ +name: cdmamba +num_epochs: 200 +optimizer: adam +lr: 0.0001 +weight_decay: 0.0 +scheduler: linear +loss: ce_dice +backbone: Mamba +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/cgnet.yaml b/configs/models/cgnet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..523ff6c6db785b20677af94bd55ed464509b6428 --- /dev/null +++ b/configs/models/cgnet.yaml @@ -0,0 +1,11 @@ +name: cgnet +num_epochs: 200 +optimizer: adam +lr: 0.0001 +weight_decay: 0.0001 +scheduler: cosine +loss: original_cgnet_loss +backbone: null +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/change3d.yaml b/configs/models/change3d.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3c9590700e439d179f399a3c37e649b9e7d67ae5 --- /dev/null +++ b/configs/models/change3d.yaml @@ -0,0 +1,11 @@ +name: change3d +num_epochs: 200 +optimizer: adam +lr: 0.0002 +weight_decay: 0.0001 +scheduler: poly +loss: bce_dice +backbone: X3D-L +pretrained_weights: Change3D/model/X3D_L.pyth +img_size: 256 + diff --git a/configs/models/changeformer.yaml b/configs/models/changeformer.yaml new file mode 100644 index 0000000000000000000000000000000000000000..53a7712e980ee735e483756bebe769c1399e2011 --- /dev/null +++ b/configs/models/changeformer.yaml @@ -0,0 +1,11 @@ +name: changeformer +num_epochs: 200 +optimizer: adamw +lr: 0.00006 +weight_decay: 0.01 +scheduler: cosine +loss: cross_entropy +backbone: MiT-b4 +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/changemamba.yaml b/configs/models/changemamba.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3de4c89eadff9fec3bd331c38c02dd66fe234cce --- /dev/null +++ b/configs/models/changemamba.yaml @@ -0,0 +1,14 @@ +name: changemamba +num_epochs: 200 +max_iters: 50000 +smoke_max_iters: 2 +optimizer: adamw +lr: 0.00006 +weight_decay: 0.01 +scheduler: poly +loss: mmseg_decode_head_loss +backbone: VMamba-Small +vmamba_variant: small +changemamba_model_type: MambaBCD_Small +pretrained_weights: model_repos/ChangeMamba/pretrained_weight/vssmsmall_dp03_ckpt_epoch_238.pth +img_size: 256 diff --git a/configs/models/changer.yaml b/configs/models/changer.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fd5e138a8519d3879d1b21516b6d3d2d7c96bffa --- /dev/null +++ b/configs/models/changer.yaml @@ -0,0 +1,11 @@ +name: changer +num_epochs: 200 +optimizer: adamw +lr: 0.00006 +weight_decay: 0.01 +scheduler: poly +loss: mmseg_decode_head_loss +backbone: ResNet-18 +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/dsamnet.yaml b/configs/models/dsamnet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fd97a40da06993d5520684826e9becac0c28a528 --- /dev/null +++ b/configs/models/dsamnet.yaml @@ -0,0 +1,11 @@ +name: dsamnet +num_epochs: 200 +optimizer: adam +lr: 0.0001 +weight_decay: 0.0001 +scheduler: step +loss: contrastive_triplet_bce_original +backbone: ResNet +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/dsifn.yaml b/configs/models/dsifn.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5129dda32aec7c9d9c6df9082ebbbf65a6084fad --- /dev/null +++ b/configs/models/dsifn.yaml @@ -0,0 +1,11 @@ +name: dsifn +num_epochs: 200 +optimizer: adam +lr: 0.0001 +weight_decay: 0.0001 +scheduler: step +loss: dsifn_original +backbone: VGG-16 +pretrained_weights: model_repos/DSIFN/pretrained/vgg16.pth +img_size: 256 + diff --git a/configs/models/elgcnet.yaml b/configs/models/elgcnet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2241a73162e73d649869008cce6dd29843ed82cf --- /dev/null +++ b/configs/models/elgcnet.yaml @@ -0,0 +1,11 @@ +name: elgcnet +num_epochs: 200 +optimizer: sgd +lr: 0.01 +weight_decay: 0.0005 +scheduler: cosine +loss: cross_entropy +backbone: ResNet-18 +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/fc_ef.yaml b/configs/models/fc_ef.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ea73b21207aa8f1f68bf0fe36c3b2c68983d7760 --- /dev/null +++ b/configs/models/fc_ef.yaml @@ -0,0 +1,11 @@ +name: fc_ef +num_epochs: 200 +optimizer: adam +lr: 0.0001 +weight_decay: 0.0 +scheduler: step +loss: bce_dice +backbone: null +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/fc_siam_conc.yaml b/configs/models/fc_siam_conc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b0efbca6029109be7e91f1ee54a1b0ad410bd5e2 --- /dev/null +++ b/configs/models/fc_siam_conc.yaml @@ -0,0 +1,11 @@ +name: fc_siam_conc +num_epochs: 200 +optimizer: adam +lr: 0.0001 +weight_decay: 0.0 +scheduler: step +loss: bce_dice +backbone: null +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/fc_siam_diff.yaml b/configs/models/fc_siam_diff.yaml new file mode 100644 index 0000000000000000000000000000000000000000..74990b5c59cf6175481b67b7b40d658ceaecf1c4 --- /dev/null +++ b/configs/models/fc_siam_diff.yaml @@ -0,0 +1,11 @@ +name: fc_siam_diff +num_epochs: 200 +optimizer: adam +lr: 0.0001 +weight_decay: 0.0 +scheduler: step +loss: bce_dice +backbone: null +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/hanet.yaml b/configs/models/hanet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6b54b0988bd1ed19f2a3d887177a33e1be56c611 --- /dev/null +++ b/configs/models/hanet.yaml @@ -0,0 +1,11 @@ +name: hanet +num_epochs: 200 +optimizer: adam +lr: 0.0001 +weight_decay: 0.0001 +scheduler: cosine +loss: original_hanet_loss +backbone: null +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/ifnet.yaml b/configs/models/ifnet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..17aaef717e83a6cd8368d32071daf2e56ca3b013 --- /dev/null +++ b/configs/models/ifnet.yaml @@ -0,0 +1,11 @@ +name: ifnet +num_epochs: 200 +optimizer: adam +lr: 0.0001 +weight_decay: 0.0001 +scheduler: step +loss: dsifn_cd_loss +backbone: VGG-16 +pretrained_weights: torchvision://vgg16 +img_size: 256 + diff --git a/configs/models/registry.yaml b/configs/models/registry.yaml new file mode 100644 index 0000000000000000000000000000000000000000..95023cecf07f33e222e0908a7b6247ef613ed536 --- /dev/null +++ b/configs/models/registry.yaml @@ -0,0 +1,106 @@ +models: + bifa: + script: train/train_bifa.py + source_path: BiFA + framework: pytorch + pretrained_backbone: MixTransformer + bit_cd: + script: train/train_bit_cd.py + source_path: BIT_CD + framework: pytorch + pretrained_backbone: ResNet-18 + cdmamba: + script: train/train_cdmamba.py + source_path: CDMamba + framework: pytorch + pretrained_backbone: Mamba + change3d: + script: train/train_change3d.py + source_path: Change3D + framework: pytorch + pretrained_backbone: X3D-L + changeformer: + script: train/train_changeformer.py + source_path: ChangeFormer + framework: pytorch + pretrained_backbone: MiT-b4 + ifnet: + script: train/train_ifnet.py + source_path: IFNet + framework: pytorch + pretrained_backbone: VGG-16 + rsm_cd: + script: train/train_rsm_cd.py + source_path: RSM-CD/change_detection_mamba + framework: pytorch + pretrained_backbone: VMamba + schanger: + script: train/train_schanger.py + source_path: SChanger + framework: pytorch + pretrained_backbone: null + siam_nestedunet: + script: train/train_siam_nestedunet.py + source_path: Siam-NestedUNet + framework: pytorch + pretrained_backbone: null + stanet: + script: train/train_stanet.py + source_path: STANet + framework: pytorch + pretrained_backbone: ResNet-18 + fc_ef: + script: train/train_fc_variants.py + source_path: model_repos/fully_convolutional_change_detection + framework: pytorch + pretrained_backbone: null + fc_siam_conc: + script: train/train_fc_variants.py + source_path: model_repos/fully_convolutional_change_detection + framework: pytorch + pretrained_backbone: null + fc_siam_diff: + script: train/train_fc_variants.py + source_path: model_repos/fully_convolutional_change_detection + framework: pytorch + pretrained_backbone: null + dsifn: + script: train/train_dsifn.py + source_path: IFNet + framework: pytorch + pretrained_backbone: VGG-16 + changemamba: + script: train/train_changemamba.py + source_path: model_repos/ChangeMamba + framework: mmsegmentation + pretrained_backbone: VMamba + elgcnet: + script: train/train_elgcnet.py + source_path: model_repos/elgcnet + framework: pytorch + pretrained_backbone: ResNet-18 + changer: + script: train/train_changer.py + source_path: model_repos/open-cd + framework: mmsegmentation + pretrained_backbone: ResNet-18 + hanet: + script: train/train_hanet.py + source_path: model_repos/HANet-CD + framework: pytorch + pretrained_backbone: null + cgnet: + script: train/train_cgnet.py + source_path: model_repos/CGNet-CD + framework: pytorch + pretrained_backbone: null + dsamnet: + script: train/train_dsamnet.py + source_path: model_repos/DSAMNet + framework: pytorch + pretrained_backbone: ResNet + tinycd: + script: train/train_tinycd.py + source_path: model_repos/Tiny_model_4_CD + framework: pytorch + pretrained_backbone: EfficientNet-B4 diff --git a/configs/models/rsm_cd.yaml b/configs/models/rsm_cd.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8b22a79501540c2b18a4a903ba38f49c3fd1777c --- /dev/null +++ b/configs/models/rsm_cd.yaml @@ -0,0 +1,11 @@ +name: rsm_cd +num_epochs: 200 +optimizer: adamw +lr: 0.001 +weight_decay: 0.001 +scheduler: reduce_on_plateau +loss: fccdn_loss_without_seg +backbone: VMamba/RSM-CD tiny +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/schanger.yaml b/configs/models/schanger.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f9b67a50bde8ba8365bd6cf6aab72f283e2ca5a4 --- /dev/null +++ b/configs/models/schanger.yaml @@ -0,0 +1,11 @@ +name: schanger +num_epochs: 200 +optimizer: adamw +lr: 0.00006 +weight_decay: 0.0001 +scheduler: cosine +loss: bce_deep_supervision +backbone: SChanger-base +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/siam_nestedunet.yaml b/configs/models/siam_nestedunet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5c3bdbbcea16e623f4f57394709ee60d1064f3d0 --- /dev/null +++ b/configs/models/siam_nestedunet.yaml @@ -0,0 +1,11 @@ +name: siam_nestedunet +num_epochs: 200 +optimizer: adam +lr: 0.00015 +weight_decay: 0.0001 +scheduler: step +loss: cross_entropy +backbone: UNet++ +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/stanet.yaml b/configs/models/stanet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bd6d970a4385ce9cb5e8d3f9f46baf57b4bf04c2 --- /dev/null +++ b/configs/models/stanet.yaml @@ -0,0 +1,11 @@ +name: stanet +num_epochs: 200 +optimizer: adam +lr: 0.001 +weight_decay: 0.0 +scheduler: cosine +loss: bcl_contrastive +backbone: ResNet-18 + PAM +pretrained_weights: null +img_size: 256 + diff --git a/configs/models/tinycd.yaml b/configs/models/tinycd.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9445a9d405d7799c64c493f0b8eb37f0ad2f009b --- /dev/null +++ b/configs/models/tinycd.yaml @@ -0,0 +1,11 @@ +name: tinycd +num_epochs: 200 +optimizer: adamw +lr: 0.0001 +weight_decay: 0.01 +scheduler: cosine +loss: bce_dice +backbone: EfficientNet-B4 +pretrained_weights: model_repos/TinyCD/pretrained/efficientnet_b4.pth +img_size: 256 + diff --git a/datasets/__init__.py b/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b849f773efa6e1691a29b3ca840c240a3247ff62 --- /dev/null +++ b/datasets/__init__.py @@ -0,0 +1,4 @@ +from .cd_dataset import CDDataset, build_dataloader + +__all__ = ["CDDataset", "build_dataloader"] + diff --git a/datasets/cd_dataset.py b/datasets/cd_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..ca534484b9af447741b7e952e940002aa63ec6df --- /dev/null +++ b/datasets/cd_dataset.py @@ -0,0 +1,198 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Dict, Iterable, Optional + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from utils.dataset_cache import dataloader_kwargs, print_dataloader_policy + +try: + from torchvision import transforms +except Exception: # pragma: no cover - torchvision import errors are environment-specific. + transforms = None + + +IMG_EXTS = {".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp"} +MASK_DIR_CANDIDATES = ("Mask", "mask", "label", "labels", "OUT", "gt") + + +def _scan(folder: Path) -> Dict[str, Path]: + return { + p.stem: p + for p in sorted(folder.iterdir()) + if p.is_file() and not p.name.startswith(".") and p.suffix.lower() in IMG_EXTS + } + + +def _resolve(root: Path, value: str | Path) -> Path: + p = Path(value) + return p if p.is_absolute() else root / p + + +def _configured_dirs(root: Path, split: str, cfg: dict) -> Optional[tuple[Path, Path, Path]]: + if "splits" in cfg: + split_dir = root / cfg.get("splits", {}).get(split, split) + a_dir = split_dir / cfg.get("image_a_folder", "A") + b_dir = split_dir / cfg.get("image_b_folder", "B") + m_dir = split_dir / cfg.get("mask_folder", "label") + return a_dir, b_dir, m_dir + source = cfg.get("split", {}).get("source_folders", {}) + a_rel = source.get(f"{split}_a") or source.get("a") + b_rel = source.get(f"{split}_b") or source.get("b") + m_rel = source.get(f"{split}_mask") or source.get("mask") + if not (a_rel and b_rel and m_rel): + return None + return _resolve(root, a_rel), _resolve(root, b_rel), _resolve(root, m_rel) + + +def _mask_dir(split_dir: Path) -> Path: + for name in MASK_DIR_CANDIDATES: + candidate = split_dir / name + if candidate.is_dir(): + return candidate + tried = ", ".join(MASK_DIR_CANDIDATES) + raise FileNotFoundError(f"No mask directory under {split_dir}; tried {tried}") + + +class CDDataset(Dataset): + """Configurable binary change-detection dataset. + + This is the shared root loader for CD-Models. It intentionally supports the + folder conventions found in mamba-cd and the nested research repos instead + of adding another WildFire-specific loader. + """ + + def __init__( + self, + root: str | Path, + split: str, + cfg: Optional[dict] = None, + image_size: Optional[int] = None, + normalize: bool = True, + return_format: str = "dict", + ) -> None: + self.root = Path(root) + self.split = split + self.cfg = cfg or {} + self.return_format = return_format + ds_cfg = self.cfg.get("dataset", self.cfg) + self.image_size = int(image_size or ds_cfg.get("image_size", ds_cfg.get("img_size", 256))) + self.threshold = int(ds_cfg.get("binary_threshold", ds_cfg.get("mask_threshold", 127))) + self.mean = ds_cfg.get("mean", ds_cfg.get("mean_a", [0.485, 0.456, 0.406])) + self.std = ds_cfg.get("std", ds_cfg.get("std_a", [0.229, 0.224, 0.225])) + if not self.root.is_dir(): + dataset_name = ds_cfg.get("_dataset_name", ds_cfg.get("name", "unknown")) + raise FileNotFoundError( + f"\n{'=' * 60}\n" + f"Dataset root directory not found:\n {self.root}\n\n" + f"For dataset '{dataset_name}', either:\n" + f" 1. Download the dataset and place it at the above path\n" + f" 2. Update DATA_ROOT: export DATA_ROOT=/correct/path\n" + f" 3. Edit configs/datasets/{dataset_name}.yaml\n" + f"{'=' * 60}" + ) + + configured = _configured_dirs(self.root, split, self.cfg) + if configured is None: + split_dir = self.root / split + a_dir = split_dir / "A" + b_dir = split_dir / "B" + m_dir = _mask_dir(split_dir) + else: + a_dir, b_dir, m_dir = configured + + for label, folder in (("A", a_dir), ("B", b_dir), ("mask", m_dir)): + if not folder.is_dir(): + raise FileNotFoundError(f"{label} directory not found: {folder}") + + a_files = _scan(a_dir) + b_files = _scan(b_dir) + m_files = _scan(m_dir) + stems = sorted(set(a_files) & set(b_files) & set(m_files)) + if not stems: + raise RuntimeError(f"No matched A/B/mask triplets found for {self.root} split {split}") + self.samples = [(a_files[s], b_files[s], m_files[s], s) for s in stems] + + if transforms is None: + self.image_tf = None + else: + ops: list = [transforms.Resize((self.image_size, self.image_size)), transforms.ToTensor()] + if normalize: + ops.append(transforms.Normalize(mean=self.mean, std=self.std)) + self.image_tf = transforms.Compose(ops) + + def __len__(self) -> int: + return len(self.samples) + + def _image(self, path: Path) -> torch.Tensor: + img = Image.open(path).convert("RGB") + if self.image_tf is not None: + return self.image_tf(img) + img = img.resize((self.image_size, self.image_size), Image.BILINEAR) + arr = np.array(img, dtype=np.float32) / 255.0 + return torch.from_numpy(arr).permute(2, 0, 1) + + def _mask(self, path: Path) -> torch.Tensor: + mask = Image.open(path).convert("L").resize((self.image_size, self.image_size), Image.NEAREST) + arr = np.array(mask, dtype=np.uint8) + if arr.max() <= 1: + bin_mask = (arr > 0).astype(np.float32) + else: + bin_mask = (arr > self.threshold).astype(np.float32) + return torch.from_numpy(bin_mask).unsqueeze(0) + + def __getitem__(self, index: int): + a_path, b_path, mask_path, name = self.samples[index] + a = self._image(a_path) + b = self._image(b_path) + mask = self._mask(mask_path) + if self.return_format == "tuple": + return a, b, mask, name + if self.return_format == "legacy": + return {"A": a, "B": b, "L": mask.squeeze(0).long(), "name": name} + return {"a": a, "b": b, "mask": mask, "name": name} + + +def build_dataloader( + cfg: dict, + split: str, + shuffle: Optional[bool] = None, + return_format: str = "dict", +) -> DataLoader: + ds_cfg = cfg.get("dataset", cfg) + dataset = CDDataset( + ds_cfg.get("root", ds_cfg["data_root"]), + split=split, + cfg=cfg, + image_size=int(ds_cfg.get("image_size", ds_cfg.get("img_size", 256))), + return_format=return_format, + ) + batch_size = int(ds_cfg.get("batch_size", 8)) + num_workers = int(ds_cfg.get("num_workers", 4)) + if shuffle is None: + shuffle = split == "train" + print_dataloader_policy({**ds_cfg, "num_workers": num_workers}, torch.cuda.is_available()) + return DataLoader( + dataset, + batch_size=batch_size, + shuffle=shuffle, + **dataloader_kwargs({**ds_cfg, "num_workers": num_workers}, torch.cuda.is_available()), + drop_last=split == "train", + ) + + +def available_splits(cfg: dict) -> Iterable[str]: + ds_cfg = cfg.get("dataset", cfg) + root = Path(ds_cfg.get("root", ds_cfg["data_root"])) + if "splits" in cfg: + yield from cfg["splits"] + return + source = cfg.get("split", {}).get("source_folders", {}) + for split in ("train", "val", "test"): + if source.get(f"{split}_a") and source.get(f"{split}_b") and source.get(f"{split}_mask"): + yield split + elif (root / split).is_dir(): + yield split diff --git a/evaluate.py b/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..318c54bab8c9cf498fb972fde0e2d130f2f11e7e --- /dev/null +++ b/evaluate.py @@ -0,0 +1,142 @@ +""" +evaluate.py - Run test sweep on a trained cd-model checkpoint. + +Usage: + python evaluate.py --model changemamba --dataset levir_cd + python evaluate.py --model all --dataset all + python evaluate.py --checkpoint results/bifa/levir_cd/checkpoints/best_model.pth --model bifa --dataset levir_cd +""" +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import torch +import yaml + +from utils.config_loader import load_dataset_config, load_model_config +from utils.gpu_utils import print_gpu_diagnostics, resolve_gpu +from utils.model_adapters import get_model_adapter +from utils.results_writer import append_to_comparison_table +from utils.unified_evaluator import evaluate_with_adapter + + +ROOT = Path(__file__).resolve().parent +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +class ModelEvaluationUnavailable(RuntimeError): + pass + + +def _load_datasets() -> list[str]: + return sorted(path.stem for path in (ROOT / "configs" / "datasets").glob("*.yaml")) + + +def _load_registry() -> dict: + with (ROOT / "configs" / "models" / "registry.yaml").open("r", encoding="utf-8") as f: + return yaml.safe_load(f)["models"] + + +def _best_checkpoint(model: str, dataset: str) -> Path: + ckpt_dir = ROOT / "results" / model / dataset / "checkpoints" + direct = ckpt_dir / "best_model.pth" + if direct.exists(): + return direct + candidates = ( + sorted(ckpt_dir.glob("*F1*.pth")) + + sorted(ckpt_dir.glob("*.pth")) + + sorted(ckpt_dir.glob("**/best_ckpt.pth")) + + sorted(ckpt_dir.glob("**/best_ckpt.pt")) + + sorted(ckpt_dir.glob("**/*best*.pth")) + + sorted(ckpt_dir.glob("**/*best*.pt")) + ) + if not candidates: + raise FileNotFoundError(f"No checkpoint found under {ckpt_dir}") + return candidates[-1] + + +def _parse_args() -> argparse.Namespace: + registry = _load_registry() + datasets = _load_datasets() + parser = argparse.ArgumentParser(description="Run a test sweep on a trained cd-model checkpoint.") + parser.add_argument("--model", required=True, choices=sorted(registry) + ["all"]) + parser.add_argument("--dataset", required=True, choices=datasets + ["all"]) + parser.add_argument("--checkpoint", default=None) + parser.add_argument("--gpu", default="0") + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--allow-missing-profilers", action="store_true") + return parser.parse_args() + + +def _evaluate_one( + model: str, + dataset: str, + checkpoint: str | None, + gpu: str, + dry_run: bool, + allow_missing_profilers: bool, +) -> int: + cfg = load_dataset_config(dataset) + gpu_resolution = resolve_gpu(gpu) + print_gpu_diagnostics(gpu_resolution) + if dry_run: + print(f"[EVAL-DRY-RUN] model={model} dataset={dataset} data_root={cfg['data_root']}") + return 0 + ckpt = Path(checkpoint) if checkpoint else _best_checkpoint(model, dataset) + if not ckpt.is_absolute(): + ckpt = ROOT / ckpt + print(f"[EVAL] model={model} dataset={dataset} checkpoint={ckpt}") + adapter = get_model_adapter(model) + if not adapter.supports_inprocess_eval: + raise ModelEvaluationUnavailable(adapter.notes_or_failure_reason) + device = torch.device(gpu_resolution.local_device) + _, code = evaluate_with_adapter( + model_name=model, + dataset_cfg=cfg, + model_config=load_model_config(model), + adapter=adapter, + checkpoint_path=ckpt, + device=device, + strict_profiling=not allow_missing_profilers, + ) + return code + + +def main() -> int: + args = _parse_args() + models = sorted(_load_registry()) if args.model == "all" else [args.model] + datasets = _load_datasets() if args.dataset == "all" else [args.dataset] + rows = [] + for model in models: + for dataset in datasets: + try: + code = _evaluate_one( + model, + dataset, + args.checkpoint, + args.gpu, + args.dry_run, + args.allow_missing_profilers, + ) + rows.append((model, dataset, "complete" if code == 0 else "failed")) + except FileNotFoundError as exc: + print(f"[MISSING] {model}/{dataset}: {exc}") + rows.append((model, dataset, "missing")) + except ModelEvaluationUnavailable as exc: + print(f"[UNAVAILABLE] {model}/{dataset}: {exc}") + rows.append((model, dataset, "unavailable")) + except RuntimeError as exc: + print(f"[FAILED] {model}/{dataset}: {exc}") + rows.append((model, dataset, "failed")) + append_to_comparison_table() + print("\nModel | Dataset | Status") + print("--- | --- | ---") + for model, dataset, status in rows: + print(f"{model} | {dataset} | {status}") + return 0 if all(status == "complete" for _, _, status in rows) else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/generated_configs/dsifn_cd__bifa.json b/generated_configs/dsifn_cd__bifa.json new file mode 100644 index 0000000000000000000000000000000000000000..0eba1d5968c7f89b577e2d27024d420f8edbcd09 --- /dev/null +++ b/generated_configs/dsifn_cd__bifa.json @@ -0,0 +1,61 @@ +{ + "name": "dsifn_cd-train-bifa", + "phase": "train", + "gpu_ids": [ + 0 + ], + "path_cd": { + "log": "logs", + "result": "results", + "checkpoint": "checkpoint", + "resume_state": null + }, + "datasets": { + "train": { + "name": "DSIFN-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/dsifn_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": true, + "data_len": -1 + }, + "val": { + "name": "DSIFN-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/dsifn_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + }, + "test": { + "name": "DSIFN-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/dsifn_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + } + }, + "model": { + "name": "bifa", + "loss": "ce_dice" + }, + "train": { + "n_epoch": 200, + "train_print_iter": 50, + "val_freq": 1, + "val_print_iter": 20, + "optimizer": { + "type": "adam", + "lr": 0.0001 + }, + "sheduler": { + "lr_policy": "linear", + "n_step": 3, + "gamma": 0.1 + } + } +} \ No newline at end of file diff --git a/generated_configs/dsifn_cd__cdmamba.json b/generated_configs/dsifn_cd__cdmamba.json new file mode 100644 index 0000000000000000000000000000000000000000..692e635d4bc820a2acda72bf39f927e702c8afd6 --- /dev/null +++ b/generated_configs/dsifn_cd__cdmamba.json @@ -0,0 +1,91 @@ +{ + "name": "dsifn_cd-train-cdmamba", + "phase": "train", + "gpu_ids": [ + 0 + ], + "path_cd": { + "log": "logs", + "result": "results", + "checkpoint": "checkpoint", + "resume_state": null + }, + "datasets": { + "train": { + "name": "DSIFN-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/dsifn_cd/legacy_list", + "resolution": 256, + "num_workers": 2, + "batch_size": 8, + "use_shuffle": true, + "data_len": -1 + }, + "val": { + "name": "DSIFN-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/dsifn_cd/legacy_list", + "resolution": 256, + "num_workers": 2, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + }, + "test": { + "name": "DSIFN-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/dsifn_cd/legacy_list", + "resolution": 256, + "num_workers": 2, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + } + }, + "model": { + "name": "cdmamba", + "loss": "ce_dice", + "init_filters": 16, + "n_classes": 2, + "mode": "AGLGF", + "conv_mode": "orignal_dinner", + "local_query_model": "orignal_dinner", + "up_mode": "SRCM", + "up_conv_mode": "deepwise", + "spatial_dims": 2, + "in_channels": 3, + "resdiual": false, + "blocks_down": [ + 1, + 2, + 2, + 4 + ], + "blocks_up": [ + 1, + 1, + 1 + ], + "diff_abs": "later", + "stage": 2, + "mamba_act": "relu", + "norm": [ + "GROUP", + { + "num_groups": 8 + } + ] + }, + "train": { + "n_epoch": 200, + "train_print_iter": 50, + "val_freq": 1, + "val_print_iter": 20, + "optimizer": { + "type": "adam", + "lr": 0.0001 + }, + "sheduler": { + "lr_policy": "linear", + "n_step": 3, + "gamma": 0.1 + } + } +} \ No newline at end of file diff --git a/generated_configs/dsifn_cd__changer_opencd.py b/generated_configs/dsifn_cd__changer_opencd.py new file mode 100644 index 0000000000000000000000000000000000000000..53f3274c3febef8b6493b40e717227bdd062214f --- /dev/null +++ b/generated_configs/dsifn_cd__changer_opencd.py @@ -0,0 +1,26 @@ +_base_ = '/new-home/buddhiw/CD-Models/model_repos/open-cd/configs/changer/changer_ex_r18_512x512_40k_levircd.py' + +dataset_type = 'DSIFN_Dataset' +data_root = r'/new-home/buddhiw/CD-Models/generated_dataset_views/dsifn_cd/opencd' +crop_size = (256, 256) + +train_dataloader = dict( + batch_size=8, + num_workers=4, + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(seg_map_path='train/label', img_path_from='train/A', img_path_to='train/B'))) +val_dataloader = dict( + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(seg_map_path='val/label', img_path_from='val/A', img_path_to='val/B'))) +test_dataloader = dict( + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(seg_map_path='test/label', img_path_from='test/A', img_path_to='test/B'))) + +train_cfg = dict(type='IterBasedTrainLoop', max_iters=90000, val_interval=450) +work_dir = r'/new-home/buddhiw/CD-Models/results/changer/dsifn_cd/work_dir' diff --git a/generated_configs/dsifn_cd__hanet_metadata.json b/generated_configs/dsifn_cd__hanet_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..6d3ecdf917036636c81012ce4f4de6fc2fecf95d --- /dev/null +++ b/generated_configs/dsifn_cd__hanet_metadata.json @@ -0,0 +1,19 @@ +{ + "patch_size": 256, + "augmentation": true, + "num_gpus": 1, + "num_workers": 4, + "num_channel": 3, + "EF": false, + "epochs": 200, + "epochs_threshold": 15, + "gamma": 0.5, + "weight_decay": 0.0001, + "batch_size": 8, + "learning_rate": 0.0001, + "loss_function": "hybrid", + "dataset_dir": "/new-home/buddhiw/CD-Models/generated_dataset_views/dsifn_cd/hanet_matched/", + "weight_dir": "/new-home/buddhiw/CD-Models/results/hanet/dsifn_cd/weights/", + "Output_dir": "/new-home/buddhiw/CD-Models/results/hanet/dsifn_cd/outputs/", + "log_dir": "/new-home/buddhiw/CD-Models/results/hanet/dsifn_cd/logs" +} \ No newline at end of file diff --git a/generated_configs/kate_cd__bifa.json b/generated_configs/kate_cd__bifa.json new file mode 100644 index 0000000000000000000000000000000000000000..92787f151e672c1e33964cf4e1b83424c198cbe0 --- /dev/null +++ b/generated_configs/kate_cd__bifa.json @@ -0,0 +1,61 @@ +{ + "name": "kate_cd-train-bifa", + "phase": "train", + "gpu_ids": [ + 0 + ], + "path_cd": { + "log": "logs", + "result": "results", + "checkpoint": "checkpoint", + "resume_state": null + }, + "datasets": { + "train": { + "name": "KATE-CD-256", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/kate_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": true, + "data_len": -1 + }, + "val": { + "name": "KATE-CD-256", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/kate_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + }, + "test": { + "name": "KATE-CD-256", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/kate_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + } + }, + "model": { + "name": "bifa", + "loss": "ce_dice" + }, + "train": { + "n_epoch": 200, + "train_print_iter": 50, + "val_freq": 1, + "val_print_iter": 20, + "optimizer": { + "type": "adam", + "lr": 0.0001 + }, + "sheduler": { + "lr_policy": "linear", + "n_step": 3, + "gamma": 0.1 + } + } +} \ No newline at end of file diff --git a/generated_configs/levir_cd__bifa.json b/generated_configs/levir_cd__bifa.json new file mode 100644 index 0000000000000000000000000000000000000000..b30a9cb5bb5d8b739f348e30d78d581557cd4a0d --- /dev/null +++ b/generated_configs/levir_cd__bifa.json @@ -0,0 +1,61 @@ +{ + "name": "levir_cd-train-bifa", + "phase": "train", + "gpu_ids": [ + 0 + ], + "path_cd": { + "log": "logs", + "result": "results", + "checkpoint": "checkpoint", + "resume_state": null + }, + "datasets": { + "train": { + "name": "LEVIR-CD+", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": true, + "data_len": -1 + }, + "val": { + "name": "LEVIR-CD+", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + }, + "test": { + "name": "LEVIR-CD+", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + } + }, + "model": { + "name": "bifa", + "loss": "ce_dice" + }, + "train": { + "n_epoch": 200, + "train_print_iter": 50, + "val_freq": 1, + "val_print_iter": 20, + "optimizer": { + "type": "adam", + "lr": 0.0001 + }, + "sheduler": { + "lr_policy": "linear", + "n_step": 3, + "gamma": 0.1 + } + } +} \ No newline at end of file diff --git a/generated_configs/levir_cd__cdmamba.json b/generated_configs/levir_cd__cdmamba.json new file mode 100644 index 0000000000000000000000000000000000000000..d52d9b92b0cef3e9b9e864d1da5a02818c3d797a --- /dev/null +++ b/generated_configs/levir_cd__cdmamba.json @@ -0,0 +1,91 @@ +{ + "name": "levir_cd-train-cdmamba", + "phase": "train", + "gpu_ids": [ + 0 + ], + "path_cd": { + "log": "logs", + "result": "results", + "checkpoint": "checkpoint", + "resume_state": null + }, + "datasets": { + "train": { + "name": "LEVIR-CD+", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd/legacy_list", + "resolution": 256, + "num_workers": 1, + "batch_size": 8, + "use_shuffle": true, + "data_len": -1 + }, + "val": { + "name": "LEVIR-CD+", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd/legacy_list", + "resolution": 256, + "num_workers": 1, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + }, + "test": { + "name": "LEVIR-CD+", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd/legacy_list", + "resolution": 256, + "num_workers": 1, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + } + }, + "model": { + "name": "cdmamba", + "loss": "ce_dice", + "init_filters": 16, + "n_classes": 2, + "mode": "AGLGF", + "conv_mode": "orignal_dinner", + "local_query_model": "orignal_dinner", + "up_mode": "SRCM", + "up_conv_mode": "deepwise", + "spatial_dims": 2, + "in_channels": 3, + "resdiual": false, + "blocks_down": [ + 1, + 2, + 2, + 4 + ], + "blocks_up": [ + 1, + 1, + 1 + ], + "diff_abs": "later", + "stage": 2, + "mamba_act": "relu", + "norm": [ + "GROUP", + { + "num_groups": 8 + } + ] + }, + "train": { + "n_epoch": 200, + "train_print_iter": 50, + "val_freq": 1, + "val_print_iter": 20, + "optimizer": { + "type": "adam", + "lr": 0.0001 + }, + "sheduler": { + "lr_policy": "linear", + "n_step": 3, + "gamma": 0.1 + } + } +} \ No newline at end of file diff --git a/generated_configs/levir_cd__changer_opencd.py b/generated_configs/levir_cd__changer_opencd.py new file mode 100644 index 0000000000000000000000000000000000000000..6c63897372cd20278b4e745a6832ddb085b2227c --- /dev/null +++ b/generated_configs/levir_cd__changer_opencd.py @@ -0,0 +1,26 @@ +_base_ = '/new-home/buddhiw/CD-Models/model_repos/open-cd/configs/changer/changer_ex_r18_512x512_40k_levircd.py' + +dataset_type = 'DSIFN_Dataset' +data_root = r'/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd/opencd' +crop_size = (256, 256) + +train_dataloader = dict( + batch_size=8, + num_workers=4, + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(seg_map_path='train/label', img_path_from='train/A', img_path_to='train/B'))) +val_dataloader = dict( + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(seg_map_path='val/label', img_path_from='val/A', img_path_to='val/B'))) +test_dataloader = dict( + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(seg_map_path='test/label', img_path_from='test/A', img_path_to='test/B'))) + +train_cfg = dict(type='IterBasedTrainLoop', max_iters=204000, val_interval=1020) +work_dir = r'/new-home/buddhiw/CD-Models/results/changer/levir_cd/work_dir' diff --git a/generated_configs/levir_cd__hanet_metadata.json b/generated_configs/levir_cd__hanet_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..02fbf49b00136f99ce1622d70fa306fb3dd10e55 --- /dev/null +++ b/generated_configs/levir_cd__hanet_metadata.json @@ -0,0 +1,19 @@ +{ + "patch_size": 256, + "augmentation": true, + "num_gpus": 1, + "num_workers": 4, + "num_channel": 3, + "EF": false, + "epochs": 200, + "epochs_threshold": 15, + "gamma": 0.5, + "weight_decay": 0.0001, + "batch_size": 8, + "learning_rate": 0.0001, + "loss_function": "hybrid", + "dataset_dir": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd/hanet_matched/", + "weight_dir": "/new-home/buddhiw/CD-Models/results/hanet/levir_cd/weights/", + "Output_dir": "/new-home/buddhiw/CD-Models/results/hanet/levir_cd/outputs/", + "log_dir": "/new-home/buddhiw/CD-Models/results/hanet/levir_cd/logs" +} \ No newline at end of file diff --git a/generated_configs/levir_cd_test_as_val__bifa.json b/generated_configs/levir_cd_test_as_val__bifa.json new file mode 100644 index 0000000000000000000000000000000000000000..9bdeed23ca89095ce2da0e2ddfb2936a05d8c2e4 --- /dev/null +++ b/generated_configs/levir_cd_test_as_val__bifa.json @@ -0,0 +1,61 @@ +{ + "name": "levir_cd_test_as_val-train-bifa", + "phase": "train", + "gpu_ids": [ + 0 + ], + "path_cd": { + "log": "logs", + "result": "results", + "checkpoint": "checkpoint", + "resume_state": null + }, + "datasets": { + "train": { + "name": "LEVIR-CD+", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd_test_as_val/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": true, + "data_len": -1 + }, + "val": { + "name": "LEVIR-CD+", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd_test_as_val/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + }, + "test": { + "name": "LEVIR-CD+", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd_test_as_val/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + } + }, + "model": { + "name": "bifa", + "loss": "ce_dice" + }, + "train": { + "n_epoch": 200, + "train_print_iter": 50, + "val_freq": 1, + "val_print_iter": 20, + "optimizer": { + "type": "adam", + "lr": 0.0001 + }, + "sheduler": { + "lr_policy": "linear", + "n_step": 3, + "gamma": 0.1 + } + } +} \ No newline at end of file diff --git a/generated_configs/levir_cd_test_as_val__cdmamba.json b/generated_configs/levir_cd_test_as_val__cdmamba.json new file mode 100644 index 0000000000000000000000000000000000000000..7fd416b20d2d9b9e5a8db353220f4f48e9d053b7 --- /dev/null +++ b/generated_configs/levir_cd_test_as_val__cdmamba.json @@ -0,0 +1,91 @@ +{ + "name": "levir_cd_test_as_val-train-cdmamba", + "phase": "train", + "gpu_ids": [ + 0 + ], + "path_cd": { + "log": "logs", + "result": "results", + "checkpoint": "checkpoint", + "resume_state": null + }, + "datasets": { + "train": { + "name": "LEVIR-CD+", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd_test_as_val/legacy_list", + "resolution": 256, + "num_workers": 1, + "batch_size": 8, + "use_shuffle": true, + "data_len": -1 + }, + "val": { + "name": "LEVIR-CD+", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd_test_as_val/legacy_list", + "resolution": 256, + "num_workers": 1, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + }, + "test": { + "name": "LEVIR-CD+", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd_test_as_val/legacy_list", + "resolution": 256, + "num_workers": 1, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + } + }, + "model": { + "name": "cdmamba", + "loss": "ce_dice", + "init_filters": 16, + "n_classes": 2, + "mode": "AGLGF", + "conv_mode": "orignal_dinner", + "local_query_model": "orignal_dinner", + "up_mode": "SRCM", + "up_conv_mode": "deepwise", + "spatial_dims": 2, + "in_channels": 3, + "resdiual": false, + "blocks_down": [ + 1, + 2, + 2, + 4 + ], + "blocks_up": [ + 1, + 1, + 1 + ], + "diff_abs": "later", + "stage": 2, + "mamba_act": "relu", + "norm": [ + "GROUP", + { + "num_groups": 8 + } + ] + }, + "train": { + "n_epoch": 200, + "train_print_iter": 50, + "val_freq": 1, + "val_print_iter": 20, + "optimizer": { + "type": "adam", + "lr": 0.0001 + }, + "sheduler": { + "lr_policy": "linear", + "n_step": 3, + "gamma": 0.1 + } + } +} \ No newline at end of file diff --git a/generated_configs/levir_cd_test_as_val__changer_opencd.py b/generated_configs/levir_cd_test_as_val__changer_opencd.py new file mode 100644 index 0000000000000000000000000000000000000000..a227c17fe8a983147faa27eef7f2066cd2d8ef06 --- /dev/null +++ b/generated_configs/levir_cd_test_as_val__changer_opencd.py @@ -0,0 +1,26 @@ +_base_ = '/new-home/buddhiw/CD-Models/model_repos/open-cd/configs/changer/changer_ex_r18_512x512_40k_levircd.py' + +dataset_type = 'DSIFN_Dataset' +data_root = r'/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd_test_as_val/opencd' +crop_size = (256, 256) + +train_dataloader = dict( + batch_size=8, + num_workers=4, + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(seg_map_path='train/label', img_path_from='train/A', img_path_to='train/B'))) +val_dataloader = dict( + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(seg_map_path='val/label', img_path_from='val/A', img_path_to='val/B'))) +test_dataloader = dict( + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(seg_map_path='test/label', img_path_from='test/A', img_path_to='test/B'))) + +train_cfg = dict(type='IterBasedTrainLoop', max_iters=254800, val_interval=1274) +work_dir = r'/new-home/buddhiw/CD-Models/results/changer/levir_cd_test_as_val/work_dir' diff --git a/generated_configs/levir_cd_test_as_val__hanet_metadata.json b/generated_configs/levir_cd_test_as_val__hanet_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ff826ed40cd7b709de1d50d4c32cba5c80fc5e69 --- /dev/null +++ b/generated_configs/levir_cd_test_as_val__hanet_metadata.json @@ -0,0 +1,19 @@ +{ + "patch_size": 256, + "augmentation": true, + "num_gpus": 1, + "num_workers": 4, + "num_channel": 3, + "EF": false, + "epochs": 200, + "epochs_threshold": 15, + "gamma": 0.5, + "weight_decay": 0.0001, + "batch_size": 8, + "learning_rate": 0.0001, + "loss_function": "hybrid", + "dataset_dir": "/new-home/buddhiw/CD-Models/generated_dataset_views/levir_cd_test_as_val/hanet_matched/", + "weight_dir": "/new-home/buddhiw/CD-Models/results/hanet/levir_cd_test_as_val/weights/", + "Output_dir": "/new-home/buddhiw/CD-Models/results/hanet/levir_cd_test_as_val/outputs/", + "log_dir": "/new-home/buddhiw/CD-Models/results/hanet/levir_cd_test_as_val/logs" +} \ No newline at end of file diff --git a/generated_configs/sysu_cd__bifa.json b/generated_configs/sysu_cd__bifa.json new file mode 100644 index 0000000000000000000000000000000000000000..7aea2977a318e79a3fbe157fc3c8c9335627f5e4 --- /dev/null +++ b/generated_configs/sysu_cd__bifa.json @@ -0,0 +1,61 @@ +{ + "name": "sysu_cd-train-bifa", + "phase": "train", + "gpu_ids": [ + 0 + ], + "path_cd": { + "log": "logs", + "result": "results", + "checkpoint": "checkpoint", + "resume_state": null + }, + "datasets": { + "train": { + "name": "SYSU-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/sysu_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": true, + "data_len": -1 + }, + "val": { + "name": "SYSU-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/sysu_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + }, + "test": { + "name": "SYSU-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/sysu_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + } + }, + "model": { + "name": "bifa", + "loss": "ce_dice" + }, + "train": { + "n_epoch": 200, + "train_print_iter": 50, + "val_freq": 1, + "val_print_iter": 20, + "optimizer": { + "type": "adam", + "lr": 0.0001 + }, + "sheduler": { + "lr_policy": "linear", + "n_step": 3, + "gamma": 0.1 + } + } +} \ No newline at end of file diff --git a/generated_configs/whu_cd__bifa.json b/generated_configs/whu_cd__bifa.json new file mode 100644 index 0000000000000000000000000000000000000000..f1ce2ea3ace7f21970029d62b1fcb022092a825e --- /dev/null +++ b/generated_configs/whu_cd__bifa.json @@ -0,0 +1,61 @@ +{ + "name": "whu_cd-train-bifa", + "phase": "train", + "gpu_ids": [ + 0 + ], + "path_cd": { + "log": "logs", + "result": "results", + "checkpoint": "checkpoint", + "resume_state": null + }, + "datasets": { + "train": { + "name": "WHU-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/whu_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": true, + "data_len": -1 + }, + "val": { + "name": "WHU-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/whu_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + }, + "test": { + "name": "WHU-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/whu_cd/legacy_list", + "resolution": 256, + "num_workers": 4, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + } + }, + "model": { + "name": "bifa", + "loss": "ce_dice" + }, + "train": { + "n_epoch": 200, + "train_print_iter": 50, + "val_freq": 1, + "val_print_iter": 20, + "optimizer": { + "type": "adam", + "lr": 0.0001 + }, + "sheduler": { + "lr_policy": "linear", + "n_step": 3, + "gamma": 0.1 + } + } +} \ No newline at end of file diff --git a/generated_configs/whu_cd__cdmamba.json b/generated_configs/whu_cd__cdmamba.json new file mode 100644 index 0000000000000000000000000000000000000000..4597b96fa0bac3afdea0b5df50ef84ad4b0dd815 --- /dev/null +++ b/generated_configs/whu_cd__cdmamba.json @@ -0,0 +1,91 @@ +{ + "name": "whu_cd-train-cdmamba", + "phase": "train", + "gpu_ids": [ + 0 + ], + "path_cd": { + "log": "logs", + "result": "results", + "checkpoint": "checkpoint", + "resume_state": null + }, + "datasets": { + "train": { + "name": "WHU-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/whu_cd/legacy_list", + "resolution": 256, + "num_workers": 1, + "batch_size": 8, + "use_shuffle": true, + "data_len": -1 + }, + "val": { + "name": "WHU-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/whu_cd/legacy_list", + "resolution": 256, + "num_workers": 1, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + }, + "test": { + "name": "WHU-CD", + "datasetroot": "/new-home/buddhiw/CD-Models/generated_dataset_views/whu_cd/legacy_list", + "resolution": 256, + "num_workers": 1, + "batch_size": 8, + "use_shuffle": false, + "data_len": -1 + } + }, + "model": { + "name": "cdmamba", + "loss": "ce_dice", + "init_filters": 16, + "n_classes": 2, + "mode": "AGLGF", + "conv_mode": "orignal_dinner", + "local_query_model": "orignal_dinner", + "up_mode": "SRCM", + "up_conv_mode": "deepwise", + "spatial_dims": 2, + "in_channels": 3, + "resdiual": false, + "blocks_down": [ + 1, + 2, + 2, + 4 + ], + "blocks_up": [ + 1, + 1, + 1 + ], + "diff_abs": "later", + "stage": 2, + "mamba_act": "relu", + "norm": [ + "GROUP", + { + "num_groups": 8 + } + ] + }, + "train": { + "n_epoch": 200, + "train_print_iter": 50, + "val_freq": 1, + "val_print_iter": 20, + "optimizer": { + "type": "adam", + "lr": 0.0001 + }, + "sheduler": { + "lr_policy": "linear", + "n_step": 3, + "gamma": 0.1 + } + } +} \ No newline at end of file diff --git a/generated_configs/whu_cd__hanet_metadata.json b/generated_configs/whu_cd__hanet_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..196af9bad301ae70878efa5000fad6f3b6179219 --- /dev/null +++ b/generated_configs/whu_cd__hanet_metadata.json @@ -0,0 +1,19 @@ +{ + "patch_size": 256, + "augmentation": true, + "num_gpus": 1, + "num_workers": 4, + "num_channel": 3, + "EF": false, + "epochs": 200, + "epochs_threshold": 15, + "gamma": 0.5, + "weight_decay": 0.0001, + "batch_size": 8, + "learning_rate": 0.0001, + "loss_function": "hybrid", + "dataset_dir": "/new-home/buddhiw/CD-Models/generated_dataset_views/whu_cd/hanet/", + "weight_dir": "/new-home/buddhiw/CD-Models/results/hanet/whu_cd/weights/", + "Output_dir": "/new-home/buddhiw/CD-Models/results/hanet/whu_cd/outputs/", + "log_dir": "/new-home/buddhiw/CD-Models/results/hanet/whu_cd/logs" +} \ No newline at end of file diff --git a/generated_configs/wildfire_s2__hanet_metadata.json b/generated_configs/wildfire_s2__hanet_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..8513e7f17ce1ed0310a8d4e241652a8a6f0f5a32 --- /dev/null +++ b/generated_configs/wildfire_s2__hanet_metadata.json @@ -0,0 +1,19 @@ +{ + "patch_size": 256, + "augmentation": true, + "num_gpus": 1, + "num_workers": 4, + "num_channel": 3, + "EF": false, + "epochs": 200, + "epochs_threshold": 15, + "gamma": 0.5, + "weight_decay": 0.0001, + "batch_size": 8, + "learning_rate": 0.0001, + "loss_function": "hybrid", + "dataset_dir": 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"status": "failed_subprocess"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_changemamba.py", "--dataset", "levir_val_as_test", "--gpu", "0"], "dataset": "levir_val_as_test", "end_time": "2026-07-11T13:48:32.764511+00:00", "exit_code": 70, "model": "changemamba", "start_time": "2026-07-11T13:46:15.557014+00:00", "status": "failed_subprocess"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_changemamba.py", "--dataset", "levir_val_as_test", "--gpu", "1"], "dataset": "levir_val_as_test", "end_time": "2026-07-11T18:08:03.877680+00:00", "exit_code": 70, "model": "changemamba", "start_time": "2026-07-11T15:42:55.893298+00:00", "status": "failed_subprocess"} +{"best_epoch": 64, "best_val_f1": 0.8980578002123945, "dataset": "whu_cd", "model": "dsamnet", "status": "complete", "test_f1": 0.8904875987868284, "timestamp": "2026-07-12T10:05:40.191354+00:00", "trainer": "unified"} +{"best_epoch": 59, "best_val_f1": 0.941529065657365, "dataset": "whu_cd", "model": "cgnet", "status": "complete", "test_f1": 0.9331411054126131, "timestamp": "2026-07-12T10:55:58.346231+00:00", "trainer": "unified"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_bit_cd.py", "--dataset", "wildfire_s2", "--gpu", "0", "--eval-only"], "dataset": "wildfire_s2", "end_time": "2026-07-12T10:57:34.058455+00:00", "exit_code": 1, "model": "bit_cd", "start_time": "2026-07-12T10:56:57.029969+00:00", "status": "failed_subprocess"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_fc_variants.py", "--dataset", "wildfire_s2", "--gpu", "0", "--model", "fc_ef", "--eval-only"], "dataset": "wildfire_s2", "end_time": "2026-07-12T10:57:43.867516+00:00", "exit_code": 1, "model": "fc_ef", "start_time": "2026-07-12T10:57:36.828832+00:00", "status": "failed_subprocess"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_fc_variants.py", "--dataset", "wildfire_s2", "--gpu", "0", "--model", "fc_siam_conc", "--eval-only"], "dataset": "wildfire_s2", "end_time": "2026-07-12T10:57:54.246079+00:00", "exit_code": 1, "model": "fc_siam_conc", "start_time": "2026-07-12T10:57:46.963668+00:00", "status": "failed_subprocess"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_fc_variants.py", "--dataset", "wildfire_s2", "--gpu", "0", "--model", "fc_siam_diff", "--eval-only"], "dataset": "wildfire_s2", "end_time": "2026-07-12T10:58:03.933088+00:00", "exit_code": 1, "model": "fc_siam_diff", "start_time": "2026-07-12T10:57:57.143656+00:00", "status": "failed_subprocess"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_hanet.py", "--dataset", "levir_cd_test_as_val", "--gpu", "0", "--eval-only"], "dataset": "levir_cd_test_as_val", "end_time": "2026-07-12T11:05:14.125740+00:00", "exit_code": 0, "model": "hanet", "start_time": "2026-07-12T10:58:06.538753+00:00", "status": "trained"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_ifnet.py", "--dataset", "sysu_cd", "--gpu", "0", "--eval-only"], "dataset": "sysu_cd", "end_time": "2026-07-12T11:12:13.734500+00:00", "exit_code": 0, "model": "ifnet", "start_time": "2026-07-12T11:05:17.633801+00:00", "status": "trained"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_ifnet.py", "--dataset", "wildfire_s2", "--gpu", "0", "--eval-only"], "dataset": "wildfire_s2", "end_time": "2026-07-12T11:13:21.860401+00:00", "exit_code": 1, "model": "ifnet", "start_time": "2026-07-12T11:12:17.015803+00:00", "status": "failed_subprocess"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_schanger.py", "--dataset", "dsifn_cd", "--gpu", "0", "--eval-only"], "dataset": "dsifn_cd", "end_time": "2026-07-12T11:13:58.239017+00:00", "exit_code": 0, "model": "schanger", "start_time": "2026-07-12T11:13:25.227252+00:00", "status": "trained"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_schanger.py", "--dataset", "levir_cd_test_as_val", "--gpu", "0", "--eval-only"], "dataset": "levir_cd_test_as_val", "end_time": "2026-07-12T11:20:10.189192+00:00", "exit_code": 0, "model": "schanger", "start_time": "2026-07-12T11:14:00.955937+00:00", "status": "trained"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_siam_nestedunet.py", "--dataset", "levir_cd_test_as_val", "--gpu", "0", "--eval-only"], "dataset": "levir_cd_test_as_val", "end_time": "2026-07-12T11:26:07.588491+00:00", "exit_code": 0, "model": "siam_nestedunet", "start_time": "2026-07-12T11:20:13.425524+00:00", "status": "trained"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_stanet.py", "--dataset", "levir_cd_test_as_val", "--gpu", "0", "--eval-only"], "dataset": "levir_cd_test_as_val", "end_time": "2026-07-12T11:28:29.813927+00:00", "exit_code": 0, "model": "stanet", "start_time": "2026-07-12T11:26:10.845661+00:00", "status": "trained"} +{"command": ["/home/buddhiw/miniconda3/envs/Mamba/bin/python", "/new-home/buddhiw/CD-Models/train/train_stanet.py", "--dataset", "sysu_cd", "--gpu", "0", "--eval-only"], "dataset": "sysu_cd", "end_time": "2026-07-12T11:30:24.547745+00:00", "exit_code": 0, "model": "stanet", "start_time": "2026-07-12T11:28:33.709209+00:00", "status": "trained"} diff --git a/run_training.py b/run_training.py new file mode 100644 index 0000000000000000000000000000000000000000..e45715ba71acfa20f45301f529208be95e9a4efb --- /dev/null +++ b/run_training.py @@ -0,0 +1,218 @@ +""" +run_training.py - Master training launcher for cd-models benchmark suite. + +Usage examples: + python run_training.py --model changemamba --dataset levir_cd + python run_training.py --model bifa --dataset all + python run_training.py --model all --dataset wildfire_s2 + python run_training.py --model all --dataset all + python run_training.py --model elgcnet --dataset whu_cd --resume + python run_training.py --model hanet --dataset levir_cd --eval-only +""" +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +from datetime import datetime, timezone +from pathlib import Path + +import yaml + +from utils.config_loader import load_dataset_config +from utils.dataset_cache import dataset_runtime_summary, print_dataloader_policy +from utils.gpu_utils import subprocess_gpu_env +from utils.results_writer import append_to_comparison_table + + +ROOT = Path(__file__).resolve().parent +SKIP_EXIT_CODE = 75 +FAILED_SUBPROCESS_EXIT_CODE = 70 +FAILED_NO_CHECKPOINT_EXIT_CODE = 71 +FAILED_EVAL_EXIT_CODE = 72 +WILDFIRE_ONLY_MODELS = set() +MAMBA_MODELS = {"cdmamba", "changemamba", "rsm_cd"} +GLOBAL_DATASET_EXCLUSIONS = { + "kate_cd": "KATE-CD training is disabled for the current benchmark sweep.", + "levir_cd": "Use levir_cd_test_as_val instead of the standard LEVIR-CD split.", +} +DATASET_EXCLUSIONS = { + "tinycd": {"wildfire_s2"}, +} + + +def _load_datasets() -> list[str]: + return sorted(path.stem for path in (ROOT / "configs" / "datasets").glob("*.yaml")) + + +def _load_registry() -> dict: + with (ROOT / "configs" / "models" / "registry.yaml").open("r", encoding="utf-8") as f: + return yaml.safe_load(f)["models"] + + +def _parse_args() -> argparse.Namespace: + registry = _load_registry() + datasets = _load_datasets() + parser = argparse.ArgumentParser(description="Master training launcher for cd-models benchmark suite.") + parser.add_argument("--model", required=True, choices=sorted(registry) + ["all"]) + parser.add_argument("--dataset", required=True, choices=datasets + ["all"]) + parser.add_argument("--resume", action="store_true") + parser.add_argument("--eval-only", action="store_true") + parser.add_argument("--epochs", type=int, default=None) + parser.add_argument("--batch-size", type=int, default=None) + parser.add_argument("--lr", type=float, default=None) + parser.add_argument("--gpu", default="0") + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--force", action="store_true") + parser.add_argument("--max-iters", type=int, default=None, help="Pass an iteration override to iteration-based wrappers.") + parser.add_argument("--num-workers", type=int, default=None) + parser.add_argument("--prefetch-factor", type=int, default=None) + parser.set_defaults(persistent_workers=None, pin_memory=None) + parser.add_argument("--persistent-workers", dest="persistent_workers", action="store_true") + parser.add_argument("--no-persistent-workers", dest="persistent_workers", action="store_false") + parser.add_argument("--pin-memory", dest="pin_memory", action="store_true") + parser.add_argument("--no-pin-memory", dest="pin_memory", action="store_false") + return parser.parse_args() + + +def _log_run(row: dict) -> None: + path = ROOT / "results" / "training_log.jsonl" + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("a", encoding="utf-8") as f: + f.write(json.dumps(row, sort_keys=True) + "\n") + + +def _status_symbol(status: str) -> str: + return status + + +def _status_from_exit_code(code: int, dry_run: bool) -> str: + if code == SKIP_EXIT_CODE: + return "skipped_dataset_policy" + if dry_run: + return "dry-run" if code == 0 else "failed_subprocess" + if code == 0: + return "trained" + if code == FAILED_SUBPROCESS_EXIT_CODE: + return "failed_subprocess" + if code == FAILED_NO_CHECKPOINT_EXIT_CODE: + return "failed_no_checkpoint" + if code == FAILED_EVAL_EXIT_CODE: + return "failed_eval" + return "failed_subprocess" + + +def _dataset_root_exists(dataset: str) -> tuple[bool, str]: + cfg = load_dataset_config(dataset) + root = Path(cfg["data_root"]) + print(f"[DATASET] {dataset_runtime_summary(cfg)}", flush=True) + print_dataloader_policy(cfg) + if cfg.get("io_warning"): + print(f"[DATASET-WARNING] {cfg['io_warning']}", flush=True) + return root.exists(), str(root) + + +def main() -> int: + args = _parse_args() + registry = _load_registry() + available_datasets = _load_datasets() + models = sorted(registry) if args.model == "all" else [args.model] + datasets = available_datasets if args.dataset == "all" else [args.dataset] + rows = [] + dataset_roots = {} + for dataset in datasets: + try: + dataset_roots[dataset] = _dataset_root_exists(dataset) + except Exception as exc: + dataset_roots[dataset] = (False, f"config error: {exc}") + + for model in models: + for dataset in datasets: + root_ok, root_note = dataset_roots[dataset] + if not root_ok: + print(f"[SKIP] {model}/{dataset}: dataset root is not available ({root_note}).", flush=True) + rows.append((model, dataset, "skipped_missing_dataset")) + continue + if dataset in GLOBAL_DATASET_EXCLUSIONS and model not in MAMBA_MODELS: + print(f"[SKIP] {model}/{dataset}: {GLOBAL_DATASET_EXCLUSIONS[dataset]}", flush=True) + rows.append((model, dataset, "skipped_dataset_policy")) + continue + if model in WILDFIRE_ONLY_MODELS and dataset != "wildfire_s2": + print(f"[SKIP] {model}/{dataset}: wrapper is wired only to WildFireS2 training.", flush=True) + rows.append((model, dataset, "skipped_dataset_policy")) + continue + excluded = DATASET_EXCLUSIONS.get(model, set()) + if dataset in excluded: + print(f"[SKIP] {model}/{dataset} unsupported.", flush=True) + rows.append((model, dataset, "skipped_dataset_policy")) + continue + metrics_path = ROOT / "results" / model / dataset / "metrics_test.json" + if metrics_path.exists() and not args.force: + print(f"[SKIP] {model}/{dataset} already complete", flush=True) + rows.append((model, dataset, "skipped_complete")) + continue + + script = ROOT / registry[model]["script"] + cmd = [sys.executable, str(script), "--dataset", dataset, "--gpu", str(args.gpu)] + if model.startswith("fc_"): + cmd.extend(["--model", model]) + if args.resume: + cmd.append("--resume") + if args.eval_only: + cmd.append("--eval-only") + if args.epochs is not None: + cmd.extend(["--epochs", str(args.epochs)]) + if args.batch_size is not None: + cmd.extend(["--batch-size", str(args.batch_size)]) + if args.lr is not None: + cmd.extend(["--lr", str(args.lr)]) + if args.dry_run: + cmd.append("--dry-run") + if args.force: + cmd.append("--force") + if args.max_iters is not None: + cmd.extend(["--max-iters", str(args.max_iters)]) + if args.num_workers is not None: + cmd.extend(["--num-workers", str(args.num_workers)]) + if args.prefetch_factor is not None: + cmd.extend(["--prefetch-factor", str(args.prefetch_factor)]) + if args.persistent_workers is True: + cmd.append("--persistent-workers") + elif args.persistent_workers is False: + cmd.append("--no-persistent-workers") + if args.pin_memory is True: + cmd.append("--pin-memory") + elif args.pin_memory is False: + cmd.append("--no-pin-memory") + + start = datetime.now(timezone.utc) + child_env, child_gpu = subprocess_gpu_env(args.gpu) + print(f"[GPU] requested physical GPU: {child_gpu.requested_gpu}", flush=True) + print(f"[GPU] launching with CUDA_VISIBLE_DEVICES={child_gpu.cuda_visible_devices}", flush=True) + print("[RUN]", model, dataset, " ".join(cmd), flush=True) + code = subprocess.run(cmd, cwd=ROOT, env=child_env, check=False).returncode + end = datetime.now(timezone.utc) + status = _status_from_exit_code(code, args.dry_run) + rows.append((model, dataset, status)) + _log_run({ + "model": model, + "dataset": dataset, + "command": cmd, + "start_time": start.isoformat(), + "end_time": end.isoformat(), + "exit_code": code, + "status": status, + }) + + append_to_comparison_table() + print("\nModel | Dataset | Status", flush=True) + print("--- | --- | ---", flush=True) + for model, dataset, status in rows: + print(f"{model} | {dataset} | {_status_symbol(status)}", flush=True) + ok_statuses = {"trained", "skipped_dataset_policy", "skipped_missing_dataset", "skipped_complete", "dry-run"} + return 0 if all(status in ok_statuses for _, _, status in rows) else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..db029bcf70b13dc1cb4e6d162f4493890bc66036 --- /dev/null +++ b/setup.py @@ -0,0 +1,499 @@ +#!/usr/bin/env python3 +""" +setup.py - One-command setup for the CD-Models benchmark suite. + +Clones all model repositories, downloads pretrained backbone weights, +verifies the Python environment, and optionally prepares datasets. + +Usage: + python setup.py # full setup (clone + weights + env check) + python setup.py --skip-weights # clone only, skip weight downloads + python setup.py --env-check-only # only check Python packages + python setup.py --status # print current setup status without changes + python setup.py --dataset levir_cd # prepare list files for a specific dataset +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib +import os +import subprocess +import sys +import urllib.request +from pathlib import Path + + +ROOT = Path(__file__).resolve().parent +DATASET_CONFIGS = ROOT / "configs" / "datasets" +MODEL_REPOS = ROOT / "model_repos" +IMG_EXTS = {".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp"} +ROOT_LEVEL_REPOS = { + "BIT_CD", + "ChangeFormer", + "STANet", + "Change3D", + "BiFA", + "CDMamba", + "RSM-CD", + "SChanger", + "IFNet", + "Siam-NestedUNet", +} + +REQUIRED_PACKAGES = [ + ("torch", "torch", "all models", False), + ("torchvision", "torchvision", "all models", False), + ("numpy", "numpy", "all models", True), + ("PIL", "Pillow", "all models", True), + ("cv2", "opencv-python", "all models", True), + ("yaml", "pyyaml", "all models", True), + ("tqdm", "tqdm", "weight downloads", True), + ("timm", "timm", "TinyCD, ELGC-Net, ChangeFormer", True), + ("einops", "einops", "BIT_CD, ChangeFormer", True), + ("sklearn", "scikit-learn", "metrics", True), + ("gdown", "gdown", "GDrive weight fallback", True), + ("mmengine", "mmengine==0.10.1", "ChangeMamba, Changer", False), + ("mmcv", "mmcv==2.1.0", "ChangeMamba, Changer", False), + ("mmseg", "mmsegmentation==1.2.2", "ChangeMamba, Changer", False), +] + +CONFIRMED_REPOS = { + "BIT_CD": "https://github.com/justchenhao/BIT_CD.git", + "ChangeFormer": "https://github.com/wgcban/ChangeFormer.git", + "STANet": "https://github.com/justchenhao/STANet.git", + "Change3D": "https://github.com/Z-Zheng/Change3D.git", + "BiFA": "https://github.com/zmoka-zht/BiFA.git", + "CDMamba": "https://github.com/zmoka-zht/CDMamba.git", + "RSM-CD": "https://github.com/walking-shadow/Official_Remote_Sensing_Mamba.git", + "SChanger": None, + "IFNet": "https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection.git", + "Siam-NestedUNet": "https://github.com/likyoo/Siam-NestedUNet.git", + "fully_convolutional_change_detection": "https://github.com/rcdaudt/fully_convolutional_change_detection.git", + "ChangeMamba": "https://github.com/ChenHongruixuan/ChangeMamba.git", + "DSAMNet": "https://github.com/liumency/DSAMNet.git", + "Tiny_model_4_CD": "https://github.com/AndreaCodegoni/Tiny_model_4_CD.git", + "HANet-CD": "https://github.com/ChengxiHAN/HANet-CD.git", + "CGNet-CD": "https://github.com/ChengxiHAN/CGNet-CD.git", + "open-cd": "https://github.com/likyoo/open-cd.git", + "elgcnet": "https://github.com/techmn/elgcnet.git", +} + +ZENODO_WEIGHTS = { + "vmamba_tiny": { + "record_id": "14037770", + "filename": "vssmtiny_dp01_ckpt_epoch_292.pth", + "dest_dir": MODEL_REPOS / "ChangeMamba" / "pretrained_weight", + "sha256": None, + "notes": "VMamba-Tiny for ChangeMamba", + }, + "vmamba_small": { + "record_id": "14037770", + "filename": "vssmsmall_dp03_ckpt_epoch_238.pth", + "dest_dir": MODEL_REPOS / "ChangeMamba" / "pretrained_weight", + "sha256": None, + "notes": "VMamba-Small for ChangeMamba", + }, + "vmamba_base": { + "record_id": "14037770", + "filename": "vssmbase_dp06_ckpt_epoch_241.pth", + "dest_dir": MODEL_REPOS / "ChangeMamba" / "pretrained_weight", + "sha256": None, + "notes": "VMamba-Base for ChangeMamba", + }, +} + +TIMM_WEIGHTS_TO_PREFETCH = [ + "efficientnet_b4", + "mit_b0", + "mit_b1", + "mit_b4", +] + +TORCHVISION_WEIGHTS_TO_PREFETCH = [ + ("resnet18", "IMAGENET1K_V1"), + ("resnet50", "IMAGENET1K_V1"), + ("vgg16", "IMAGENET1K_V1"), +] + +TIMM_ALIASES = { + "efficientnet_b4": ("efficientnet_b4", "tf_efficientnet_b4", "tf_efficientnet_b4_ns"), + "mit_b0": ("mit_b0", "segformer_b0"), + "mit_b1": ("mit_b1", "segformer_b1"), + "mit_b4": ("mit_b4", "segformer_b4"), +} + + +def run(cmd: list[str]) -> subprocess.CompletedProcess: + return subprocess.run(cmd, capture_output=True, text=True, check=False) + + +def import_status(import_name: str) -> tuple[bool, str]: + try: + module = importlib.import_module(import_name) + except Exception as exc: + return False, str(exc) + return True, getattr(module, "__version__", "present") + + +def pip_install(package: str) -> bool: + print(f" [INSTALL] pip install {package}") + result = run([sys.executable, "-m", "pip", "install", package]) + if result.returncode != 0: + print(f" [FAIL] {package}: {result.stderr[-500:]}") + return False + print(f" [OK] {package} installed") + return True + + +def check_environment(status_only: bool = False) -> dict[str, int]: + print("\nPYTHON ENVIRONMENT") + print("------------------") + print(f"Python executable: {sys.executable}") + print(f"Python version: {sys.version.split()[0]}") + present = missing = installed = warnings = 0 + + for import_name, pip_name, required_for, auto_install in REQUIRED_PACKAGES: + ok, detail = import_status(import_name) + if ok: + present += 1 + print(f" [OK] {import_name:<12} {detail:<18} required for {required_for}") + continue + missing += 1 + if auto_install and not status_only: + if pip_install(pip_name): + installed += 1 + continue + if not auto_install: + warnings += 1 + print(f" [WARN] {import_name:<12} missing; install manually: pip install {pip_name}") + else: + print(f" [MISSING] {import_name:<12} install: pip install {pip_name}") + + if warnings: + print("\nVersion-sensitive packages are intentionally not auto-installed.") + print("For ChangeMamba / Changer:") + print(" pip install mmengine==0.10.1") + print(" pip install mmcv==2.1.0 -f https://download.openmmlab.com/mmcv/dist/cu124/torch2.6/index.html") + print(" pip install mmsegmentation==1.2.2 mmdet==3.3.0 mmpretrain==1.2.0") + + return {"present": present, "missing": missing, "installed": installed, "warnings": warnings} + + +def clone_repo(name: str, url: str | None, dest_dir: Path = MODEL_REPOS, status_only: bool = False) -> bool: + dest = (ROOT / name) if name in ROOT_LEVEL_REPOS else (dest_dir / name) + if dest.is_dir(): + print(f" [OK] {name} already cloned") + return True + if url is None: + print(f" [SKIP] {name} - URL not confirmed, clone manually") + return False + if status_only: + print(f" [MISSING] {name} -> {url}") + return False + print(f" [CLONE] {name}...") + dest.parent.mkdir(parents=True, exist_ok=True) + result = run(["git", "clone", "--depth", "1", url, str(dest)]) + if result.returncode == 0: + print(f" [OK] {name} cloned") + return True + print(f" [FAIL] {name}: {result.stderr[:300]}") + return False + + +def clone_repositories(status_only: bool = False) -> dict[str, int]: + print("\nMODEL REPOSITORIES") + print("------------------") + ok = missing = skipped = 0 + for name, url in CONFIRMED_REPOS.items(): + result = clone_repo(name, url, status_only=status_only) + if result: + ok += 1 + elif url is None: + skipped += 1 + else: + missing += 1 + return {"ok": ok, "missing": missing, "skipped": skipped, "total": len(CONFIRMED_REPOS)} + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as f: + for chunk in iter(lambda: f.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def download_zenodo_weight( + record_id: str, + filename: str, + dest_dir: Path, + expected_sha256: str | None = None, + status_only: bool = False, +) -> Path | None: + dest_dir.mkdir(parents=True, exist_ok=True) + dest_path = dest_dir / filename + if dest_path.exists(): + if expected_sha256: + actual = sha256_file(dest_path) + if actual != expected_sha256: + print(f" [FAIL] {filename} sha256 mismatch") + return None + print(f" [OK] {filename} already present") + return dest_path + if status_only: + print(f" [MISSING] {filename}") + return None + + try: + from tqdm import tqdm + except Exception: + tqdm = None + + url = f"https://zenodo.org/records/{record_id}/files/{filename}" + print(f" [DOWNLOAD] {filename} from Zenodo record {record_id}") + + class ProgressHook: + def __init__(self) -> None: + self.pbar = None + + def __call__(self, count: int, block_size: int, total_size: int) -> None: + if tqdm is None: + return + if self.pbar is None: + self.pbar = tqdm(total=total_size, unit="B", unit_scale=True, desc=filename) + self.pbar.update(count * block_size - self.pbar.n) + + hook = ProgressHook() + try: + urllib.request.urlretrieve(url, dest_path, reporthook=hook) + if hook.pbar: + hook.pbar.close() + if expected_sha256: + actual = sha256_file(dest_path) + if actual != expected_sha256: + dest_path.unlink(missing_ok=True) + print(f" [FAIL] {filename}: sha256 mismatch") + return None + print(f" [OK] {filename} downloaded") + return dest_path + except Exception as exc: + if hook.pbar: + hook.pbar.close() + dest_path.unlink(missing_ok=True) + print(f" [FAIL] {filename}: {exc}") + return None + + +def prefetch_timm_weights(model_name: str, status_only: bool = False) -> bool: + ok, detail = import_status("timm") + if not ok: + print(f" [SKIP] timm:{model_name} - timm missing ({detail})") + return False + if status_only: + print(f" [CHECK] timm:{model_name} cache status cannot be proven without instantiation") + return True + + import timm + + candidates = TIMM_ALIASES.get(model_name, (model_name,)) + for candidate in candidates: + try: + model = timm.create_model(candidate, pretrained=True, num_classes=0) + del model + print(f" [OK] timm {model_name} via {candidate}") + return True + except RuntimeError as exc: + if "Unknown model" not in str(exc): + print(f" [FAIL] timm {model_name}: {exc}") + return False + print(f" [WARN] timm {getattr(timm, '__version__', 'unknown')} does not provide {model_name}") + return False + + +def prefetch_torchvision_weights(model_name: str, weight_name: str, status_only: bool = False) -> bool: + ok, detail = import_status("torchvision.models") + if not ok: + print(f" [SKIP] torchvision:{model_name} - torchvision missing ({detail})") + return False + if status_only: + print(f" [CHECK] torchvision:{model_name} cache status cannot be proven without instantiation") + return True + + import torchvision.models as models + + builder = getattr(models, model_name) + enum_name = "".join(part.capitalize() for part in model_name.split("_")) + "_Weights" + weights_enum = getattr(models, enum_name, None) + try: + if weights_enum is not None: + weights = getattr(weights_enum, weight_name, weights_enum.DEFAULT) + model = builder(weights=weights) + else: + model = builder(pretrained=True) + del model + print(f" [OK] torchvision {model_name}") + return True + except Exception as exc: + print(f" [FAIL] torchvision {model_name}: {exc}") + return False + + +def download_weights(status_only: bool = False) -> dict[str, int]: + print("\nPRETRAINED WEIGHTS") + print("------------------") + ok = missing = 0 + for spec in ZENODO_WEIGHTS.values(): + path = download_zenodo_weight( + spec["record_id"], + spec["filename"], + spec["dest_dir"], + expected_sha256=spec["sha256"], + status_only=status_only, + ) + ok += int(path is not None) + missing += int(path is None) + for model_name in TIMM_WEIGHTS_TO_PREFETCH: + ok += int(prefetch_timm_weights(model_name, status_only=status_only)) + for model_name, weight_name in TORCHVISION_WEIGHTS_TO_PREFETCH: + ok += int(prefetch_torchvision_weights(model_name, weight_name, status_only=status_only)) + return {"ok": ok, "missing": missing, "total": len(ZENODO_WEIGHTS) + len(TIMM_WEIGHTS_TO_PREFETCH) + len(TORCHVISION_WEIGHTS_TO_PREFETCH)} + + +def parse_simple_yaml(path: Path) -> dict[str, object]: + data: dict[str, object] = {} + stack: list[tuple[int, dict[str, object]]] = [(-1, data)] + for raw in path.read_text(encoding="utf-8").splitlines(): + line = raw.split("#", 1)[0].rstrip() + if not line.strip() or ":" not in line: + continue + indent = len(line) - len(line.lstrip(" ")) + key, value = line.strip().split(":", 1) + while stack and indent <= stack[-1][0]: + stack.pop() + current = stack[-1][1] + value = value.strip() + if not value: + child: dict[str, object] = {} + current[key] = child + stack.append((indent, child)) + continue + if value.startswith("[") and value.endswith("]"): + current[key] = [x.strip().strip("'\"") for x in value[1:-1].split(",") if x.strip()] + elif value.isdigit(): + current[key] = int(value) + else: + current[key] = value.strip("'\"") + return data + + +def dataset_root_from_config(cfg: dict[str, object]) -> Path: + value = str(cfg.get("data_root", "")) + data_root = os.environ.get("DATA_ROOT", str(ROOT.parent / "Datasets")) + return Path(value.replace("${DATA_ROOT}", data_root)).expanduser() + + +def prepare_dataset_lists(data_root: Path, image_a_folder: str = "A", splits: tuple[str, ...] = ("train", "val", "test")) -> dict[str, int]: + list_dir = data_root / "list" + list_dir.mkdir(parents=True, exist_ok=True) + counts: dict[str, int] = {} + for split in splits: + out = list_dir / f"{split}.txt" + if out.exists(): + counts[split] = sum(1 for _ in out.open("r", encoding="utf-8")) + print(f" [OK] {out} already exists ({counts[split]} entries)") + continue + split_dir = data_root / split + img_dir = None + for candidate in (image_a_folder, "A", "T1", "t1", "img", "images", "image"): + if (split_dir / candidate).is_dir(): + img_dir = split_dir / candidate + break + if img_dir is None: + counts[split] = 0 + print(f" [WARN] no image folder found under {split_dir}") + continue + names = sorted(p.name for p in img_dir.iterdir() if p.is_file() and p.suffix.lower() in IMG_EXTS) + out.write_text("\n".join(names) + ("\n" if names else ""), encoding="utf-8") + counts[split] = len(names) + print(f" [OK] {out}: {len(names)} entries") + return counts + + +def prepare_datasets(selected: str | None = None, status_only: bool = False) -> dict[str, int]: + print("\nDATASETS") + print("--------") + exists = missing = prepared = 0 + paths = sorted(DATASET_CONFIGS.glob("*.yaml")) + for path in paths: + if selected and path.stem != selected: + continue + cfg = parse_simple_yaml(path) + root = dataset_root_from_config(cfg) + if root.is_dir(): + exists += 1 + print(f" [EXISTS] {path.stem}: {root}") + if not status_only: + split_keys = tuple((cfg.get("splits") or {"train": "train", "val": "val", "test": "test"}).keys()) + prepare_dataset_lists(root, image_a_folder=str(cfg.get("image_a_folder", "A")), splits=split_keys) + prepared += 1 + else: + missing += 1 + print(f" [MISSING] {path.stem}: {root}") + return {"exists": exists, "missing": missing, "prepared": prepared, "total": len(paths)} + + +def print_final_status(env: dict[str, int], repos: dict[str, int], weights: dict[str, int] | None, datasets: dict[str, int]) -> None: + print("\nSETUP STATUS") + print("Component Status Notes") + print("-----------------------------|-------------|----------------------------------") + env_status = "COMPLETE" if env["missing"] == 0 else "CHECK" + print(f"Python environment {env_status:<12} {env['installed']} packages auto-installed; {env['warnings']} manual warnings") + print(f"Model repos ({repos['total']} total) {repos['ok']} / {repos['total']:<6} {repos['skipped']} URL-unconfirmed entries") + if weights is None: + print("Backbone weights SKIPPED run: python setup.py --weights-only") + else: + print(f"Backbone weights {weights['ok']} / {weights['total']:<6} Zenodo, timm, and torchvision warmups") + print(f"Datasets {datasets['exists']} / {datasets['total']:<6} roots found under DATA_ROOT/default search") + print("\nNext steps:") + print(" export DATA_ROOT=/path/to/datasets") + print(" python run_training.py --model bifa --dataset levir_cd --dry-run") + print(" python run_training.py --model bifa --dataset levir_cd") + print(" For ChangeMamba/Changer, install the MMSeg stack shown above.") + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="One-command setup for the CD-Models benchmark suite.") + parser.add_argument("--skip-weights", action="store_true") + parser.add_argument("--weights-only", action="store_true") + parser.add_argument("--env-check-only", action="store_true") + parser.add_argument("--status", action="store_true") + parser.add_argument("--dataset", default=None, help="Prepare list files for one dataset config name.") + return parser.parse_args() + + +def main() -> int: + args = parse_args() + status_only = args.status + + env = check_environment(status_only=status_only) + if args.env_check_only: + print_final_status(env, {"ok": 0, "missing": 0, "skipped": 0, "total": len(CONFIRMED_REPOS)}, None, {"exists": 0, "missing": 0, "prepared": 0, "total": 0}) + return 0 if env["warnings"] == 0 else 1 + + repos = {"ok": 0, "missing": 0, "skipped": 0, "total": len(CONFIRMED_REPOS)} + if not args.weights_only: + repos = clone_repositories(status_only=status_only) + + weights = None + if not args.skip_weights: + weights = download_weights(status_only=status_only) + + datasets = prepare_datasets(selected=args.dataset, status_only=status_only) + print_final_status(env, repos, weights, datasets) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tools/generate_hf_model_card.py b/tools/generate_hf_model_card.py new file mode 100644 index 0000000000000000000000000000000000000000..7b99f7c4af26e66b2c95a5d07b4179604a7885eb --- /dev/null +++ b/tools/generate_hf_model_card.py @@ -0,0 +1,249 @@ +from __future__ import annotations + +import argparse +import json +from collections import defaultdict +from pathlib import Path +from typing import Any + + +ROOT = Path(__file__).resolve().parents[1] +RESULTS = ROOT / "results" +README = ROOT / "README.md" +REPO_ID = "dineth18/CD-Models" + +DATASET_LABELS = { + "dsifn_cd": "DSIFN-CD", + "levir_cd_test_as_val": "LEVIR-CD+ (test-as-val protocol)", + "sysu_cd": "SYSU-CD", + "whu_cd": "WHU-CD", +} +DATASET_ORDER = tuple(DATASET_LABELS) +MODEL_LABELS = { + "bifa": "BiFA", + "bit_cd": "BIT-CD", + "cgnet": "CGNet", + "changeformer": "ChangeFormer", + "changemamba": "ChangeMamba", + "dsamnet": "DSAMNet", + "fc_ef": "FC-EF", + "fc_siam_conc": "FC-Siam-conc", + "fc_siam_diff": "FC-Siam-diff", + "hanet": "HANet", + "ifnet": "IFNet", + "schanger": "SChanger", + "siam_nestedunet": "Siam-NestedUNet", + "stanet": "STANet", +} + + +def load_rows() -> dict[str, list[dict[str, Any]]]: + grouped: dict[str, list[dict[str, Any]]] = defaultdict(list) + for path in sorted(RESULTS.glob("*/*/metrics_test.json")): + with path.open(encoding="utf-8") as handle: + row = json.load(handle) + dataset = str(row.get("dataset") or path.parent.name) + if dataset not in DATASET_LABELS: + continue + if row.get("split") != "test" or row.get("status") != "complete": + continue + if not isinstance(row.get("f1"), (int, float)): + continue + row["_path"] = path + row["_model"] = str(row.get("model") or path.parents[1].name) + grouped[dataset].append(row) + for rows in grouped.values(): + rows.sort(key=lambda row: (-float(row["f1"]), row["_model"])) + return grouped + + +def metric(row: dict[str, Any], *keys: str) -> Any: + for key in keys: + value = row.get(key) + if value is not None and value != "": + return value + return None + + +def fmt(value: Any, digits: int = 4) -> str: + if value is None: + return "—" + return f"{float(value):.{digits}f}" + + +def fmt_profile(value: Any) -> str: + if value is None: + return "—" + number = float(value) + return f"{number:.2f}" + + +def checkpoint_path(row: dict[str, Any]) -> Path: + canonical = (row["_path"].parent / "checkpoints" / "best_model.pth").resolve() + if canonical.exists(): + return canonical + raw = metric(row, "checkpoint", "checkpoint_path") + if raw: + path = Path(str(raw)) + if path.exists() and path.resolve().is_relative_to(ROOT): + return path.resolve() + return canonical + + +def repo_relative(path: Path) -> Path: + return path.resolve().relative_to(ROOT) + + +def download_url(path: Path) -> str: + relative = repo_relative(path).as_posix() + return f"https://huggingface.co/{REPO_ID}/resolve/main/{relative}?download=true" + + +def markdown_table(headers: list[str], rows: list[list[str]]) -> list[str]: + return [ + "| " + " | ".join(headers) + " |", + "| " + " | ".join("---" for _ in headers) + " |", + *("| " + " | ".join(row) + " |" for row in rows), + ] + + +def build_results() -> tuple[str, list[Path]]: + grouped = load_rows() + winners: list[Path] = [] + lines = [ + "## Results and released checkpoints", + "", + "Only completed `metrics_test.json` evaluations are eligible. Rankings are computed per dataset by test-set F1 (descending), and only the rank-1 checkpoint for each dataset is released. Validation-only, incomplete, and checkpoint-only runs are excluded.", + "", + "All accuracy values are fractions. GPU memory is the PyTorch peak reserved memory for inference, with peak allocated memory used only when reserved memory is absent. FPS is model-only throughput. A dash means the evaluator did not record that metric; values are never estimated.", + "", + "The current evaluator records BF1 but not boundary mean IoU (BmIoU), so BmIoU remains explicitly unavailable rather than being inferred from BF1. Boundary IoU and boundary F1 are distinct measures.", + "", + "### Released checkpoint summary", + "", + ] + summary_rows: list[list[str]] = [] + for dataset in DATASET_ORDER: + rows = grouped.get(dataset, []) + if not rows: + continue + winner = rows[0] + checkpoint = checkpoint_path(winner) + if not checkpoint.is_file(): + raise FileNotFoundError(f"Winning checkpoint is missing: {checkpoint}") + winners.append(checkpoint) + summary_rows.append( + [ + DATASET_LABELS[dataset], + MODEL_LABELS.get(winner["_model"], winner["_model"]), + fmt(winner["f1"]), + f"[{repo_relative(checkpoint).name}]({download_url(checkpoint)})", + f"{checkpoint.stat().st_size / (1024 ** 2):.1f} MiB", + ] + ) + lines.extend(markdown_table(["Dataset", "Selected model", "Test F1", "Download", "Size"], summary_rows)) + + headers = [ + "Rank", + "Model", + "F1", + "mIoU", + "Overall accuracy", + "Recall", + "Precision", + "BF1", + "BmIoU", + "GFLOPs", + "GPU (GB)", + "FPS", + "Parameters (M)", + "Checkpoint", + ] + for dataset in DATASET_ORDER: + rows = grouped.get(dataset, []) + lines.extend(["", f"### {DATASET_LABELS[dataset]}", ""]) + table_rows: list[list[str]] = [] + for rank, row in enumerate(rows, start=1): + link = "—" + if rank == 1: + checkpoint = checkpoint_path(row) + link = f"[Download]({download_url(checkpoint)})" + table_rows.append( + [ + str(rank), + MODEL_LABELS.get(row["_model"], row["_model"]), + fmt(metric(row, "f1", "F1")), + fmt(metric(row, "miou", "mIoU")), + fmt(metric(row, "oa", "OA")), + fmt(metric(row, "recall", "Recall")), + fmt(metric(row, "precision", "Precision")), + fmt(metric(row, "bf1", "BF1", "boundary_f1")), + fmt(metric(row, "bmiou", "BmIoU", "boundary_miou")), + fmt_profile(metric(row, "flops_g", "FLOPsG")), + fmt_profile(metric(row, "gpu_mem_reserved_peak_gb", "gpu_mem_allocated_peak_gb")), + fmt_profile(metric(row, "fps_model_only", "fps", "FPS")), + fmt_profile(metric(row, "params_m", "ParamsM")), + link, + ] + ) + if table_rows: + lines.extend(markdown_table(headers, table_rows)) + else: + lines.append("No completed test-set result is available, so no checkpoint is published.") + + lines.extend( + [ + "", + "### Datasets without a released checkpoint", + "", + "WildFire-S2, KATE-CD-256, the standard LEVIR-CD+ configuration, and Custom-CD currently have no eligible completed test result. Their validation-only or incomplete checkpoints are intentionally not uploaded.", + "", + ] + ) + return "\n".join(lines), winners + + +def update_readme(results_markdown: str) -> None: + text = README.read_text(encoding="utf-8") + current_heading = "## Results and released checkpoints\n" + legacy_heading = "## Results\n" + if current_heading in text: + start = text.index(current_heading) + else: + start = text.index(legacy_heading) + end = text.index("## Installation\n", start) + text = text[:start] + results_markdown + "\n\n" + text[end:] + old = ( + "### Trained checkpoints\n\n" + "Trained benchmark checkpoints are not released in the root README. " + "The sweep writes local checkpoints under `results/{model}/{dataset}/checkpoints/`." + ) + new = ( + "### Trained checkpoints\n\n" + "The released rank-1 test checkpoints and direct download links are listed in " + "[Results and released checkpoints](#results-and-released-checkpoints). Local training still " + "writes checkpoints under `results/{model}/{dataset}/checkpoints/`." + ) + if old in text: + text = text.replace(old, new) + elif new not in text: + raise RuntimeError("Expected trained-checkpoint README text was not found") + README.write_text(text, encoding="utf-8") + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--write", action="store_true", help="Replace the README result sections") + args = parser.parse_args() + markdown, winners = build_results() + if args.write: + update_readme(markdown) + else: + print(markdown) + print("Winning checkpoints:") + for winner in winners: + print(repo_relative(winner)) + + +if __name__ == "__main__": + main() diff --git a/train/__init__.py b/train/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a9a2c5b3bb437bff74e283b62c894075e8c15331 --- /dev/null +++ b/train/__init__.py @@ -0,0 +1 @@ +__all__ = [] diff --git a/train/fc_adapter.py b/train/fc_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..2b6d18217656abd9f65a5b0f2922f4ce3bd8bfd8 --- /dev/null +++ b/train/fc_adapter.py @@ -0,0 +1,313 @@ +from __future__ import annotations + +import argparse +import json +import sys +from datetime import datetime, timezone +from pathlib import Path + +import torch +import torch.nn as nn +from torch.utils.data import DataLoader + + +ROOT = Path(__file__).resolve().parents[1] +REPO = ROOT / "model_repos" / "fully_convolutional_change_detection" +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) +if str(REPO) not in sys.path: + sys.path.insert(0, str(REPO)) + +from datasets.cd_dataset import CDDataset +from siamunet_conc import SiamUnet_conc +from siamunet_diff import SiamUnet_diff +from unet import Unet +from utils.config_loader import load_dataset_config, load_model_config +from utils.dataset_cache import apply_dataloader_cli_overrides, dataloader_kwargs, dataloader_policy_lines, dataset_runtime_summary, print_dataloader_policy +from utils.gpu_utils import print_gpu_diagnostics, resolve_gpu +from utils.metrics import BinaryMetrics +from utils.model_adapters import get_model_adapter +from utils.results_writer import append_to_comparison_table, save_metrics +from utils.unified_evaluator import evaluate_with_adapter + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Train FC-EF / FC-Siam variants on a CD-Models dataset.") + parser.add_argument("--model", required=True, choices=["fc_ef", "fc_siam_conc", "fc_siam_diff"]) + parser.add_argument("--dataset", required=True) + parser.add_argument("--epochs", type=int, default=None) + parser.add_argument("--batch-size", type=int, default=None) + parser.add_argument("--lr", type=float, default=None) + parser.add_argument("--gpu", default="0") + parser.add_argument("--resume", action="store_true") + parser.add_argument("--force", action="store_true") + parser.add_argument("--eval-only", action="store_true") + parser.add_argument("--smoke-test", action="store_true") + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--output-dir", default=None) + parser.add_argument("--allow-missing-profilers", action="store_true") + parser.add_argument("--num-workers", type=int, default=None) + parser.add_argument("--prefetch-factor", type=int, default=None) + parser.set_defaults(persistent_workers=None, pin_memory=None) + parser.add_argument("--persistent-workers", dest="persistent_workers", action="store_true") + parser.add_argument("--no-persistent-workers", dest="persistent_workers", action="store_false") + parser.add_argument("--pin-memory", dest="pin_memory", action="store_true") + parser.add_argument("--no-pin-memory", dest="pin_memory", action="store_false") + return parser.parse_args() + + +def _build_model(model_name: str) -> nn.Module: + if model_name == "fc_ef": + return Unet(input_nbr=6, label_nbr=2) + if model_name == "fc_siam_conc": + return SiamUnet_conc(input_nbr=3, label_nbr=2) + return SiamUnet_diff(input_nbr=3, label_nbr=2) + + +def _metrics(log_probs: torch.Tensor, target: torch.Tensor) -> tuple[int, int, int, int]: + pred = log_probs.argmax(dim=1) + tp = ((pred == 1) & (target == 1)).sum().item() + fp = ((pred == 1) & (target == 0)).sum().item() + fn = ((pred == 0) & (target == 1)).sum().item() + tn = ((pred == 0) & (target == 0)).sum().item() + return tp, fp, fn, tn + + +def _score(tp: int, fp: int, fn: int, tn: int) -> dict[str, float]: + eps = 1e-8 + precision = tp / (tp + fp + eps) + recall = tp / (tp + fn + eps) + f1 = 2 * precision * recall / (precision + recall + eps) + iou = tp / (tp + fp + fn + eps) + acc = (tp + tn) / (tp + fp + fn + tn + eps) + return {"f1": f1, "iou": iou, "precision": precision, "recall": recall, "accuracy": acc} + + +def _forward(model: nn.Module, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + return model(a, b) + + +def _fc_loss(model_name: str, log_probs: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + weight = None + if model_name == "fc_siam_diff": + pos = target.eq(1).sum().float() + neg = target.eq(0).sum().float() + if bool(pos.item() > 0): + pos_weight = (neg / pos.clamp_min(1.0)).clamp(min=1.0, max=50.0) + weight = torch.stack([torch.ones_like(pos_weight), pos_weight]).to(log_probs.device) + return nn.functional.nll_loss(log_probs.float(), target, weight=weight) + + +def _load_checkpoint_if_requested(model: nn.Module, optimizer: torch.optim.Optimizer, path: Path) -> tuple[int, float]: + if not path.exists(): + raise FileNotFoundError(f"Cannot resume; checkpoint not found: {path}") + checkpoint = torch.load(path, map_location="cpu") + if not isinstance(checkpoint, dict) or "model_state_dict" not in checkpoint: + raise RuntimeError(f"Checkpoint {path} is not a CD-Models FC checkpoint.") + model.load_state_dict(checkpoint["model_state_dict"], strict=True) + if "optimizer_state_dict" in checkpoint: + optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + return int(checkpoint.get("epoch", 0)), float(checkpoint.get("best_val_f1", -1.0)) + + +def _run_smoke_test(args: argparse.Namespace) -> int: + gpu_resolution = resolve_gpu(args.gpu) + print_gpu_diagnostics(gpu_resolution) + dataset_cfg = load_dataset_config(args.dataset) + apply_dataloader_cli_overrides(dataset_cfg, args) + print_dataloader_policy(dataset_cfg, torch.cuda.is_available()) + ds = CDDataset(dataset_cfg["data_root"], "test", cfg=dataset_cfg, return_format="tuple") + a, b, mask, name = ds[0] + model = _build_model(args.model) + with torch.inference_mode(): + output = model(a.unsqueeze(0), b.unsqueeze(0)) + metrics = BinaryMetrics(threshold=float(dataset_cfg.get("eval", {}).get("threshold", 0.5))) + metrics.update(output, mask.unsqueeze(0)) + param_count = sum(p.numel() for p in model.parameters()) + smoke_dir = ROOT / "results" / args.model / dataset_cfg["name"] / "smoke_test" + smoke_dir.mkdir(parents=True, exist_ok=True) + from utils.metrics import normalize_binary_prediction + from utils.qualitative import save_binary_prediction + from utils.profiling import ProfilingUnavailable, count_flops + + pred, _ = normalize_binary_prediction(output) + save_binary_prediction(pred[0], smoke_dir / f"{name}_pred.png") + flops_error = None + try: + count_flops( + model, + lambda: ( + torch.zeros(1, 3, int(dataset_cfg.get("img_size", 256)), int(dataset_cfg.get("img_size", 256))), + torch.zeros(1, 3, int(dataset_cfg.get("img_size", 256)), int(dataset_cfg.get("img_size", 256))), + ), + torch.device("cpu"), + ) + except ProfilingUnavailable as exc: + flops_error = str(exc) + print( + f"[SMOKE] {args.model}/{dataset_cfg['name']} sample={name} output={tuple(output.shape)} " + f"params={param_count} metrics={metrics.compute()} flops_error={flops_error}" + ) + return 0 + + +def main() -> int: + args = _parse_args() + if args.smoke_test: + return _run_smoke_test(args) + gpu_resolution = resolve_gpu(args.gpu) + print_gpu_diagnostics(gpu_resolution) + device = torch.device(gpu_resolution.local_device) + + dataset_cfg = load_dataset_config(args.dataset) + apply_dataloader_cli_overrides(dataset_cfg, args) + model_cfg = load_model_config(args.model) + if args.dry_run: + print(f"[DRY-RUN] fc model={args.model} dataset={dataset_cfg['name']} root={dataset_cfg['data_root']}") + print(f"[DATASET] {dataset_runtime_summary(dataset_cfg)}") + print_dataloader_policy(dataset_cfg, torch.cuda.is_available()) + return 0 + train_ds = CDDataset(dataset_cfg["data_root"], "train", cfg=dataset_cfg, return_format="tuple") + val_ds = CDDataset(dataset_cfg["data_root"], "val", cfg=dataset_cfg, return_format="tuple") + batch_size = int(args.batch_size or dataset_cfg.get("batch_size", 8)) + loader_kwargs = dataloader_kwargs(dataset_cfg, torch.cuda.is_available()) + print(f"[DATASET] {dataset_runtime_summary(dataset_cfg)}") + print_dataloader_policy(dataset_cfg, torch.cuda.is_available()) + if dataset_cfg.get("io_warning"): + print(f"[DATASET-WARNING] {dataset_cfg['io_warning']}") + train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, **loader_kwargs, drop_last=True) + val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, **loader_kwargs) + + model = _build_model(args.model).to(device) + optimizer = torch.optim.Adam( + model.parameters(), + lr=float(args.lr or model_cfg.get("lr", 1e-4)), + weight_decay=float(model_cfg.get("weight_decay", 0.0) or 0.0), + ) + + out_dir = Path(args.output_dir) if args.output_dir else ROOT / "results" / args.model / dataset_cfg["name"] + if not out_dir.is_absolute(): + out_dir = ROOT / out_dir + ckpt_dir = out_dir / "checkpoints" + log_dir = out_dir / "logs" + ckpt_dir.mkdir(parents=True, exist_ok=True) + log_dir.mkdir(parents=True, exist_ok=True) + log_path = log_dir / "train.log" + best_f1 = -1.0 + best_epoch = 0 + start_epoch = 1 + latest_path = ckpt_dir / "latest.pth" + best_path = ckpt_dir / "best_model.pth" + + if args.resume: + last_epoch, best_f1 = _load_checkpoint_if_requested(model, optimizer, latest_path) + start_epoch = last_epoch + 1 + best_epoch = int(last_epoch) + + if args.eval_only: + _, code = evaluate_with_adapter( + model_name=args.model, + dataset_cfg=dataset_cfg, + model_config=model_cfg, + adapter=get_model_adapter(args.model), + checkpoint_path=best_path, + device=device, + strict_profiling=not args.allow_missing_profilers, + output_dir=out_dir, + ) + return code + + with log_path.open("w", encoding="utf-8") as log: + log.write(f"# {args.model} {dataset_cfg['name']} training\n") + log.write(f"# Dataset: {dataset_runtime_summary(dataset_cfg)}\n") + for line in dataloader_policy_lines(dataset_cfg, torch.cuda.is_available()): + log.write(line + "\n") + epochs = int(args.epochs or model_cfg.get("num_epochs", 200)) + train_start = datetime.now(timezone.utc) + for epoch in range(start_epoch, epochs + 1): + model.train() + total_loss = 0.0 + n_batches = 0 + for a, b, mask, _ in train_loader: + a = a.to(device) + b = b.to(device) + target = mask.squeeze(1).long().to(device) + optimizer.zero_grad() + loss = _fc_loss(args.model, model(a, b), target) + loss.backward() + optimizer.step() + total_loss += loss.item() + n_batches += 1 + + model.eval() + val_metrics = BinaryMetrics(threshold=float(dataset_cfg.get("eval", {}).get("threshold", 0.5))) + val_start = datetime.now(timezone.utc) + with torch.no_grad(): + for a, b, mask, _ in val_loader: + scores = model(a.to(device), b.to(device)) + val_metrics.update(scores.detach().cpu(), mask) + val_end = datetime.now(timezone.utc) + scores = val_metrics.compute() + train_loss = total_loss / max(n_batches, 1) + is_best = scores["f1"] > best_f1 + checkpoint = { + "model": args.model, + "dataset": dataset_cfg["name"], + "epoch": epoch, + "best_epoch": best_epoch, + "best_val_f1": best_f1, + "model_state_dict": model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + } + if is_best: + best_f1 = scores["f1"] + best_epoch = epoch + checkpoint["best_epoch"] = best_epoch + checkpoint["best_val_f1"] = best_f1 + torch.save(checkpoint, best_path) + torch.save(checkpoint, latest_path) + val_payload = dict(scores) + val_payload.update({ + "model": args.model, + "dataset": dataset_cfg["name"], + "split": "val", + "epoch": epoch, + "best_epoch": best_epoch, + "checkpoint": str(best_path if is_best else latest_path), + "validation_time": (val_end - val_start).total_seconds(), + "timestamp": val_end.isoformat(), + "status": "complete", + }) + save_metrics(args.model, dataset_cfg["name"], "val", val_payload) + line = ( + f"[Epoch {epoch:03d}/{int(model_cfg.get('num_epochs', 200)):03d}] " + f"train_loss={train_loss:.4f} | val_F1={scores['f1']:.4f} " + f"val_IoU={scores['iou']:.4f} val_Prec={scores['precision']:.4f} " + f"val_Rec={scores['recall']:.4f} val_Acc={scores['accuracy']:.4f}" + f"{' | BEST' if is_best else ''}" + ) + print(line, flush=True) + log.write(line + "\n") + log.flush() + + train_end = datetime.now(timezone.utc) + test_metrics, code = evaluate_with_adapter( + model_name=args.model, + dataset_cfg=dataset_cfg, + model_config=model_cfg, + adapter=get_model_adapter(args.model), + checkpoint_path=best_path, + device=device, + strict_profiling=not args.allow_missing_profilers, + output_dir=out_dir, + ) + test_metrics["best_epoch"] = best_epoch + test_metrics["training_time"] = (train_end - train_start).total_seconds() + test_metrics["best_val_f1"] = best_f1 + save_metrics(args.model, dataset_cfg["name"], "test", test_metrics) + append_to_comparison_table() + return code + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/train/train_bifa.py b/train/train_bifa.py new file mode 100644 index 0000000000000000000000000000000000000000..50752424fb042e80a5ad9104fe3516d42ae33b33 --- /dev/null +++ b/train/train_bifa.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("bifa")) + diff --git a/train/train_bit_cd.py b/train/train_bit_cd.py new file mode 100644 index 0000000000000000000000000000000000000000..08f44613c6801eb2a0c2b07eb5d8d390654b6142 --- /dev/null +++ b/train/train_bit_cd.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("bit_cd")) + diff --git a/train/train_cdmamba.py b/train/train_cdmamba.py new file mode 100644 index 0000000000000000000000000000000000000000..03ecd2c998076f8f0d82e671088b4e9747ecf3b4 --- /dev/null +++ b/train/train_cdmamba.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("cdmamba")) + diff --git a/train/train_cgnet.py b/train/train_cgnet.py new file mode 100644 index 0000000000000000000000000000000000000000..7534788edc75d621d20964ad3f1657418577a37e --- /dev/null +++ b/train/train_cgnet.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("cgnet")) + diff --git a/train/train_change3d.py b/train/train_change3d.py new file mode 100644 index 0000000000000000000000000000000000000000..c71363462bcdc85d67afec8f46a2a1036a2387fa --- /dev/null +++ b/train/train_change3d.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("change3d")) + diff --git a/train/train_changeformer.py b/train/train_changeformer.py new file mode 100644 index 0000000000000000000000000000000000000000..393e38ea4b375e84368aa174f35e256ff8302959 --- /dev/null +++ b/train/train_changeformer.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("changeformer")) + diff --git a/train/train_changemamba.py b/train/train_changemamba.py new file mode 100644 index 0000000000000000000000000000000000000000..2dec13685bda28787150a701aae094a80d1f24f0 --- /dev/null +++ b/train/train_changemamba.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +import os +import shutil +import sys +from pathlib import Path + +LOCAL_ROOT = Path(__file__).resolve().parents[1] +if str(LOCAL_ROOT) not in sys.path: + sys.path.insert(0, str(LOCAL_ROOT)) + +from utils.weight_downloader import WEIGHT_REGISTRY, ensure_weights +from wrapper_common import ROOT, main_for + + +def _safe_link_or_copy(src: Path, dst: Path) -> None: + if dst.exists(): + return + try: + os.symlink(src, dst, target_is_directory=True) + except OSError: + print(f"[ChangeMamba] WARNING: symlink failed for {dst}; copying directory tree.") + shutil.copytree(src, dst, dirs_exist_ok=True) + + +def make_changemamba_dataset_config(dataset_cfg: dict, split: str, output_path: str) -> str: + data_root = Path(dataset_cfg["data_root"]) + split_dir = data_root / dataset_cfg.get("splits", {}).get(split, split) + mapping = { + "T1": split_dir / dataset_cfg.get("image_a_folder", "A"), + "T2": split_dir / dataset_cfg.get("image_b_folder", "B"), + "GT": split_dir / dataset_cfg.get("mask_folder", "label"), + } + for link_name, src in mapping.items(): + if src.exists(): + _safe_link_or_copy(src, split_dir / link_name) + + out = Path(output_path) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text( + "\n".join([ + "# Generated by train/train_changemamba.py", + f"dataset_name = {dataset_cfg['name']!r}", + f"data_root = {str(data_root)!r}", + f"crop_size = {int(dataset_cfg.get('img_size', 256))!r}", + f"batch_size = {int(dataset_cfg.get('batch_size', 8))!r}", + f"num_workers = {int(dataset_cfg.get('num_workers', 4))!r}", + f"mean = {dataset_cfg.get('mean_a', [0.485, 0.456, 0.406])!r}", + f"std = {dataset_cfg.get('std_a', [0.229, 0.224, 0.225])!r}", + f"train_split = {dataset_cfg.get('splits', {}).get('train', 'train')!r}", + f"val_split = {dataset_cfg.get('splits', {}).get('val', 'val')!r}", + f"test_split = {dataset_cfg.get('splits', {}).get('test', 'test')!r}", + "", + ]), + encoding="utf-8", + ) + return str(out) + + +def ensure_changemamba_weights(cfg: dict) -> str: + variant_key = f"vmamba_{cfg.get('vmamba_variant', 'tiny')}" + if variant_key not in WEIGHT_REGISTRY: + raise KeyError(f"Unknown ChangeMamba VMamba variant: {variant_key}") + return ensure_weights(variant_key) + + +if __name__ == "__main__": + raise SystemExit(main_for("changemamba")) diff --git a/train/train_changer.py b/train/train_changer.py new file mode 100644 index 0000000000000000000000000000000000000000..07b64080efa0f70cf08f29be3e7c907af4ffba59 --- /dev/null +++ b/train/train_changer.py @@ -0,0 +1,31 @@ +from __future__ import annotations + +from pathlib import Path + +from wrapper_common import ROOT, main_for + + +def make_changer_config(dataset_cfg: dict, output_path: str) -> str: + template = ROOT / "model_repos/open-cd/configs/changer/changer_ex_r18_512x512_40k_levircd.py" + out = Path(output_path) + out.parent.mkdir(parents=True, exist_ok=True) + rel_template = template.relative_to(out.parent).as_posix() if template.is_relative_to(out.parent) else str(template) + out.write_text( + "\n".join([ + "# Generated by train/train_changer.py", + f"_base_ = [{rel_template!r}]", + f"data_root = {dataset_cfg['data_root']!r}", + f"crop_size = ({int(dataset_cfg.get('img_size', 256))}, {int(dataset_cfg.get('img_size', 256))})", + f"train_dataloader = dict(batch_size={int(dataset_cfg.get('batch_size', 8))}, num_workers={int(dataset_cfg.get('num_workers', 4))}, dataset=dict(data_root=data_root))", + f"val_dataloader = dict(batch_size=1, num_workers={int(dataset_cfg.get('num_workers', 4))}, dataset=dict(data_root=data_root))", + f"test_dataloader = dict(batch_size=1, num_workers={int(dataset_cfg.get('num_workers', 4))}, dataset=dict(data_root=data_root))", + f"data_preprocessor = dict(mean={dataset_cfg.get('mean_a', [0.485, 0.456, 0.406])!r}, std={dataset_cfg.get('std_a', [0.229, 0.224, 0.225])!r})", + "", + ]), + encoding="utf-8", + ) + return str(out) + + +if __name__ == "__main__": + raise SystemExit(main_for("changer")) diff --git a/train/train_dsamnet.py b/train/train_dsamnet.py new file mode 100644 index 0000000000000000000000000000000000000000..ac83f2784a08c533c5475fa09dc3d0c9f029f4de --- /dev/null +++ b/train/train_dsamnet.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +import sys +from pathlib import Path + +LOCAL_ROOT = Path(__file__).resolve().parents[1] +if str(LOCAL_ROOT) not in sys.path: + sys.path.insert(0, str(LOCAL_ROOT)) + +from utils.weight_downloader import ensure_torchvision_weight +from wrapper_common import ROOT, main_for + + +DSAMNET_ROOT = ROOT / "model_repos" / "DSAMNet" +if DSAMNET_ROOT.exists(): + sys.path.insert(0, str(DSAMNET_ROOT)) + + +def ensure_dsamnet_backbone() -> None: + ensure_torchvision_weight("resnet18") + + +if __name__ == "__main__": + raise SystemExit(main_for("dsamnet")) diff --git a/train/train_dsifn.py b/train/train_dsifn.py new file mode 100644 index 0000000000000000000000000000000000000000..2ab8715ab588b70cb51e5dc6132e607870b053f9 --- /dev/null +++ b/train/train_dsifn.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("dsifn")) + diff --git a/train/train_elgcnet.py b/train/train_elgcnet.py new file mode 100644 index 0000000000000000000000000000000000000000..f3da253bda8baee90578209c964961c8c88ba60e --- /dev/null +++ b/train/train_elgcnet.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("elgcnet")) + diff --git a/train/train_fc_variants.py b/train/train_fc_variants.py new file mode 100644 index 0000000000000000000000000000000000000000..5d1d95e223485a58984a3e8015d1c292f39f944a --- /dev/null +++ b/train/train_fc_variants.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("fc_variants")) + diff --git a/train/train_hanet.py b/train/train_hanet.py new file mode 100644 index 0000000000000000000000000000000000000000..65f4686735718bc1e33961d670055d9ac08722e3 --- /dev/null +++ b/train/train_hanet.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("hanet")) + diff --git a/train/train_ifnet.py b/train/train_ifnet.py new file mode 100644 index 0000000000000000000000000000000000000000..91b0528bb0ebf5feda8344f26a76ce382870a807 --- /dev/null +++ b/train/train_ifnet.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("ifnet")) + diff --git a/train/train_rsm_cd.py b/train/train_rsm_cd.py new file mode 100644 index 0000000000000000000000000000000000000000..2065fd44619e731dc1ad22ad0163ff6952ac0f5d --- /dev/null +++ b/train/train_rsm_cd.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("rsm_cd")) + diff --git a/train/train_schanger.py b/train/train_schanger.py new file mode 100644 index 0000000000000000000000000000000000000000..5797e49692476f1286126506ae075036d5469c2a --- /dev/null +++ b/train/train_schanger.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("schanger")) + diff --git a/train/train_siam_nestedunet.py b/train/train_siam_nestedunet.py new file mode 100644 index 0000000000000000000000000000000000000000..390eae43c6e8e9a20636c6e97df373580277909f --- /dev/null +++ b/train/train_siam_nestedunet.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("siam_nestedunet")) + diff --git a/train/train_stanet.py b/train/train_stanet.py new file mode 100644 index 0000000000000000000000000000000000000000..f5a18c0e8a0dcc3412e4d26d4bfb3821138da05c --- /dev/null +++ b/train/train_stanet.py @@ -0,0 +1,3 @@ +from wrapper_common import main_for +raise SystemExit(main_for("stanet")) + diff --git a/train/train_tinycd.py b/train/train_tinycd.py new file mode 100644 index 0000000000000000000000000000000000000000..57b57f75970ff13313135bf00b942386e8bf1375 --- /dev/null +++ b/train/train_tinycd.py @@ -0,0 +1,19 @@ +from __future__ import annotations + +import sys +from pathlib import Path + +LOCAL_ROOT = Path(__file__).resolve().parents[1] +if str(LOCAL_ROOT) not in sys.path: + sys.path.insert(0, str(LOCAL_ROOT)) + +from utils.weight_downloader import ensure_timm_weight +from wrapper_common import main_for + + +def ensure_tinycd_backbone() -> None: + ensure_timm_weight("efficientnet_b4") + + +if __name__ == "__main__": + raise SystemExit(main_for("tinycd")) diff --git a/train/wrapper_common.py b/train/wrapper_common.py new file mode 100644 index 0000000000000000000000000000000000000000..bd01f18b5a24abf658ac0126be176032cfc15603 --- /dev/null +++ b/train/wrapper_common.py @@ -0,0 +1,1120 @@ +from __future__ import annotations + +import argparse +import json +import os +import shutil +import subprocess +import sys +from pathlib import Path + +import torch + + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from utils.gpu_utils import ( + gpu_mapping_message, + print_gpu_diagnostics, + resolve_gpu, + subprocess_gpu_env, +) +from utils.dataset_cache import apply_dataloader_cli_overrides, dataset_runtime_summary, print_dataloader_policy + +SKIP_EXIT_CODE = 75 +FAILED_SUBPROCESS_EXIT_CODE = 70 +FAILED_NO_CHECKPOINT_EXIT_CODE = 71 +FAILED_EVAL_EXIT_CODE = 72 + +EXISTING_LEGACY = { + "bifa": ("BiFA", ["python3", "train_wildfire.py"]), + "bit_cd": ("BIT_CD", ["python3", "train_wildfire.py"]), + "cdmamba": ("CDMamba", ["python3", "train_wildfire.py"]), + "change3d": ("Change3D", ["python3", "train_wildfire.py"]), + "changeformer": ("ChangeFormer", ["python3", "train_wildfire.py"]), + "dsifn": ("IFNet", ["python3", "train_wildfire.py"]), + "ifnet": ("IFNet", ["python3", "train_wildfire.py"]), + "rsm_cd": ("RSM-CD/change_detection_mamba", ["python3", "train_wildfire.py"]), + "schanger": ("SChanger", ["python3", "train_wildfire.py"]), + "siam_nestedunet": ("Siam-NestedUNet", ["python3", "train_wildfire.py"]), + "stanet": ("STANet", ["python3", "train_wildfire.py"]), +} + +JSON_LEGACY_TRAINERS = {"bifa", "cdmamba"} +MAIN_CD_TRAINERS = {"bit_cd", "changeformer"} + +NEW_REPOS = { + "fc_ef": ("model_repos/fully_convolutional_change_detection", "https://github.com/rcdaudt/fully_convolutional_change_detection"), + "fc_siam_conc": ("model_repos/fully_convolutional_change_detection", "https://github.com/rcdaudt/fully_convolutional_change_detection"), + "fc_siam_diff": ("model_repos/fully_convolutional_change_detection", "https://github.com/rcdaudt/fully_convolutional_change_detection"), + "dsifn": ("model_repos/DSIFN", "original DSIFN repo still requires URL confirmation"), + "changemamba": ("model_repos/ChangeMamba", "https://github.com/ChenHongruixuan/ChangeMamba"), + "elgcnet": ("model_repos/elgcnet", "https://github.com/techmn/elgcnet"), + "changer": ("model_repos/open-cd", "https://github.com/likyoo/open-cd"), + "hanet": ("model_repos/HANet-CD", "https://github.com/ChengxiHAN/HANet-CD"), + "cgnet": ("model_repos/CGNet-CD", "https://github.com/ChengxiHAN/CGNet-CD"), + "dsamnet": ("model_repos/DSAMNet", "https://github.com/liumency/DSAMNet"), + "tinycd": ("model_repos/Tiny_model_4_CD", "https://github.com/AndreaCodegoni/Tiny_model_4_CD"), +} + +TORCHVISION_WEIGHTS = { + "changer": "resnet18", + "dsamnet": "resnet18", + "hanet": "resnet50", + "cgnet": "resnet50", + "dsifn": "vgg16", +} + +TIMM_WEIGHTS = { + "tinycd": "efficientnet_b4", + "elgcnet": "mit_b0", + "changeformer": "mit_b1", +} + +EXTERNAL_TRAINABLE = {"dsamnet", "cgnet", "hanet", "tinycd"} + + +def parse_args(model_name: str) -> argparse.Namespace: + parser = argparse.ArgumentParser(description=f"Train/evaluate {model_name} on a cd-models dataset.") + parser.add_argument("--dataset", default="wildfire_s2") + parser.add_argument("--epochs", type=int, default=None) + parser.add_argument("--batch-size", type=int, default=None) + parser.add_argument("--lr", type=float, default=None) + parser.add_argument("--gpu", default="0") + parser.add_argument("--resume", action="store_true") + parser.add_argument("--eval-only", action="store_true") + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--smoke-test", action="store_true") + parser.add_argument("--force", action="store_true") + parser.add_argument("--output-dir", default=None) + parser.add_argument("--model", default=model_name, help="Variant selector used by FC-Siam wrappers.") + parser.add_argument("--max-iters", type=int, default=None, help="Iteration override for iteration-based upstream trainers.") + parser.add_argument("--num-workers", type=int, default=None) + parser.add_argument("--prefetch-factor", type=int, default=None) + parser.set_defaults(persistent_workers=None, pin_memory=None) + parser.add_argument("--persistent-workers", dest="persistent_workers", action="store_true") + parser.add_argument("--no-persistent-workers", dest="persistent_workers", action="store_false") + parser.add_argument("--pin-memory", dest="pin_memory", action="store_true") + parser.add_argument("--no-pin-memory", dest="pin_memory", action="store_false") + return parser.parse_args() + + +def _ensure_pretrained(model_name: str, variant: str = "tiny") -> str | None: + from utils.weight_downloader import ensure_timm_weight, ensure_torchvision_weight, ensure_weights + + if model_name == "changemamba": + return ensure_weights(f"vmamba_{variant}") + if model_name in TIMM_WEIGHTS: + ensure_timm_weight(TIMM_WEIGHTS[model_name], required=model_name == "tinycd") + if model_name in TORCHVISION_WEIGHTS: + ensure_torchvision_weight(TORCHVISION_WEIGHTS[model_name]) + return None + + +def _dataset_ready(dataset_name: str, return_format: str = "tuple", dry_run: bool = False) -> dict: + from datasets.cd_dataset import CDDataset + from utils.config_loader import load_dataset_config + + cfg = load_dataset_config(dataset_name) + root_exists = Path(cfg["data_root"]).exists() + print( + f"[DATASET] {cfg['name']} root={cfg['data_root']} img_size={cfg.get('img_size')} " + f"channels={cfg.get('channels')} return_format={return_format}" + ) + print(f"[DATASET] {dataset_runtime_summary(cfg)}") + print_dataloader_policy(cfg, torch.cuda.is_available()) + if cfg.get("io_warning"): + print(f"[DATASET-WARNING] {cfg['io_warning']}") + print(f"[DRY-RUN] Dataset root exists: {root_exists} -> {cfg['data_root']}" if dry_run else f"[DATASET] Root exists: {root_exists}") + if dry_run: + return cfg + if not root_exists: + raise FileNotFoundError( + f"Dataset root directory not found for {dataset_name}: {cfg['data_root']}. " + "Set DATA_ROOT or edit the dataset YAML." + ) + try: + from utils.dataset_list_generator import generate_list_files + + list_dir = Path(cfg["data_root"]) / "list" + if not (list_dir / "train.txt").exists(): + print(f"[SETUP] Generating list files for {dataset_name}...") + generate_list_files( + cfg["data_root"], + splits=list((cfg.get("splits") or {"train": "train", "val": "val", "test": "test"}).keys()), + img_subdir=cfg.get("image_a_folder", "A"), + ) + except Exception as exc: + print(f"[SETUP] List-file generation skipped: {exc}") + if not Path(cfg["data_root"]).exists(): + print(f"[DATASET] Root does not exist yet, skipping CDDataset construction: {cfg['data_root']}") + return cfg + _ = CDDataset(cfg["data_root"], "train", cfg=cfg, return_format=return_format) + return cfg + + +def _run_logged(cmd: list[str], cwd: Path, env: dict, stdout_path: Path, stderr_path: Path) -> int: + stdout_path.parent.mkdir(parents=True, exist_ok=True) + stderr_path.parent.mkdir(parents=True, exist_ok=True) + visible = env.get("CUDA_VISIBLE_DEVICES") + if visible: + print(_gpu_mapping_message(env)) + with stdout_path.open("w", encoding="utf-8") as stdout, stderr_path.open("w", encoding="utf-8") as stderr: + return subprocess.run(cmd, cwd=cwd, env=env, stdout=stdout, stderr=stderr, check=False).returncode + + +def _run_streamed(cmd: list[str], cwd: Path, env: dict, log_path: Path) -> int: + log_path.parent.mkdir(parents=True, exist_ok=True) + with log_path.open("a", encoding="utf-8") as log: + log.write("\n[WRAPPER] cwd=" + str(cwd) + "\n") + log.write("[WRAPPER] " + _gpu_mapping_message(env) + "\n") + log.write("[WRAPPER] command=" + " ".join(cmd) + "\n") + log.flush() + process = subprocess.Popen( + cmd, + cwd=cwd, + env=env, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, + ) + assert process.stdout is not None + for line in process.stdout: + print(line, end="", flush=True) + log.write(line) + log.flush() + return process.wait() + + +def _gpu_mapping_message(env: dict) -> str: + return gpu_mapping_message(env=env) + + +def _print_train_command(cwd: Path, cmd: list[str], env: dict) -> None: + print(_gpu_mapping_message(env)) + print("[TRAIN]", cwd, " ".join(cmd)) + + +def _evaluate(model_name: str, dataset: str, gpu: str, dry_run: bool) -> int: + cmd = [sys.executable, str(ROOT / "evaluate.py"), "--model", model_name, "--dataset", dataset, "--gpu", gpu] + print("[EVAL]", " ".join(cmd)) + if dry_run: + return 0 + log_dir = ROOT / "results" / model_name / dataset / "logs" + env, _ = subprocess_gpu_env(gpu) + return _run_logged(cmd, ROOT, env, log_dir / "eval_stdout.log", log_dir / "eval_stderr.log") + + +def _legacy_checkpoint_candidates(model_name: str, dataset_name: str) -> list[Path]: + if model_name != "cdmamba": + return [] + + experiments = ROOT / "CDMamba" / "experiments" + if not experiments.exists(): + return [] + + candidates: list[Path] = [] + run_dirs = sorted( + ( + path + for path in experiments.glob(f"{dataset_name}-train-cdmamba_*") + if path.is_dir() + ), + key=lambda path: path.stat().st_mtime, + ) + preferred_patterns = [ + "checkpoint/best_cd_model_gen.pth", + "checkpoint/*_gen.pth", + "checkpoint/*best*.pth", + "checkpoint/*.pth", + "**/*.pth", + "**/*.pt", + ] + for run_dir in run_dirs: + for pattern in preferred_patterns: + matches = [ + path + for path in sorted(run_dir.glob(pattern), key=lambda item: item.stat().st_mtime) + if path.is_file() and "_opt" not in path.name.lower() + ] + if matches: + candidates.extend(matches) + break + return candidates + + +def _canonicalize_checkpoints(model_name: str, dataset_name: str) -> None: + out_dir = ROOT / "results" / model_name / dataset_name + ckpt_dir = out_dir / "checkpoints" + ckpt_dir.mkdir(parents=True, exist_ok=True) + best_out = ckpt_dir / "best_model.pth" + latest_out = ckpt_dir / "latest.pth" + if best_out.exists(): + return + patterns = [ + "**/best_model.pth", + "**/best_ckpt.pth", + "**/best_ckpt.pt", + "**/*best*.pth", + "**/*best*.pt", + "**/*_best_iou.pth", + "**/netCD_epoch_*.pth", + "**/*.pth", + "**/*.pt", + ] + candidates: list[Path] = [] + for pattern in patterns: + for path in sorted(out_dir.glob(pattern)): + if path.is_file() and path.resolve() != best_out.resolve(): + candidates.append(path) + if candidates: + break + if not candidates: + candidates = _legacy_checkpoint_candidates(model_name, dataset_name) + if not candidates: + raise FileNotFoundError( + f"Training completed for {model_name}/{dataset_name}, but no checkpoint file was found under " + f"{out_dir} or known legacy output directories." + ) + source = candidates[-1] + shutil.copy2(source, best_out) + if not latest_out.exists(): + shutil.copy2(source, latest_out) + metadata = { + "model": model_name, + "dataset": dataset_name, + "canonical_best_model": str(best_out), + "source_checkpoint": str(source), + "source_checkpoint_name": source.name, + } + (ckpt_dir / "checkpoint_metadata.json").write_text(json.dumps(metadata, indent=2), encoding="utf-8") + + +def _checkpoint_files(checkpoint_dir: Path) -> list[Path]: + suffixes = {".pth", ".pt", ".ckpt"} + if not checkpoint_dir.exists(): + return [] + return sorted(path for path in checkpoint_dir.rglob("*") if path.is_file() and path.suffix.lower() in suffixes) + + +def _print_checkpoint_failure(model_name: str, dataset_name: str, cmd: list[str], checkpoint_dir: Path, result_dir: Path) -> None: + print(f"[FAILED_NO_CHECKPOINT] {model_name}/{dataset_name}: upstream command returned successfully but no checkpoint was found.") + print(f"[FAILED_NO_CHECKPOINT] command={' '.join(cmd)}") + print(f"[FAILED_NO_CHECKPOINT] expected checkpoint dir={checkpoint_dir}") + if result_dir.exists(): + existing = [str(path.relative_to(result_dir)) for path in sorted(result_dir.rglob("*"))[:80]] + print(f"[FAILED_NO_CHECKPOINT] existing result files={existing}") + else: + print(f"[FAILED_NO_CHECKPOINT] result dir does not exist: {result_dir}") + + +def _iteration_budget(model_name: str, model_cfg: dict, args: argparse.Namespace, default: int) -> tuple[int, str]: + if args.max_iters is not None: + return int(args.max_iters), "cli --max-iters" + if args.smoke_test: + return int(model_cfg.get("smoke_max_iters", min(default, 2))), "model smoke_max_iters" + if "max_iters" in model_cfg: + return int(model_cfg["max_iters"]), "model max_iters" + return default, f"{model_name} wrapper default" + + +def _model_config(model_name: str) -> dict: + from utils.config_loader import load_model_config + + return load_model_config(model_name) + + +def _legacy_dataset_token(dataset_cfg: dict) -> str: + name = str(dataset_cfg.get("name", "")).lower() + source = str(dataset_cfg.get("source_name", "")).lower() + if "dsifn" in name or "dsifn" in source: + return "DSIFN" + if "levir" in name or "levir" in source: + return "LEVIR" + if "whu" in name or "whu" in source: + return "WHU" + if "wildfire" in name or "wildfire" in source: + return "WildFireS2" + return dataset_cfg.get("source_name", dataset_cfg["name"]) + + +def _split_dir(dataset_cfg: dict, split: str) -> Path: + return Path(dataset_cfg["data_root"]) / dataset_cfg.get("splits", {}).get(split, split) + + +def _safe_link_or_copy(src: Path, dst: Path) -> None: + if dst.is_symlink(): + try: + if Path(os.readlink(dst)) == src: + return + except OSError: + pass + dst.unlink() + if dst.exists(): + return + dst.parent.mkdir(parents=True, exist_ok=True) + if not src.exists(): + print(f"[DATASET-VIEW] Source path is not present yet, skipping generated link: {src}") + return + try: + os.symlink(src, dst, target_is_directory=src.is_dir()) + except OSError: + if src.is_dir(): + shutil.copytree(src, dst, dirs_exist_ok=True) + else: + shutil.copy2(src, dst) + + +def _safe_mask_link_or_copy(src: Path, dst: Path) -> None: + dst.parent.mkdir(parents=True, exist_ok=True) + if not src.exists(): + print(f"[DATASET-VIEW] Source mask is not present yet, skipping generated link: {src}") + return + + try: + import numpy as np + from PIL import Image + + arr = np.asarray(Image.open(src)) + if arr.ndim == 3: + arr = arr[..., 0] + if arr.size and int(arr.max()) <= 1: + if dst.exists() or dst.is_symlink(): + dst.unlink() + Image.fromarray((arr > 0).astype(np.uint8) * 255).save(dst, format="PNG") + return + except Exception as exc: + print(f"[DATASET-VIEW] Could not inspect mask {src}: {exc}") + + _safe_link_or_copy(src, dst) + + +def _scan_images(folder: Path) -> dict[str, Path]: + exts = {".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp"} + if not folder.is_dir(): + return {} + return { + p.stem: p + for p in sorted(folder.iterdir()) + if p.is_file() and p.suffix.lower() in exts + } + + +def _view_split_counts(dataset_cfg: dict) -> dict[str, int]: + counts: dict[str, int] = {} + for split in ("train", "val", "test"): + split_root = _split_dir(dataset_cfg, split) + a_files = _scan_images(split_root / dataset_cfg.get("image_a_folder", "A")) + b_files = _scan_images(split_root / dataset_cfg.get("image_b_folder", "B")) + label_files = _scan_images(split_root / dataset_cfg.get("mask_folder", "label")) + counts[split] = len(set(a_files) & set(b_files) & set(label_files)) + return counts + + +def _log_generated_view(dataset_cfg: dict, view: Path) -> None: + counts = _view_split_counts(dataset_cfg) + print(f"[DATASET] dataset name: {dataset_cfg['name']}") + print(f"[DATASET] original root: {dataset_cfg.get('original_data_root', dataset_cfg.get('data_root'))}") + print(f"[DATASET] local root: {dataset_cfg.get('local_root')}") + print(f"[DATASET] using local root: {bool(dataset_cfg.get('using_local_root'))}") + print(f"[DATASET] generated view path: {view}") + print(f"[DATASET] train/val/test counts: {counts}") + + +def _prepare_matched_split_view(dataset_cfg: dict, split: str) -> Path: + split_root = _split_dir(dataset_cfg, split) + view = ROOT / "generated_dataset_views" / dataset_cfg["name"] / "matched" / split + a_files = _scan_images(split_root / dataset_cfg.get("image_a_folder", "A")) + b_files = _scan_images(split_root / dataset_cfg.get("image_b_folder", "B")) + label_files = _scan_images(split_root / dataset_cfg.get("mask_folder", "label")) + for folder in ("A", "B", "label"): + (view / folder).mkdir(parents=True, exist_ok=True) + for stem in sorted(set(a_files) & set(b_files) & set(label_files)): + target_name = a_files[stem].name + _safe_link_or_copy(a_files[stem], view / "A" / target_name) + _safe_link_or_copy(b_files[stem], view / "B" / target_name) + _safe_mask_link_or_copy(label_files[stem], view / "label" / target_name) + _log_generated_view(dataset_cfg, view) + return view + + +def _prepare_split_view(dataset_cfg: dict, split: str) -> Path: + split_root = _split_dir(dataset_cfg, split) + view = ROOT / "generated_dataset_views" / dataset_cfg["name"] / split + mapping = { + "A": split_root / dataset_cfg.get("image_a_folder", "A"), + "B": split_root / dataset_cfg.get("image_b_folder", "B"), + "label": split_root / dataset_cfg.get("mask_folder", "label"), + } + for target, src in mapping.items(): + _safe_link_or_copy(src, view / target) + _log_generated_view(dataset_cfg, view) + return view + + +def _prepare_tinycd_view(dataset_cfg: dict) -> Path: + view = ROOT / "generated_dataset_views" / dataset_cfg["name"] / "tinycd" + list_dir = view / "list" + list_dir.mkdir(parents=True, exist_ok=True) + for folder in ("A", "B", "label"): + (view / folder).mkdir(parents=True, exist_ok=True) + for split in ("train", "val", "test"): + split_root = _split_dir(dataset_cfg, split) + a_files = _scan_images(split_root / dataset_cfg.get("image_a_folder", "A")) + b_files = _scan_images(split_root / dataset_cfg.get("image_b_folder", "B")) + label_files = _scan_images(split_root / dataset_cfg.get("mask_folder", "label")) + names = [] + for stem in sorted(set(a_files) & set(b_files) & set(label_files)): + target_name = f"{split}__{a_files[stem].name}" + _safe_link_or_copy(a_files[stem], view / "A" / target_name) + _safe_link_or_copy(b_files[stem], view / "B" / target_name) + _safe_mask_link_or_copy(label_files[stem], view / "label" / target_name) + names.append(target_name) + (list_dir / f"{split}.txt").write_text("\n".join(names) + ("\n" if names else ""), encoding="utf-8") + _log_generated_view(dataset_cfg, view) + return view + + +def _prepare_changemamba_view(dataset_cfg: dict) -> Path: + view = ROOT / "generated_dataset_views" / dataset_cfg["name"] / "changemamba" + split_map = {"train": "train", "test": "val"} + for target_split, source_split in split_map.items(): + for folder in ("T1", "T2", "GT"): + (view / target_split / folder).mkdir(parents=True, exist_ok=True) + split_root = _split_dir(dataset_cfg, source_split) + a_files = _scan_images(split_root / dataset_cfg.get("image_a_folder", "A")) + b_files = _scan_images(split_root / dataset_cfg.get("image_b_folder", "B")) + label_files = _scan_images(split_root / dataset_cfg.get("mask_folder", "label")) + names = [] + for stem in sorted(set(a_files) & set(b_files) & set(label_files)): + target_name = f"{source_split}__{a_files[stem].name}" + _safe_link_or_copy(a_files[stem], view / target_split / "T1" / target_name) + _safe_link_or_copy(b_files[stem], view / target_split / "T2" / target_name) + _safe_mask_link_or_copy(label_files[stem], view / target_split / "GT" / target_name) + names.append(target_name) + list_name = "train_set.txt" if target_split == "train" else "test_set.txt" + (view / list_name).write_text("\n".join(names) + ("\n" if names else ""), encoding="utf-8") + _log_generated_view(dataset_cfg, view) + return view + + +def _prepare_opencd_view(dataset_cfg: dict) -> Path: + view = ROOT / "generated_dataset_views" / dataset_cfg["name"] / "opencd" + for split in ("train", "val", "test"): + for folder in ("A", "B", "label"): + (view / split / folder).mkdir(parents=True, exist_ok=True) + split_root = _split_dir(dataset_cfg, split) + a_files = _scan_images(split_root / dataset_cfg.get("image_a_folder", "A")) + b_files = _scan_images(split_root / dataset_cfg.get("image_b_folder", "B")) + label_files = _scan_images(split_root / dataset_cfg.get("mask_folder", "label")) + for stem in sorted(set(a_files) & set(b_files) & set(label_files)): + image_name = a_files[stem].name + label_name = f"{a_files[stem].stem}{label_files[stem].suffix}" + _safe_link_or_copy(a_files[stem], view / split / "A" / image_name) + _safe_link_or_copy(b_files[stem], view / split / "B" / image_name) + _safe_link_or_copy(label_files[stem], view / split / "label" / label_name) + _log_generated_view(dataset_cfg, view) + return view + + +def _write_opencd_changer_config(dataset_cfg: dict, model_cfg: dict, view: Path) -> Path: + train_split = _split_dir(dataset_cfg, "train") + n_train = len(_scan_images(train_split / dataset_cfg.get("image_a_folder", "A"))) + batch_size = int(dataset_cfg.get("batch_size", 8)) + steps_per_epoch = max((n_train + batch_size - 1) // batch_size, 1) + max_iters = int(model_cfg.get("max_iters", int(model_cfg.get("num_epochs", 200)) * steps_per_epoch)) + val_interval = max(steps_per_epoch, 1) + cfg_path = ROOT / "generated_configs" / f"{dataset_cfg['name']}__changer_opencd.py" + cfg_path.parent.mkdir(parents=True, exist_ok=True) + cfg_path.write_text( + "\n".join( + [ + f"_base_ = '{ROOT / 'model_repos' / 'open-cd' / 'configs' / 'changer' / 'changer_ex_r18_512x512_40k_levircd.py'}'", + "", + "dataset_type = 'DSIFN_Dataset'", + f"data_root = r'{view}'", + f"crop_size = ({int(dataset_cfg.get('img_size', model_cfg.get('img_size', 256)))}, {int(dataset_cfg.get('img_size', model_cfg.get('img_size', 256)))})", + "", + "train_dataloader = dict(", + f" batch_size={batch_size},", + f" num_workers={int(dataset_cfg.get('num_workers', 4))},", + " dataset=dict(", + " type=dataset_type,", + " data_root=data_root,", + " data_prefix=dict(seg_map_path='train/label', img_path_from='train/A', img_path_to='train/B')))", + "val_dataloader = dict(", + " dataset=dict(", + " type=dataset_type,", + " data_root=data_root,", + " data_prefix=dict(seg_map_path='val/label', img_path_from='val/A', img_path_to='val/B')))", + "test_dataloader = dict(", + " dataset=dict(", + " type=dataset_type,", + " data_root=data_root,", + " data_prefix=dict(seg_map_path='test/label', img_path_from='test/A', img_path_to='test/B')))", + "", + f"train_cfg = dict(type='IterBasedTrainLoop', max_iters={max_iters}, val_interval={val_interval})", + f"work_dir = r'{ROOT / 'results' / 'changer' / dataset_cfg['name'] / 'work_dir'}'", + "", + ] + ), + encoding="utf-8", + ) + return cfg_path + + +def _write_hanet_metadata(dataset_cfg: dict, model_cfg: dict, view_root: Path) -> Path: + out_dir = ROOT / "results" / "hanet" / dataset_cfg["name"] + metadata = { + "patch_size": int(dataset_cfg.get("img_size", 256)), + "augmentation": True, + "num_gpus": 1, + "num_workers": int(dataset_cfg.get("num_workers", 4)), + "num_channel": 3, + "EF": False, + "epochs": int(model_cfg.get("num_epochs", 50)), + "epochs_threshold": 15, + "gamma": 0.5, + "weight_decay": float(model_cfg.get("weight_decay", 5e-4) or 5e-4), + "batch_size": int(dataset_cfg.get("batch_size", 8)), + "learning_rate": float(model_cfg.get("lr", 5e-4)), + "loss_function": "hybrid", + "dataset_dir": str(view_root) + "/", + "weight_dir": str(out_dir / "weights") + "/", + "Output_dir": str(out_dir / "outputs") + "/", + "log_dir": str(out_dir / "logs"), + } + path = ROOT / "generated_configs" / f"{dataset_cfg['name']}__hanet_metadata.json" + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(metadata, indent=2), encoding="utf-8") + return path + + +def _existing_train_command(model_name: str, dataset_cfg: dict, args: argparse.Namespace) -> tuple[Path, list[str], dict]: + workdir_rel, default_cmd = EXISTING_LEGACY[model_name] + workdir = ROOT / workdir_rel + env, _ = subprocess_gpu_env(args.gpu) + child_gpu = "0" + env["CD_MODELS_DATASET_ROOT"] = dataset_cfg["data_root"] + env["CD_MODELS_DSIFN_ROOT"] = dataset_cfg["data_root"] + env["CD_MODELS_DATASET_NAME"] = dataset_cfg["name"] + env["CD_MODELS_NUM_WORKERS"] = str(int(dataset_cfg.get("num_workers", 2))) + env["CD_MODELS_PREFETCH_FACTOR"] = str(int(dataset_cfg.get("prefetch_factor", 2))) + env["CD_MODELS_PERSISTENT_WORKERS"] = "1" if dataset_cfg.get("persistent_workers") else "0" + env["CD_MODELS_PIN_MEMORY"] = "1" if dataset_cfg.get("pin_memory", True) else "0" + env["CD_MODELS_BATCH_SIZE"] = str(int(args.batch_size or dataset_cfg.get("batch_size", 8))) + if args.epochs is not None: + env["CD_MODELS_EPOCHS"] = str(int(args.epochs)) + if dataset_cfg["name"] == "wildfire_s2": + return workdir, [sys.executable, default_cmd[1]], env + if model_name in JSON_LEGACY_TRAINERS: + from utils.legacy_config_writer import write_bifa_or_cdmamba_config + + model_cfg = _model_config(model_name) + if args.epochs is not None: + model_cfg["num_epochs"] = int(args.epochs) + if args.lr is not None: + model_cfg["lr"] = float(args.lr) + if args.batch_size is not None: + dataset_cfg["batch_size"] = int(args.batch_size) + config_path = write_bifa_or_cdmamba_config( + model_name, + dataset_cfg, + model_cfg, + prepare_view=not args.dry_run, + ) + return workdir, [sys.executable, "train_cd.py", "--config", str(config_path), "--phase", "train", "--gpu_ids", child_gpu], env + if model_name in MAIN_CD_TRAINERS: + from utils.legacy_config_writer import prepare_legacy_list_view + + legacy_root = ( + ROOT / "generated_dataset_views" / dataset_cfg["name"] / "legacy_list" + if args.dry_run + else prepare_legacy_list_view(dataset_cfg) + ) + env["CD_MODELS_DATASET_ROOT"] = str(legacy_root) + env["CD_MODELS_DSIFN_ROOT"] = str(legacy_root) + model_cfg = _model_config(model_name) + token = _legacy_dataset_token(dataset_cfg) + project = f"{dataset_cfg['name']}-train-{model_name}" + if model_name == "bit_cd": + net_g = "base_transformer_pos_s4_dd8" + optimizer = "sgd" + loss = "ce" + lr = float(model_cfg.get("lr", 0.01)) + else: + net_g = "ChangeFormerV6" + optimizer = "adamw" + loss = "ce" + lr = float(model_cfg.get("lr", 0.00006)) + cmd = [ + sys.executable, + "main_cd.py", + "--gpu_ids", + child_gpu, + "--project_name", + project, + "--data_name", + token, + "--img_size", + str(int(dataset_cfg.get("img_size", model_cfg.get("img_size", 256)))), + "--batch_size", + str(int(dataset_cfg.get("batch_size", 8))), + "--num_workers", + str(int(dataset_cfg.get("num_workers", 4))), + "--max_epochs", + str(int(model_cfg.get("num_epochs", 200))), + "--optimizer", + optimizer, + "--lr", + str(lr), + "--loss", + loss, + "--net_G", + net_g, + ] + return workdir, cmd, env + from utils.legacy_config_writer import prepare_legacy_list_view + + legacy_root = ( + ROOT / "generated_dataset_views" / dataset_cfg["name"] / "legacy_list" + if args.dry_run + else prepare_legacy_list_view(dataset_cfg) + ) + out_dir = ROOT / "results" / model_name / dataset_cfg["name"] + env["CD_MODELS_DATASET_ROOT"] = str(legacy_root) + env["CD_MODELS_DSIFN_ROOT"] = str(legacy_root) + env["CD_MODELS_DATASET_NAME"] = dataset_cfg["name"] + env["CD_MODELS_CHECKPOINT_DIR"] = str(out_dir / "checkpoints") + env["CD_MODELS_LOG_PATH"] = str(out_dir / "logs" / "train.log") + if args.lr is not None: + env["CD_MODELS_LR"] = str(float(args.lr)) + else: + env["CD_MODELS_LR"] = str(float(_model_config(model_name).get("lr", 1e-3))) + return workdir, [sys.executable, "train_wildfire.py"], env + + +def run_existing_model(model_name: str, args: argparse.Namespace) -> int: + from utils.model_adapters import get_model_adapter + from utils.unified_trainer import train_with_adapter + + adapter = get_model_adapter(model_name) + if adapter.supports_unified_training: + return train_with_adapter(model_name, args) + dataset_cfg = _dataset_ready(args.dataset, return_format="legacy", dry_run=args.dry_run) + apply_dataloader_cli_overrides(dataset_cfg, args) + print_dataloader_policy(dataset_cfg, torch.cuda.is_available()) + workdir_rel, cmd = EXISTING_LEGACY[model_name] + workdir, cmd, env = _existing_train_command(model_name, dataset_cfg, args) + if not cmd: + print( + f"[SKIP] {model_name}/{args.dataset}: no verified non-WildFire training command is wired for " + f"{workdir_rel} yet." + ) + return 0 if args.dry_run else SKIP_EXIT_CODE + _print_train_command(workdir, cmd, env) + if args.dry_run: + return _evaluate(model_name, args.dataset, args.gpu, dry_run=True) + code = 0 + if not args.eval_only: + log_dir = ROOT / "results" / model_name / dataset_cfg["name"] / "logs" + code = _run_logged(cmd, workdir, env, log_dir / "train_stdout.log", log_dir / "train_stderr.log") + if code == 0: + _canonicalize_checkpoints(model_name, dataset_cfg["name"]) + code = _evaluate(model_name, args.dataset, args.gpu, dry_run=False) + return code + + +def run_external_model(model_name: str, args: argparse.Namespace) -> int: + from utils.model_adapters import get_model_adapter + from utils.unified_trainer import train_with_adapter + + adapter = get_model_adapter(model_name) + if adapter.supports_unified_training: + return train_with_adapter(model_name, args) + cfg = _dataset_ready(args.dataset, return_format="tuple", dry_run=args.dry_run) + apply_dataloader_cli_overrides(cfg, args) + print_dataloader_policy(cfg, torch.cuda.is_available()) + repo_rel, repo_url = NEW_REPOS[model_name] + repo = ROOT / repo_rel + if not repo.exists(): + print( + f"[SKIP] {model_name}/{args.dataset}: source repo is not present at {repo}. " + f"Expected source: {repo_url}." + ) + return 0 if args.dry_run else SKIP_EXIT_CODE + if model_name == "tinycd" and int(cfg.get("channels", 3)) > 3: + raise ValueError("TinyCD supports RGB input only. Set channels: 3 in the dataset config override.") + weight_path = None + if not args.dry_run: + weight_path = _ensure_pretrained(model_name) + print(f"[READY] {model_name} repo found at {repo}") + if weight_path: + print(f"[WEIGHTS] {weight_path}") + train_cmd, train_env, train_cwd = _external_train_command(model_name, cfg, args) + if not train_cmd: + print("[NOTE] Model-specific training command construction must be completed from the cloned repo config.") + print(f"[SKIP] {model_name}/{args.dataset}: repo-specific train command wiring is not implemented yet.") + return 0 if args.dry_run else SKIP_EXIT_CODE + if args.dry_run or args.eval_only: + _print_train_command(train_cwd, train_cmd, train_env) + return _evaluate(model_name, args.dataset, args.gpu, dry_run=args.dry_run) + _print_train_command(train_cwd, train_cmd, train_env) + log_dir = ROOT / "results" / model_name / cfg["name"] / "logs" + if model_name == "changemamba": + result_dir = ROOT / "results" / model_name / cfg["name"] + checkpoint_dir = result_dir / "checkpoints" + code = _run_streamed(train_cmd, train_cwd, train_env, log_dir / "train_wrapper.log") + if code != 0: + return FAILED_SUBPROCESS_EXIT_CODE + checkpoints = _checkpoint_files(checkpoint_dir) + if not checkpoints: + _print_checkpoint_failure(model_name, cfg["name"], train_cmd, checkpoint_dir, result_dir) + return FAILED_NO_CHECKPOINT_EXIT_CODE + _canonicalize_checkpoints(model_name, cfg["name"]) + code = _evaluate(model_name, args.dataset, args.gpu, dry_run=False) + return 0 if code == 0 else FAILED_EVAL_EXIT_CODE + code = _run_logged(train_cmd, train_cwd, train_env, log_dir / "train_stdout.log", log_dir / "train_stderr.log") + if code == 0 and model_name not in {"fc_ef", "fc_siam_conc", "fc_siam_diff"}: + _canonicalize_checkpoints(model_name, cfg["name"]) + code = _evaluate(model_name, args.dataset, args.gpu, dry_run=False) + return code + + +def run_smoke_test(model_name: str, args: argparse.Namespace) -> int: + from datasets.cd_dataset import CDDataset + from utils.config_loader import load_dataset_config, load_model_config + from utils.metrics import BinaryMetrics + from utils.model_adapters import get_model_adapter + from utils.profiling import count_flops, count_parameters + from utils.qualitative import safe_sample_id, save_binary_prediction, save_probability_map, select_or_load_manifest + + cfg = load_dataset_config(args.dataset) + apply_dataloader_cli_overrides(cfg, args) + print_dataloader_policy(cfg, torch.cuda.is_available()) + root = Path(cfg["data_root"]) + print(f"[SMOKE] {model_name}/{args.dataset}") + print(f"[SMOKE] Dataset root exists: {root.is_dir()} -> {root}") + if not root.is_dir(): + return 1 + ds = CDDataset(root, "test", cfg=cfg, return_format="tuple") + a, b, mask, name = ds[0] + print(f"[SMOKE] Batch sample: name={name} A={tuple(a.shape)} B={tuple(b.shape)} mask={tuple(mask.shape)}") + adapter = get_model_adapter(model_name) + if adapter.supports_inprocess_eval: + gpu_resolution = resolve_gpu(args.gpu) + print_gpu_diagnostics(gpu_resolution) + device = torch.device(gpu_resolution.local_device) + model = adapter.build_model(load_model_config(model_name), cfg, device) + ckpt = ROOT / "results" / model_name / cfg["name"] / "checkpoints" / "best_model.pth" + if ckpt.exists(): + adapter.load_checkpoint(model, ckpt, device) + print(f"[SMOKE] Loaded checkpoint: {ckpt}") + else: + print(f"[SMOKE] No checkpoint found at {ckpt}; validating construction and forward pass only.") + batch = (a.unsqueeze(0), b.unsqueeze(0), mask.unsqueeze(0), [name]) + train_step = None + if adapter.supports_unified_training: + model.train() + optimizer = adapter.build_optimizer(model, load_model_config(model_name)) + optimizer.zero_grad(set_to_none=True) + raw_train = adapter.forward(model, batch, device) + loss_dict = adapter.compute_loss(raw_train, batch, load_model_config(model_name), cfg, device) + loss_dict["loss"].backward() + optimizer.step() + train_step = {key: float(value.detach().cpu().item()) for key, value in loss_dict.items()} + model.eval() + with torch.inference_mode(): + raw = adapter.forward(model, batch, device) + normalized = adapter.normalize_output(raw, batch, cfg) + metrics = BinaryMetrics(threshold=float(cfg.get("eval", {}).get("threshold", 0.5))) + metrics.update(normalized.metric_tensor, mask.unsqueeze(0)) + params = count_parameters(model) + flops = None + if adapter.supports_flops: + flops = count_flops(model, lambda: adapter.get_dummy_inputs(cfg, device), device) + manifest = select_or_load_manifest(cfg) + smoke_dir = ROOT / "results" / model_name / cfg["name"] / "smoke_test" + clean_id = safe_sample_id(str(name)) + save_binary_prediction(normalized.binary[0], smoke_dir / f"{clean_id}_pred.png") + if normalized.score is not None: + save_probability_map(normalized.score[0], smoke_dir / f"{clean_id}_prob.png") + print( + f"[SMOKE] PASS {model_name}/{cfg['name']} adapter={adapter.model_class_path} " + f"output={tuple(normalized.metric_tensor.shape)} params_m={params['params_m']:.3f} " + f"flops_g={None if flops is None else flops.get('flops_g')} " + f"manifest_samples={manifest.get('selected_count')} train_step={train_step} metrics={metrics.compute()}" + ) + return 0 + args.dry_run = True + if model_name in EXISTING_LEGACY: + workdir, cmd, env = _existing_train_command(model_name, cfg, args) + else: + cmd, env, workdir = _external_train_command(model_name, cfg, args) + if cmd: + print(_gpu_mapping_message(env)) + print(f"[SMOKE] Command construction OK: cwd={workdir} cmd={' '.join(cmd)}") + else: + print("[SMOKE] Command construction reports this adapter is not wired yet.") + print( + f"[SMOKE-UNAVAILABLE] {model_name}/{args.dataset}: {adapter.notes_or_failure_reason}" + ) + return 1 + + +def _external_train_command(model_name: str, dataset_cfg: dict, args: argparse.Namespace) -> tuple[list[str], dict, Path]: + from utils.legacy_config_writer import prepare_legacy_list_view + + env, _ = subprocess_gpu_env(args.gpu) + child_gpu = "0" + model_cfg = _model_config(model_name) + out_dir = ROOT / "results" / model_name / dataset_cfg["name"] + out_dir.mkdir(parents=True, exist_ok=True) + if model_name == "dsamnet": + repo = ROOT / "model_repos" / "DSAMNet" + train_root = _split_dir(dataset_cfg, "train") + val_root = _split_dir(dataset_cfg, "val") + ckpt_dir = out_dir / "checkpoints" + ckpt_dir.mkdir(parents=True, exist_ok=True) + cmd = [ + sys.executable, + "train.py", + "--num_epochs", + str(int(model_cfg.get("num_epochs", 100))), + "--batchsize", + str(int(dataset_cfg.get("batch_size", 8))), + "--val_batchsize", + str(int(dataset_cfg.get("batch_size", 8))), + "--num_workers", + str(int(dataset_cfg.get("num_workers", 4))), + "--gpu_id", + child_gpu, + "--train1_dir", + str(train_root / dataset_cfg.get("image_a_folder", "A")), + "--train2_dir", + str(train_root / dataset_cfg.get("image_b_folder", "B")), + "--label_train", + str(train_root / dataset_cfg.get("mask_folder", "label")), + "--val1_dir", + str(val_root / dataset_cfg.get("image_a_folder", "A")), + "--val2_dir", + str(val_root / dataset_cfg.get("image_b_folder", "B")), + "--label_val", + str(val_root / dataset_cfg.get("mask_folder", "label")), + "--model_dir", + str(ckpt_dir) + "/", + "--sta_dir", + str(out_dir / "statistics.csv"), + ] + return cmd, env, repo + if model_name == "cgnet": + repo = ROOT / "model_repos" / "CGNet-CD" + if args.dry_run: + train_view = ROOT / "generated_dataset_views" / dataset_cfg["name"] / "train" + val_view = ROOT / "generated_dataset_views" / dataset_cfg["name"] / "val" + else: + train_view = _prepare_matched_split_view(dataset_cfg, "train") + val_view = _prepare_matched_split_view(dataset_cfg, "val") + cmd = [ + sys.executable, + "train_CGNet.py", + "--epoch", + str(int(model_cfg.get("num_epochs", 50))), + "--batchsize", + str(int(dataset_cfg.get("batch_size", 8))), + "--trainsize", + str(int(dataset_cfg.get("img_size", 256))), + "--gpu_id", + child_gpu, + "--data_name", + dataset_cfg["name"], + "--model_name", + "CGNet", + "--save_path", + str(out_dir) + "/", + "--train_root", + str(train_view) + "/", + "--val_root", + str(val_view) + "/", + ] + return cmd, env, repo + if model_name == "hanet": + repo = ROOT / "model_repos" / "HANet-CD" + view_root = ROOT / "generated_dataset_views" / dataset_cfg["name"] / "hanet_matched" + if not args.dry_run: + for split in ("train", "val"): + split_view = _prepare_matched_split_view(dataset_cfg, split) + _safe_link_or_copy(split_view, view_root / split) + env["HANET_METADATA_JSON"] = str(_write_hanet_metadata(dataset_cfg, model_cfg, view_root)) + return [sys.executable, "trainHCX.py"], env, repo + if model_name == "tinycd": + repo = ROOT / "model_repos" / "Tiny_model_4_CD" + view = ROOT / "generated_dataset_views" / dataset_cfg["name"] / "tinycd" if args.dry_run else _prepare_tinycd_view(dataset_cfg) + cmd = [ + sys.executable, + "training.py", + "--datapath", + str(view), + "--log-path", + str(out_dir / "logs"), + "--gpu-id", + child_gpu, + "--batch-size", + str(int(args.batch_size or dataset_cfg.get("batch_size", 8))), + "--epochs", + str(int(args.epochs or model_cfg.get("num_epochs", 100))), + ] + return cmd, env, repo + if model_name == "changer": + repo = ROOT / "model_repos" / "open-cd" + view = ( + ROOT / "generated_dataset_views" / dataset_cfg["name"] / "opencd" + if args.dry_run + else _prepare_opencd_view(dataset_cfg) + ) + cfg_path = _write_opencd_changer_config(dataset_cfg, model_cfg, view) + return [sys.executable, "tools/train.py", str(cfg_path), "--work-dir", str(out_dir / "work_dir")], env, repo + if model_name == "changemamba": + repo = ROOT / "model_repos" / "ChangeMamba" + changedetection = repo / "changedetection" + view = ( + ROOT / "generated_dataset_views" / dataset_cfg["name"] / "changemamba" + if args.dry_run + else _prepare_changemamba_view(dataset_cfg) + ) + variant = str(model_cfg.get("vmamba_variant", "small")).lower() + model_type = str(model_cfg.get("changemamba_model_type", f"MambaBCD_{variant.capitalize()}")) + pretrained_name = { + "tiny": "vssmtiny_dp01_ckpt_epoch_292.pth", + "small": "vssmsmall_dp03_ckpt_epoch_238.pth", + "base": "vssmbase_dp06_ckpt_epoch_241.pth", + }.get(variant) + cfg_name = { + "tiny": "vssm_tiny_224_0229flex.yaml", + "small": "vssm_small_224.yaml", + "base": "vssm_base_224.yaml", + }.get(variant) + if pretrained_name is None or cfg_name is None: + raise ValueError(f"Unsupported ChangeMamba VMamba variant: {variant}") + pretrained = repo / "pretrained_weight" / pretrained_name + cfg_path = changedetection / "configs" / "vssm1" / cfg_name + max_iters, max_iters_source = _iteration_budget("changemamba", model_cfg, args, default=50000) + print(f"[TRAIN-CFG] smoke_test={bool(args.smoke_test)}") + print(f"[TRAIN-CFG] max_iters={max_iters}") + print(f"[TRAIN-CFG] max_iters_source={max_iters_source}") + cmd = [ + sys.executable, + "script/train_MambaBCD.py", + "--dataset", + _legacy_dataset_token(dataset_cfg), + "--batch_size", + str(int(dataset_cfg.get("batch_size", 8))), + "--num_workers", + str(int(dataset_cfg.get("num_workers", 4))), + "--crop_size", + str(int(dataset_cfg.get("img_size", model_cfg.get("img_size", 256)))), + "--max_iters", + str(max_iters), + "--model_type", + model_type, + "--model_param_path", + str(out_dir / "checkpoints"), + "--train_dataset_path", + str(view / "train"), + "--train_data_list_path", + str(view / "train_set.txt"), + "--test_dataset_path", + str(view / "test"), + "--test_data_list_path", + str(view / "test_set.txt"), + "--cfg", + str(cfg_path), + "--encoder_pretrained_path", + str(pretrained), + "--learning_rate", + str(float(model_cfg.get("lr", 6e-5))), + "--weight_decay", + str(float(model_cfg.get("weight_decay", 0.01))), + ] + return cmd, env, changedetection + if model_name in {"fc_ef", "fc_siam_conc", "fc_siam_diff"}: + cmd = [ + sys.executable, + str(ROOT / "train" / "fc_adapter.py"), + "--model", + model_name, + "--dataset", + dataset_cfg["name"], + "--gpu", + str(args.gpu), + ] + if args.epochs is not None: + cmd.extend(["--epochs", str(args.epochs)]) + if args.batch_size is not None: + cmd.extend(["--batch-size", str(args.batch_size)]) + if args.lr is not None: + cmd.extend(["--lr", str(args.lr)]) + if args.resume: + cmd.append("--resume") + if args.eval_only: + cmd.append("--eval-only") + if args.force: + cmd.append("--force") + if args.output_dir: + cmd.extend(["--output-dir", args.output_dir]) + return cmd, env, ROOT + if model_name == "elgcnet": + repo = ROOT / "model_repos" / "elgcnet" + view = ( + ROOT / "generated_dataset_views" / dataset_cfg["name"] / "legacy_list" + if args.dry_run + else prepare_legacy_list_view(dataset_cfg) + ) + env["CD_MODELS_DATASET_ROOT"] = str(view) + env["CD_MODELS_DSIFN_ROOT"] = str(view) + cmd = [ + sys.executable, + "main_cd.py", + "--gpu_ids", + child_gpu, + "--project_name", + f"{dataset_cfg['name']}-train-{model_name}", + "--data_name", + _legacy_dataset_token(dataset_cfg), + "--img_size", + str(int(dataset_cfg.get("img_size", model_cfg.get("img_size", 256)))), + "--batch_size", + str(int(dataset_cfg.get("batch_size", 8))), + "--num_workers", + str(int(dataset_cfg.get("num_workers", 4))), + "--max_epochs", + str(int(model_cfg.get("num_epochs", 200))), + "--optimizer", + str(model_cfg.get("optimizer", "adamw")), + "--lr", + str(float(model_cfg.get("lr", 0.00031))), + "--loss", + "ce", + "--net_G", + "ELGCNet", + ] + return cmd, env, repo + return [], env, ROOT + + +def main_for(model_name: str) -> int: + args = parse_args(model_name) + gpu_resolution = resolve_gpu(args.gpu) + print_gpu_diagnostics(gpu_resolution) + selected = args.model if model_name == "fc_variants" else model_name + if selected == "fc_variants": + print("[SKIP] fc_variants requires --model fc_ef, --model fc_siam_conc, or --model fc_siam_diff.") + return 0 if args.dry_run else SKIP_EXIT_CODE + if args.smoke_test: + return run_smoke_test(selected, args) + if selected in EXISTING_LEGACY: + return run_existing_model(selected, args) + return run_external_model(selected, args) diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..63115bbd4232935a981062087fadfb448e8e360e --- /dev/null +++ b/utils/__init__.py @@ -0,0 +1,19 @@ +from .config import deep_merge, load_config, load_dataset_config, load_model_registry +from .config_loader import load_dataset_config as load_dataset_config_v2 +from .config_loader import load_model_config, merge_configs +from .results import append_result, write_json +from .results_writer import append_to_comparison_table, save_metrics + +__all__ = [ + "append_to_comparison_table", + "append_result", + "deep_merge", + "load_config", + "load_dataset_config", + "load_dataset_config_v2", + "load_model_config", + "load_model_registry", + "merge_configs", + "save_metrics", + "write_json", +] diff --git a/utils/config.py b/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..11b30e2dc0df1efe414a1904d6649628b173701a --- /dev/null +++ b/utils/config.py @@ -0,0 +1,42 @@ +from __future__ import annotations + +import json +from copy import deepcopy +from pathlib import Path +from typing import Any + +import yaml + + +ROOT = Path(__file__).resolve().parents[1] + + +def load_config(path: str | Path) -> dict[str, Any]: + path = Path(path) + if not path.is_absolute(): + path = ROOT / path + with path.open("r", encoding="utf-8") as f: + if path.suffix.lower() == ".json": + return json.load(f) + return yaml.safe_load(f) or {} + + +def deep_merge(base: dict[str, Any], override: dict[str, Any]) -> dict[str, Any]: + merged = deepcopy(base) + for key, value in override.items(): + if isinstance(value, dict) and isinstance(merged.get(key), dict): + merged[key] = deep_merge(merged[key], value) + else: + merged[key] = deepcopy(value) + return merged + + +def load_dataset_config(name_or_path: str) -> dict[str, Any]: + path = Path(name_or_path) + if path.suffix: + return load_config(path) + return load_config(Path("configs/datasets") / f"{name_or_path}.yaml") + + +def load_model_registry(path: str | Path = "configs/models/registry.yaml") -> dict[str, Any]: + return load_config(path).get("models", {}) diff --git a/utils/config_loader.py b/utils/config_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..f8a83f86a8fe2b583efaaba0d5ed8386f8807aa5 --- /dev/null +++ b/utils/config_loader.py @@ -0,0 +1,87 @@ +from __future__ import annotations + +import os +from copy import deepcopy +from pathlib import Path +from typing import Any + +import yaml + +from utils.dataset_cache import apply_io_defaults, resolve_effective_dataset_root + + +ROOT = Path(__file__).resolve().parents[1] +DEFAULT_DATA_ROOT = ROOT.parent / "Datasets" +FALLBACK_DATA_ROOTS = (ROOT.parent / "mamba-cd" / "datasets",) + + +def _read_yaml(path: Path) -> dict[str, Any]: + if not path.exists(): + raise FileNotFoundError(f"Config file not found: {path}") + with path.open("r", encoding="utf-8") as f: + return yaml.safe_load(f) or {} + + +def _expand_data_root(value: str) -> str: + unresolved = value + if "${DATA_ROOT}" in value: + data_root = os.environ.get("DATA_ROOT") + if not data_root and DEFAULT_DATA_ROOT.is_dir(): + data_root = str(DEFAULT_DATA_ROOT) + if not data_root: + raise EnvironmentError( + "DATA_ROOT is not set. Set DATA_ROOT to the parent directory containing " + "LEVIR-CD-plus-256, WHU-CD, WildFireS2, and other dataset folders." + ) + value = value.replace("${DATA_ROOT}", data_root) + expanded = Path(value).expanduser().resolve() + if expanded.exists() or "${DATA_ROOT}" not in unresolved: + return str(expanded) + + suffix = unresolved.split("${DATA_ROOT}", 1)[1].lstrip("/\\") + for root in FALLBACK_DATA_ROOTS: + candidate = (root / suffix).expanduser().resolve() + if candidate.exists(): + return str(candidate) + return str(expanded) + + +def list_available_datasets() -> list[str]: + configs_dir = ROOT / "configs" / "datasets" + return sorted(path.stem for path in configs_dir.glob("*.yaml")) + + +def load_dataset_config(dataset_name: str) -> dict[str, Any]: + path = Path(dataset_name) + if not path.suffix: + path = ROOT / "configs" / "datasets" / f"{dataset_name}.yaml" + elif not path.is_absolute(): + path = ROOT / path + cfg = _read_yaml(path) + if "data_root" not in cfg: + raise KeyError(f"{path} must define data_root") + cfg["data_root"] = _expand_data_root(str(cfg["data_root"])) + cfg["_config_path"] = str(path) + cfg["_dataset_name"] = path.stem + effective = resolve_effective_dataset_root(path.stem, cfg) + cfg.update(effective) + apply_io_defaults(cfg) + return cfg + + +def load_model_config(model_name: str) -> dict[str, Any]: + path = Path(model_name) + if not path.suffix: + path = ROOT / "configs" / "models" / f"{model_name}.yaml" + elif not path.is_absolute(): + path = ROOT / path + return _read_yaml(path) + + +def merge_configs(model_cfg: dict[str, Any], dataset_cfg: dict[str, Any]) -> dict[str, Any]: + merged = deepcopy(model_cfg) + merged["dataset"] = deepcopy(dataset_cfg) + for key in ("batch_size", "num_workers", "img_size", "channels", "num_classes", "ignore_index"): + if key in dataset_cfg: + merged[key] = deepcopy(dataset_cfg[key]) + return merged diff --git a/utils/dataset_list_generator.py b/utils/dataset_list_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..a390c5d85f27fd176071dc1cfe52b965c2151f9e --- /dev/null +++ b/utils/dataset_list_generator.py @@ -0,0 +1,93 @@ +"""Generate train/val/test list files for list-based change-detection loaders.""" + +from __future__ import annotations + +import argparse +import os +import sys +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +IMG_EXTS = {".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp"} + + +def generate_list_files( + data_root: str, + splits: list[str] | None = None, + img_subdir: str = "A", + overwrite: bool = False, +) -> dict[str, int]: + if splits is None: + splits = ["train", "val", "test"] + + root = Path(data_root) + list_dir = root / "list" + list_dir.mkdir(parents=True, exist_ok=True) + + results: dict[str, int] = {} + for split in splits: + list_file = list_dir / f"{split}.txt" + if list_file.exists() and not overwrite: + count = sum(1 for _ in list_file.open("r", encoding="utf-8")) + print(f" [SKIP] {list_file} already exists ({count} entries)") + results[split] = count + continue + + img_dir = None + for candidate in [img_subdir, "A", "T1", "t1", "img", "images", "image"]: + candidate_path = root / split / candidate + if candidate_path.is_dir(): + img_dir = candidate_path + break + + if img_dir is None: + print(f" [WARN] No image directory found under {root / split} - skipping {split}") + results[split] = 0 + continue + + filenames = sorted( + p.name for p in img_dir.iterdir() if p.is_file() and p.suffix.lower() in IMG_EXTS + ) + if not filenames: + print(f" [WARN] No image files found in {img_dir}") + results[split] = 0 + continue + + list_file.write_text("\n".join(filenames) + "\n", encoding="utf-8") + print(f" [OK] {list_file}: {len(filenames)} entries") + results[split] = len(filenames) + + return results + + +def generate_for_dataset(dataset_name: str, overwrite: bool = False) -> None: + from utils.config_loader import load_dataset_config + + cfg = load_dataset_config(dataset_name) + data_root = cfg["data_root"] + if not os.path.isdir(data_root): + print(f"[SKIP] Dataset root does not exist: {data_root}") + return + img_subdir = cfg.get("image_a_folder", "A") + splits = list((cfg.get("splits") or {"train": "train", "val": "val", "test": "test"}).keys()) + print(f"[LIST_GEN] Generating list files for {dataset_name} at {data_root}") + results = generate_list_files(data_root, splits=splits, img_subdir=img_subdir, overwrite=overwrite) + print(f"[LIST_GEN] Done: {results}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--dataset", type=str, default=None) + parser.add_argument("--all", action="store_true") + parser.add_argument("--overwrite", action="store_true") + args = parser.parse_args() + + from utils.config_loader import list_available_datasets + + datasets = list_available_datasets() if args.all else ([args.dataset] if args.dataset else []) + for ds in datasets: + generate_for_dataset(ds, overwrite=args.overwrite) diff --git a/utils/env_checker.py b/utils/env_checker.py new file mode 100644 index 0000000000000000000000000000000000000000..2223ec0fa36a2fda9b89ee860501b9b9d44024a0 --- /dev/null +++ b/utils/env_checker.py @@ -0,0 +1,46 @@ +"""Check importable packages for the CD-Models training environment.""" + +from __future__ import annotations + + +REQUIRED_PACKAGES = { + ("torch", "torch", "all models"), + ("torchvision", "torchvision", "all models"), + ("numpy", "numpy", "all models"), + ("PIL", "Pillow", "all models"), + ("cv2", "opencv-python", "all models"), + ("yaml", "pyyaml", "all models"), + ("tqdm", "tqdm", "weight_downloader"), + ("timm", "timm", "TinyCD, ELGCNet, ChangeFormer"), + ("einops", "einops", "BIT_CD, ChangeFormer"), + ("mmengine", "mmengine==0.10.1", "ChangeMamba, Changer"), + ("mmcv", "mmcv==2.1.0", "ChangeMamba, Changer"), + ("mmseg", "mmsegmentation==1.2.2", "ChangeMamba, Changer"), + ("gdown", "gdown", "weight_downloader GDrive fallback"), +} + + +def check_env(verbose: bool = True) -> bool: + missing = [] + for import_name, pip_name, required_by in sorted(REQUIRED_PACKAGES): + try: + __import__(import_name) + if verbose: + print(f" [OK] {import_name}") + except ImportError: + print(f" [MISSING] {import_name} (install: pip install {pip_name}) - needed by: {required_by}") + missing.append((import_name, pip_name)) + + if missing: + print(f"\n{len(missing)} packages missing. Install with:") + for _, pip_name in missing: + print(f" pip install {pip_name}") + else: + print("\nAll packages present.") + return not missing + + +if __name__ == "__main__": + import sys + + sys.exit(0 if check_env(verbose=True) else 1) diff --git a/utils/legacy_config_writer.py b/utils/legacy_config_writer.py new file mode 100644 index 0000000000000000000000000000000000000000..ec73e8f23349a39d170eb7ae34fd7bea9e06cfef --- /dev/null +++ b/utils/legacy_config_writer.py @@ -0,0 +1,204 @@ +from __future__ import annotations + +import json +import os +import shutil +from pathlib import Path + +import numpy as np +from PIL import Image +from utils.dataset_cache import dataset_runtime_summary + + +ROOT = Path(__file__).resolve().parents[1] +IMG_EXTS = {".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp"} + + +def _safe_link_or_copy(src: Path, dst: Path) -> None: + if dst.is_symlink(): + try: + if Path(os.readlink(dst)) == src: + return + except OSError: + pass + dst.unlink() + if dst.exists(): + return + dst.parent.mkdir(parents=True, exist_ok=True) + try: + os.symlink(src, dst, target_is_directory=src.is_dir()) + except OSError: + if src.is_dir(): + shutil.copytree(src, dst, dirs_exist_ok=True) + else: + shutil.copy2(src, dst) + + +def _safe_mask_link_or_copy(src: Path, dst: Path) -> None: + dst.parent.mkdir(parents=True, exist_ok=True) + if not src.exists(): + return + + try: + arr = np.asarray(Image.open(src)) + if arr.ndim == 3: + arr = arr[..., 0] + if arr.size and int(arr.max()) <= 1: + if dst.exists() or dst.is_symlink(): + dst.unlink() + Image.fromarray((arr > 0).astype(np.uint8) * 255).save(dst, format="PNG") + return + except Exception as exc: + print(f"[DATASET-VIEW] Could not inspect mask {src}: {exc}") + + _safe_link_or_copy(src, dst) + + +def _split_dir(dataset_cfg: dict, split: str) -> Path: + return Path(dataset_cfg["data_root"]) / dataset_cfg.get("splits", {}).get(split, split) + + +def _scan(folder: Path) -> dict[str, Path]: + if not folder.is_dir(): + return {} + return { + p.stem: p + for p in sorted(folder.iterdir()) + if p.is_file() and not p.name.startswith(".") and p.suffix.lower() in IMG_EXTS + } + + +def prepare_legacy_list_view(dataset_cfg: dict) -> Path: + view = ROOT / "generated_dataset_views" / dataset_cfg["name"] / "legacy_list" + list_dir = view / "list" + for folder in ("A", "B", "label", "list"): + (view / folder).mkdir(parents=True, exist_ok=True) + + for split in ("train", "val", "test"): + split_root = _split_dir(dataset_cfg, split) + a_files = _scan(split_root / dataset_cfg.get("image_a_folder", "A")) + b_files = _scan(split_root / dataset_cfg.get("image_b_folder", "B")) + m_files = _scan(split_root / dataset_cfg.get("mask_folder", "label")) + names = [] + for stem in sorted(set(a_files) & set(b_files) & set(m_files)): + target_name = f"{split}__{stem}{a_files[stem].suffix}" + _safe_link_or_copy(a_files[stem], view / "A" / target_name) + _safe_link_or_copy(b_files[stem], view / "B" / target_name) + _safe_mask_link_or_copy(m_files[stem], view / "label" / target_name) + label_name = f"{split}__{stem}{m_files[stem].suffix}" + if label_name != target_name: + _safe_mask_link_or_copy(m_files[stem], view / "label" / label_name) + names.append(target_name) + (list_dir / f"{split}.txt").write_text("\n".join(names) + ("\n" if names else ""), encoding="utf-8") + + counts = {} + for split in ("train", "val", "test"): + list_path = list_dir / f"{split}.txt" + counts[split] = len(list_path.read_text(encoding="utf-8").splitlines()) if list_path.exists() else 0 + print(f"[DATASET] {dataset_runtime_summary(dataset_cfg)}") + print(f"[DATASET] generated view path: {view}") + print(f"[DATASET] train/val/test counts: {counts}") + return view + + +def _base_config(model_name: str, dataset_cfg: dict, model_cfg: dict, prepare_view: bool = True) -> dict: + root_path = prepare_legacy_list_view(dataset_cfg) if prepare_view else ROOT / "generated_dataset_views" / dataset_cfg["name"] / "legacy_list" + root = str(root_path) + img_size = int(dataset_cfg.get("img_size", model_cfg.get("img_size", 256))) + batch_size = int(dataset_cfg.get("batch_size", 8)) + num_workers = int(dataset_cfg.get("num_workers", 4)) + epochs = int(model_cfg.get("num_epochs", 200)) + lr = float(model_cfg.get("lr", 1e-4)) + optimizer = str(model_cfg.get("optimizer", "adam")) + loss = str(model_cfg.get("loss", "ce_dice")) + dataset_name = dataset_cfg.get("source_name", dataset_cfg.get("name", "CD")) + return { + "name": f"{dataset_cfg['name']}-train-{model_name}", + "phase": "train", + "gpu_ids": [0], + "path_cd": { + "log": "logs", + "result": "results", + "checkpoint": "checkpoint", + "resume_state": None, + }, + "datasets": { + "train": { + "name": dataset_name, + "datasetroot": root, + "resolution": img_size, + "num_workers": num_workers, + "batch_size": batch_size, + "use_shuffle": True, + "data_len": -1, + }, + "val": { + "name": dataset_name, + "datasetroot": root, + "resolution": img_size, + "num_workers": num_workers, + "batch_size": batch_size, + "use_shuffle": False, + "data_len": -1, + }, + "test": { + "name": dataset_name, + "datasetroot": root, + "resolution": img_size, + "num_workers": num_workers, + "batch_size": batch_size, + "use_shuffle": False, + "data_len": -1, + }, + }, + "model": {"name": model_name, "loss": loss}, + "train": { + "n_epoch": epochs, + "train_print_iter": 50, + "val_freq": 1, + "val_print_iter": 20, + "optimizer": {"type": optimizer, "lr": lr}, + "sheduler": {"lr_policy": model_cfg.get("scheduler", "linear"), "n_step": 3, "gamma": 0.1}, + }, + } + + +def _cdmamba_model_block() -> dict: + return { + "name": "cdmamba", + "loss": "ce_dice", + "init_filters": 16, + "n_classes": 2, + "mode": "AGLGF", + "conv_mode": "orignal_dinner", + "local_query_model": "orignal_dinner", + "up_mode": "SRCM", + "up_conv_mode": "deepwise", + "spatial_dims": 2, + "in_channels": 3, + "resdiual": False, + "blocks_down": [1, 2, 2, 4], + "blocks_up": [1, 1, 1], + "diff_abs": "later", + "stage": 2, + "mamba_act": "relu", + "norm": ["GROUP", {"num_groups": 8}], + } + + +def write_bifa_or_cdmamba_config( + model_name: str, + dataset_cfg: dict, + model_cfg: dict, + prepare_view: bool = True, +) -> Path: + if model_name not in {"bifa", "cdmamba"}: + raise ValueError(f"Unsupported generated legacy JSON model: {model_name}") + cfg = _base_config(model_name, dataset_cfg, model_cfg, prepare_view=prepare_view) + if model_name == "cdmamba": + cfg["model"] = _cdmamba_model_block() + out = ROOT / "generated_configs" / f"{dataset_cfg['name']}__{model_name}.json" + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w", encoding="utf-8") as f: + json.dump(cfg, f, indent=2) + return out diff --git a/utils/metrics.py b/utils/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..3a34ef17ca8ab067cf594b35cbb5f74ce3312c01 --- /dev/null +++ b/utils/metrics.py @@ -0,0 +1,156 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Dict, Tuple + +import numpy as np +import torch +import torch.nn.functional as F + + +def normalize_binary_prediction(preds: torch.Tensor, threshold: float = 0.5) -> Tuple[torch.Tensor, torch.Tensor | None]: + """Return binary predictions and optional changed-class score maps. + + Two-channel tensors are treated as class logits/log-probabilities and are + converted with argmax. One-channel floating tensors outside [0, 1] are + treated as logits and passed through sigmoid. Integer tensors are treated as + labels. + """ + if preds.ndim == 4 and preds.shape[1] == 2: + pred = torch.argmax(preds, dim=1, keepdim=True).float() + scores = torch.softmax(preds.float(), dim=1)[:, 1:2] + return pred, scores + if preds.ndim == 3: + preds = preds.unsqueeze(1) + if preds.dtype.is_floating_point: + if float(preds.min()) < 0.0 or float(preds.max()) > 1.0: + preds = torch.sigmoid(preds) + return (preds >= threshold).float(), preds.float() + return (preds > 0).float(), None + + +def _as_binary(preds: torch.Tensor, threshold: float) -> torch.Tensor: + pred, _ = normalize_binary_prediction(preds, threshold=threshold) + return pred + + +def _target(mask: torch.Tensor) -> torch.Tensor: + if mask.ndim == 3: + mask = mask.unsqueeze(1) + return (mask > 0).float() + + +@dataclass +class BinaryMetrics: + threshold: float = 0.5 + tp: int = 0 + fp: int = 0 + fn: int = 0 + tn: int = 0 + + def update(self, preds: torch.Tensor, targets: torch.Tensor) -> None: + pred = _as_binary(preds.detach().cpu(), self.threshold).bool() + tgt = _target(targets.detach().cpu()).bool() + self.tp += int((pred & tgt).sum().item()) + self.fp += int((pred & ~tgt).sum().item()) + self.fn += int((~pred & tgt).sum().item()) + self.tn += int((~pred & ~tgt).sum().item()) + + def compute(self) -> Dict[str, float]: + eps = 1e-8 + precision = self.tp / (self.tp + self.fp + eps) + recall = self.tp / (self.tp + self.fn + eps) + f1 = 2 * precision * recall / (precision + recall + eps) + iou = self.tp / (self.tp + self.fp + self.fn + eps) + oa = (self.tp + self.tn) / (self.tp + self.fp + self.fn + self.tn + eps) + total = self.tp + self.fp + self.fn + self.tn + pe = ( + ((self.tp + self.fp) * (self.tp + self.fn)) + + ((self.fn + self.tn) * (self.fp + self.tn)) + ) / ((total * total) + eps) + kappa = (oa - pe) / (1.0 - pe + eps) + precision_0 = self.tn / (self.tn + self.fn + eps) + recall_0 = self.tn / (self.tn + self.fp + eps) + f1_0 = 2 * precision_0 * recall_0 / (precision_0 + recall_0 + eps) + iou_0 = self.tn / (self.tn + self.fp + self.fn + eps) + miou = (iou + iou_0) / 2.0 + return { + "threshold": self.threshold, + "f1": f1, + "iou": iou, + "miou": miou, + "precision": precision, + "recall": recall, + "oa": oa, + "kappa": kappa, + "f1_0": f1_0, + "iou_0": iou_0, + "tp": float(self.tp), + "fp": float(self.fp), + "fn": float(self.fn), + "tn": float(self.tn), + } + + +def _boundary_map(mask: torch.Tensor, radius: int) -> torch.Tensor: + mask = _target(mask).float() + if radius < 1: + radius = 1 + k = 2 * radius + 1 + eroded = -F.max_pool2d(-mask, kernel_size=k, stride=1, padding=radius) + return (mask - eroded).clamp(min=0.0, max=1.0).bool() + + +@dataclass +class BoundaryMetrics: + tolerance: int = 2 + boundary_tp_pred: int = 0 + boundary_total_pred: int = 0 + boundary_tp_gt: int = 0 + boundary_total_gt: int = 0 + + def update(self, preds: torch.Tensor, targets: torch.Tensor) -> None: + pred = _as_binary(preds.detach().cpu(), 0.5) + tgt = _target(targets.detach().cpu()) + pred_b = _boundary_map(pred, radius=1) + tgt_b = _boundary_map(tgt, radius=1) + tol = max(int(self.tolerance), 1) + k = 2 * tol + 1 + pred_match = F.max_pool2d(pred_b.float(), kernel_size=k, stride=1, padding=tol).bool() + tgt_match = F.max_pool2d(tgt_b.float(), kernel_size=k, stride=1, padding=tol).bool() + self.boundary_tp_pred += int((pred_b & tgt_match).sum().item()) + self.boundary_total_pred += int(pred_b.sum().item()) + self.boundary_tp_gt += int((tgt_b & pred_match).sum().item()) + self.boundary_total_gt += int(tgt_b.sum().item()) + + def compute(self) -> Dict[str, float]: + eps = 1e-8 + precision = self.boundary_tp_pred / (self.boundary_total_pred + eps) + recall = self.boundary_tp_gt / (self.boundary_total_gt + eps) + bf1 = 2 * precision * recall / (precision + recall + eps) + return { + "bf1": bf1, + "boundary_precision": precision, + "boundary_recall": recall, + "boundary_tolerance": int(self.tolerance), + } + + +def compute_binary_metrics(preds: torch.Tensor, targets: torch.Tensor, threshold: float = 0.5) -> Dict[str, float]: + metrics = BinaryMetrics(threshold=threshold) + metrics.update(preds, targets) + return metrics.compute() + + +def compute_binary_cd_metrics(pred, target, threshold: float = 0.5, logits: bool = False) -> Dict[str, float]: + if isinstance(pred, np.ndarray): + pred_t = torch.from_numpy(pred) + else: + pred_t = pred.detach().cpu() if hasattr(pred, "detach") else torch.as_tensor(pred) + if isinstance(target, np.ndarray): + target_t = torch.from_numpy(target) + else: + target_t = target.detach().cpu() if hasattr(target, "detach") else torch.as_tensor(target) + if logits and pred_t.dtype.is_floating_point: + pred_t = torch.sigmoid(pred_t) + return compute_binary_metrics(pred_t, target_t, threshold=threshold) diff --git a/utils/model_adapters.py b/utils/model_adapters.py new file mode 100644 index 0000000000000000000000000000000000000000..33f9231ca1be7ffff4aa5efae9c781b23ef0a719 --- /dev/null +++ b/utils/model_adapters.py @@ -0,0 +1,936 @@ +from __future__ import annotations + +import importlib +import sys +from abc import ABC, abstractmethod +from contextlib import contextmanager +from dataclasses import dataclass +from pathlib import Path +from types import SimpleNamespace +from typing import Any + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from utils.metrics import normalize_binary_prediction + + +ROOT = Path(__file__).resolve().parents[1] + + +@dataclass +class NormalizedOutput: + binary: torch.Tensor + score: torch.Tensor | None + metric_tensor: torch.Tensor + + +class AdapterUnavailable(RuntimeError): + pass + + +@contextmanager +def repo_import_context(repo: Path): + repo_str = str(repo) + old_path = list(sys.path) + upstream_prefixes = ("models", "model", "network", "cd", "DSIFN", "changedetection") + for name in list(sys.modules): + if any(name == prefix or name.startswith(f"{prefix}.") for prefix in upstream_prefixes): + del sys.modules[name] + sys.path.insert(0, repo_str) + try: + yield + finally: + sys.path = old_path + + +class BaseModelAdapter(ABC): + model_name = "base" + supports_inprocess_eval = False + supports_unified_eval = False + supports_unified_training = False + supports_flops = False + supports_amp_training = True + notes_or_failure_reason = "Adapter is not implemented." + model_class_path = "" + input_format = "" + output_format = "" + checkpoint_format = "" + final_output_for_metrics = "" + + def __init__(self, model_name: str | None = None) -> None: + if model_name: + self.model_name = model_name + + def _missing(self, method: str) -> NotImplementedError: + return NotImplementedError(f"{self.model_name} adapter does not implement {method}: {self.notes_or_failure_reason}") + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + raise self._missing("build_model") + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + raise self._missing("load_checkpoint") + + def forward(self, model: nn.Module, batch: tuple, device: torch.device) -> Any: + raise self._missing("forward") + + def normalize_output(self, raw_output: Any, batch: tuple, dataset_config: dict) -> NormalizedOutput: + raise self._missing("normalize_output") + + def get_dummy_inputs(self, dataset_config: dict, device: torch.device) -> tuple: + raise self._missing("get_dummy_inputs") + + def compute_loss(self, raw_output: Any, batch: tuple, model_config: dict, dataset_config: dict, device: torch.device) -> dict[str, torch.Tensor]: + output = raw_output[-1] if isinstance(raw_output, (list, tuple)) else raw_output + output = output.float() + mask = batch[2].to(device, non_blocking=True).float() + if output.ndim == 4 and output.shape[1] == 2: + target = mask.squeeze(1).long() + loss = F.cross_entropy(output, target) + elif output.ndim == 4 and output.shape[1] == 1: + target = mask + if output.shape[-2:] != target.shape[-2:]: + output = F.interpolate(output, size=target.shape[-2:], mode="bilinear", align_corners=True) + if output.detach().min() >= 0 and output.detach().max() <= 1: + loss = F.binary_cross_entropy(output.clamp(1e-6, 1 - 1e-6), target) + else: + loss = F.binary_cross_entropy_with_logits(output, target) + else: + raise RuntimeError(f"{self.model_name} default loss cannot handle output shape {tuple(output.shape)}.") + return {"loss": loss, "main_loss": loss.detach()} + + def save_checkpoint( + self, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: Any, + path: Path, + metadata: dict, + ) -> None: + payload = dict(metadata) + payload["model_state_dict"] = model.state_dict() + payload["optimizer_state_dict"] = optimizer.state_dict() + if scheduler is not None: + payload["scheduler_state_dict"] = scheduler.state_dict() + torch.save(payload, path) + + def build_optimizer(self, model: nn.Module, model_config: dict) -> torch.optim.Optimizer: + lr = float(model_config.get("lr", 1e-4)) + weight_decay = float(model_config.get("weight_decay", 0.0) or 0.0) + optimizer_name = str(model_config.get("optimizer", "adam")).lower() + if optimizer_name == "sgd": + return torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=weight_decay) + if optimizer_name == "adamw": + return torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay) + return torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay) + + def build_scheduler(self, optimizer: torch.optim.Optimizer, model_config: dict) -> Any: + scheduler_name = str(model_config.get("scheduler", "")).lower() + epochs = int(model_config.get("num_epochs", 200)) + if scheduler_name == "cosine": + return torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs) + if scheduler_name == "step": + return torch.optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.5) + return None + + def get_primary_metric(self) -> str: + return "f1" + + def get_threshold(self, dataset_config: dict) -> float: + return float(dataset_config.get("eval", {}).get("threshold", 0.5)) + + def get_threshold_mode(self) -> str: + return "argmax" if "two-channel" in self.output_format else "threshold" + + +class UnsupportedAdapter(BaseModelAdapter): + def __init__(self, model_name: str, reason: str) -> None: + super().__init__(model_name) + self.notes_or_failure_reason = reason + + +class TwoTensorLogitAdapter(BaseModelAdapter): + supports_inprocess_eval = True + supports_unified_eval = True + supports_unified_training = True + supports_flops = True + input_format = "two tensors (A, B), each [B, 3, H, W]" + output_format = "two-channel logits [B, 2, H, W]" + final_output_for_metrics = "raw two-channel logits" + + def forward(self, model: nn.Module, batch: tuple, device: torch.device) -> torch.Tensor: + a, b, _mask, _names = batch + return model(a.to(device, non_blocking=True), b.to(device, non_blocking=True)) + + def normalize_output(self, raw_output: Any, batch: tuple, dataset_config: dict) -> NormalizedOutput: + if isinstance(raw_output, (list, tuple)): + raw_output = raw_output[-1] + threshold = float(dataset_config.get("eval", {}).get("threshold", 0.5)) + binary, score = normalize_binary_prediction(raw_output.detach().cpu(), threshold=threshold) + return NormalizedOutput(binary=binary, score=score, metric_tensor=raw_output.detach().cpu()) + + def get_dummy_inputs(self, dataset_config: dict, device: torch.device) -> tuple: + size = int(dataset_config.get("img_size", dataset_config.get("image_size", 256))) + return ( + torch.zeros(1, 3, size, size, device=device), + torch.zeros(1, 3, size, size, device=device), + ) + + +class FCAdapter(TwoTensorLogitAdapter): + notes_or_failure_reason = "Direct FC model from fully_convolutional_change_detection." + checkpoint_format = "CD-Models checkpoint dict with model_state_dict, or raw state dict" + + def __init__(self, model_name: str) -> None: + super().__init__(model_name) + self.model_class_path = { + "fc_ef": "model_repos/fully_convolutional_change_detection/unet.py:Unet", + "fc_siam_conc": "model_repos/fully_convolutional_change_detection/siamunet_conc.py:SiamUnet_conc", + "fc_siam_diff": "model_repos/fully_convolutional_change_detection/siamunet_diff.py:SiamUnet_diff", + }[model_name] + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "model_repos" / "fully_convolutional_change_detection" + with repo_import_context(repo): + if self.model_name == "fc_ef": + module = importlib.import_module("unet") + model = module.Unet(input_nbr=6, label_nbr=2) + elif self.model_name == "fc_siam_conc": + module = importlib.import_module("siamunet_conc") + model = module.SiamUnet_conc(input_nbr=3, label_nbr=2) + else: + module = importlib.import_module("siamunet_diff") + model = module.SiamUnet_diff(input_nbr=3, label_nbr=2) + return model.to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state = checkpoint["model_state_dict"] + elif isinstance(checkpoint, dict) and all(torch.is_tensor(v) for v in checkpoint.values()): + state = checkpoint + else: + raise RuntimeError(f"{self.model_name} checkpoint {checkpoint_path} has no recognized state dict.") + model.load_state_dict(state, strict=True) + + def compute_loss(self, raw_output: Any, batch: tuple, model_config: dict, dataset_config: dict, device: torch.device) -> dict[str, torch.Tensor]: + target = batch[2].squeeze(1).long().to(device, non_blocking=True) + weight = None + if self.model_name == "fc_siam_diff": + pos = target.eq(1).sum().float() + neg = target.eq(0).sum().float() + if bool(pos.item() > 0): + pos_weight = (neg / pos.clamp_min(1.0)).clamp(min=1.0, max=50.0) + weight = torch.stack([torch.ones_like(pos_weight), pos_weight]).to(device) + loss = F.nll_loss(raw_output.float(), target, weight=weight) + result = {"loss": loss, "nll_loss": loss.detach()} + if weight is not None: + result["positive_class_weight"] = weight[1].detach() + return result + + +class BITAdapter(TwoTensorLogitAdapter): + model_name = "bit_cd" + notes_or_failure_reason = "BIT_CD models.networks.define_G with base_transformer_pos_s4_dd8." + model_class_path = "BIT_CD/models/networks.py:define_G" + checkpoint_format = "raw state dict, CD-Models model_state_dict, or upstream model_G_state_dict" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "BIT_CD" + with repo_import_context(repo): + networks = importlib.import_module("models.networks") + args = SimpleNamespace(net_G="base_transformer_pos_s4_dd8", lr_policy="linear", max_epochs=1) + model = networks.define_G(args, gpu_ids=[]) + return model.to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, dict) and "model_G_state_dict" in checkpoint: + state = checkpoint["model_G_state_dict"] + elif isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state = checkpoint["model_state_dict"] + elif isinstance(checkpoint, dict) and all(torch.is_tensor(v) for v in checkpoint.values()): + state = checkpoint + else: + raise RuntimeError(f"BIT_CD checkpoint {checkpoint_path} has no recognized state dict.") + model.load_state_dict(state, strict=True) + + +class ChangeFormerAdapter(BITAdapter): + model_name = "changeformer" + supports_amp_training = False + notes_or_failure_reason = "ChangeFormer models.networks.define_G with ChangeFormerV6 embed_dim=64." + model_class_path = "ChangeFormer/models/networks.py:define_G" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "ChangeFormer" + with repo_import_context(repo): + networks = importlib.import_module("models.networks") + args = SimpleNamespace(net_G="ChangeFormerV6", embed_dim=64, lr_policy="linear", max_epochs=1) + model = networks.define_G(args, gpu_ids=[]) + return model.to(device) + + +class BiFAAdapter(TwoTensorLogitAdapter): + model_name = "bifa" + supports_amp_training = False + notes_or_failure_reason = "BiFA models.bifa.BiFA with mit_b0 backbone; returns two-channel logits." + model_class_path = "BiFA/models/bifa.py:BiFA" + output_format = "two-channel logits [B, 2, H, W]" + checkpoint_format = "raw state dict or CD-Models model_state_dict" + final_output_for_metrics = "raw two-channel logits" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "BiFA" + with repo_import_context(repo): + module = importlib.import_module("models.bifa") + model = module.BiFA(backbone="mit_b0") + return model.to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state = checkpoint["model_state_dict"] + elif isinstance(checkpoint, dict) and all(torch.is_tensor(v) for v in checkpoint.values()): + state = checkpoint + else: + raise RuntimeError(f"BiFA checkpoint {checkpoint_path} has no recognized state dict.") + model.load_state_dict(state, strict=True) + + +class SiamNestedUNetAdapter(TwoTensorLogitAdapter): + model_name = "siam_nestedunet" + supports_amp_training = False + notes_or_failure_reason = "Siam-NestedUNet SNUNet_ECAM returns a tuple; final output is tuple[-1]." + model_class_path = "Siam-NestedUNet/models/Models.py:SNUNet_ECAM" + output_format = "tuple containing two-channel logits [B, 2, H, W]" + checkpoint_format = "raw state dict or CD-Models model_state_dict" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "Siam-NestedUNet" + with repo_import_context(repo): + module = importlib.import_module("models.Models") + model = module.SNUNet_ECAM(3, 2) + return model.to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state = checkpoint["model_state_dict"] + elif isinstance(checkpoint, dict) and all(torch.is_tensor(v) for v in checkpoint.values()): + state = checkpoint + else: + raise RuntimeError(f"Siam-NestedUNet checkpoint {checkpoint_path} has no recognized state dict.") + model.load_state_dict(state, strict=True) + + +class STANetModule(nn.Module): + def __init__(self, net_f: nn.Module, net_a: nn.Module) -> None: + super().__init__() + self.netF = net_f + self.netA = net_a + + def forward(self, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + feat_a = self.netF(a) + feat_b = self.netF(b) + feat_a, feat_b = self.netA(feat_a, feat_b) + dist = torch.norm(feat_a - feat_b, p=2, dim=1, keepdim=True) + return F.interpolate(dist, size=a.shape[-2:], mode="bilinear", align_corners=True) + + +class STANetAdapter(BaseModelAdapter): + model_name = "stanet" + supports_inprocess_eval = True + supports_unified_eval = True + supports_unified_training = True + supports_flops = True + notes_or_failure_reason = "STANet netF + CDSA(PAM), distance map thresholded at 1.0 as in train_wildfire.py." + model_class_path = "STANet/models/backbone.py:define_F + CDSA" + input_format = "two tensors (A, B), each [B, 3, H, W]" + output_format = "one-channel L2 distance map [B, 1, H, W]" + checkpoint_format = "dict with netF and netA state dicts" + final_output_for_metrics = "binary distance > 1.0" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "STANet" + with repo_import_context(repo): + backbone = importlib.import_module("models.backbone") + net_f = backbone.define_F(in_c=3, f_c=64, type="mynet3") + net_a = backbone.CDSA(in_c=64, ds=1, mode="PAM") + return STANetModule(net_f, net_a).to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + model.load_state_dict(checkpoint["model_state_dict"], strict=True) + return + if not isinstance(checkpoint, dict) or "netF" not in checkpoint or "netA" not in checkpoint: + raise RuntimeError(f"STANet checkpoint {checkpoint_path} must contain netF and netA keys.") + model.netF.load_state_dict(checkpoint["netF"], strict=True) + model.netA.load_state_dict(checkpoint["netA"], strict=True) + + def forward(self, model: nn.Module, batch: tuple, device: torch.device) -> torch.Tensor: + a, b, _mask, _names = batch + return model(a.to(device, non_blocking=True), b.to(device, non_blocking=True)) + + def normalize_output(self, raw_output: Any, batch: tuple, dataset_config: dict) -> NormalizedOutput: + dist = raw_output.detach().cpu() + binary = (dist > 1.0).float() + return NormalizedOutput(binary=binary, score=None, metric_tensor=binary) + + def get_dummy_inputs(self, dataset_config: dict, device: torch.device) -> tuple: + size = int(dataset_config.get("img_size", dataset_config.get("image_size", 256))) + return ( + torch.zeros(1, 3, size, size, device=device), + torch.zeros(1, 3, size, size, device=device), + ) + + def compute_loss(self, raw_output: Any, batch: tuple, model_config: dict, dataset_config: dict, device: torch.device) -> dict[str, torch.Tensor]: + repo = ROOT / "STANet" + with repo_import_context(repo): + module = importlib.import_module("models.loss") + criterion = module.BCL().to(device) + target = batch[2].to(device, non_blocking=True).float() + label = target.clone() + label[target == 1] = -1 + label[target == 0] = 1 + loss = criterion(raw_output, label) + return {"loss": loss, "bcl_loss": loss.detach()} + + +class TinyCDAdapter(TwoTensorLogitAdapter): + model_name = "tinycd" + supports_inprocess_eval = True + supports_unified_eval = True + supports_unified_training = True + supports_flops = True + supports_amp_training = False + notes_or_failure_reason = "TinyCD ChangeClassifier, sigmoid one-channel probability output." + model_class_path = "model_repos/Tiny_model_4_CD/models/change_classifier.py:ChangeClassifier" + output_format = "one-channel sigmoid probability map [B, 1, H, W]" + checkpoint_format = "raw state dict or CD-Models model_state_dict" + final_output_for_metrics = "sigmoid probability thresholded with dataset eval threshold" + + def get_threshold_mode(self) -> str: + return "threshold" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "model_repos" / "Tiny_model_4_CD" + with repo_import_context(repo): + module = importlib.import_module("models.change_classifier") + model = module.ChangeClassifier() + return model.to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state = checkpoint["model_state_dict"] + elif isinstance(checkpoint, dict) and all(torch.is_tensor(v) for v in checkpoint.values()): + state = checkpoint + else: + raise RuntimeError(f"TinyCD checkpoint {checkpoint_path} has no recognized state dict.") + model.load_state_dict(state, strict=True) + + def compute_loss(self, raw_output: Any, batch: tuple, model_config: dict, dataset_config: dict, device: torch.device) -> dict[str, torch.Tensor]: + target = batch[2].to(device, non_blocking=True).float() + output = raw_output.float() + if output.shape[-2:] != target.shape[-2:]: + output = F.interpolate(output, size=target.shape[-2:], mode="bilinear", align_corners=True) + loss = F.binary_cross_entropy(output.clamp(1e-6, 1 - 1e-6), target) + return {"loss": loss, "bce_loss": loss.detach()} + + +class DSAMNetAdapter(BaseModelAdapter): + model_name = "dsamnet" + supports_inprocess_eval = True + supports_unified_eval = True + supports_unified_training = True + supports_flops = True + notes_or_failure_reason = "DSAMNet returns distance map plus deep supervision outputs; distance > 1 is change." + model_class_path = "model_repos/DSAMNet/model/dsamnet.py:DSAMNet" + input_format = "two tensors (A, B), each [B, 3, H, W]" + output_format = "tuple(dist [B,1,H,W], ds2, ds3)" + checkpoint_format = "raw state dict or CD-Models model_state_dict" + final_output_for_metrics = "dist > 1.0" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "model_repos" / "DSAMNet" + with repo_import_context(repo): + module = importlib.import_module("model.dsamnet") + model = module.DSAMNet(2) + return model.to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state = checkpoint["model_state_dict"] + elif isinstance(checkpoint, dict) and all(torch.is_tensor(v) for v in checkpoint.values()): + state = checkpoint + else: + raise RuntimeError(f"DSAMNet checkpoint {checkpoint_path} has no recognized state dict.") + model.load_state_dict(state, strict=True) + + def forward(self, model: nn.Module, batch: tuple, device: torch.device) -> Any: + a, b, _mask, _names = batch + return model(a.to(device, non_blocking=True), b.to(device, non_blocking=True)) + + def normalize_output(self, raw_output: Any, batch: tuple, dataset_config: dict) -> NormalizedOutput: + dist = raw_output[0].detach().cpu() if isinstance(raw_output, (list, tuple)) else raw_output.detach().cpu() + binary = (dist > 1.0).float() + return NormalizedOutput(binary=binary, score=None, metric_tensor=binary) + + def get_dummy_inputs(self, dataset_config: dict, device: torch.device) -> tuple: + size = int(dataset_config.get("img_size", dataset_config.get("image_size", 256))) + return ( + torch.zeros(1, 3, size, size, device=device), + torch.zeros(1, 3, size, size, device=device), + ) + + def compute_loss(self, raw_output: Any, batch: tuple, model_config: dict, dataset_config: dict, device: torch.device) -> dict[str, torch.Tensor]: + if not isinstance(raw_output, (list, tuple)) or len(raw_output) < 3: + raise RuntimeError("DSAMNet training expects (dist, ds2, ds3).") + dist, ds2, ds3 = raw_output[:3] + repo = ROOT / "model_repos" / "DSAMNet" + with repo_import_context(repo): + bcl_module = importlib.import_module("loss.BCL") + dice_module = importlib.import_module("loss.DiceLoss") + bcl = bcl_module.BCL().to(device) + dice = dice_module.DiceLoss().to(device) + mask = batch[2].to(device, non_blocking=True).float() + one_hot = torch.cat([1.0 - mask, mask], dim=1) + dice_loss = 0.5 * (dice(ds2, one_hot) + dice(ds3, one_hot)) + ct_loss = bcl(dist, mask.clone()) + total = ct_loss + float(model_config.get("wDice", 0.5)) * dice_loss + return {"loss": total, "contrastive_loss": ct_loss.detach(), "dice_loss": dice_loss.detach()} + + +class CGNetAdapter(TwoTensorLogitAdapter): + model_name = "cgnet" + notes_or_failure_reason = "CGNet returns (change_map, final_map); final_map is used for metrics." + model_class_path = "model_repos/CGNet-CD/network/CGNet.py:CGNet" + output_format = "tuple(one-channel coarse logits, one-channel final logits)" + checkpoint_format = "raw state dict or CD-Models model_state_dict" + final_output_for_metrics = "tuple[-1] final_map" + + def get_threshold_mode(self) -> str: + return "threshold" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "model_repos" / "CGNet-CD" + with repo_import_context(repo): + module = importlib.import_module("network.CGNet") + model = module.CGNet() + return model.to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state = checkpoint["model_state_dict"] + elif isinstance(checkpoint, dict) and all(torch.is_tensor(v) for v in checkpoint.values()): + state = checkpoint + else: + raise RuntimeError(f"CGNet checkpoint {checkpoint_path} has no recognized state dict.") + model.load_state_dict(state, strict=True) + + def compute_loss(self, raw_output: Any, batch: tuple, model_config: dict, dataset_config: dict, device: torch.device) -> dict[str, torch.Tensor]: + if not isinstance(raw_output, (list, tuple)) or len(raw_output) < 2: + raise RuntimeError("CGNet training expects a tuple of two prediction maps.") + target = batch[2].to(device, non_blocking=True).float() + loss1 = F.binary_cross_entropy_with_logits(raw_output[0], target) + loss2 = F.binary_cross_entropy_with_logits(raw_output[1], target) + loss = loss1 + loss2 + return {"loss": loss, "coarse_loss": loss1.detach(), "final_loss": loss2.detach()} + + +class HANetAdapter(TwoTensorLogitAdapter): + model_name = "hanet" + supports_amp_training = False + notes_or_failure_reason = "HANet HAN returns a tuple; tuple[-1] is used for metrics." + model_class_path = "model_repos/HANet-CD/models/HANet.py:HAN" + output_format = "tuple containing two-channel logits [B, 2, H, W]" + checkpoint_format = "full torch-saved model, raw state dict, or CD-Models model_state_dict" + final_output_for_metrics = "tuple[-1]" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "model_repos" / "HANet-CD" + with repo_import_context(repo): + module = importlib.import_module("models.HANet") + model = module.HAN(3, 2) + return model.to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, nn.Module): + model.load_state_dict(checkpoint.state_dict(), strict=True) + return + if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state = checkpoint["model_state_dict"] + elif isinstance(checkpoint, dict) and all(torch.is_tensor(v) for v in checkpoint.values()): + state = checkpoint + else: + raise RuntimeError(f"HANet checkpoint {checkpoint_path} has no recognized model/state dict.") + model.load_state_dict(state, strict=True) + + def compute_loss(self, raw_output: Any, batch: tuple, model_config: dict, dataset_config: dict, device: torch.device) -> dict[str, torch.Tensor]: + logits = raw_output[-1] if isinstance(raw_output, (list, tuple)) else raw_output + logits = logits.float() + target = batch[2].long().to(device, non_blocking=True) + if target.ndim == 4: + target = target.squeeze(1) + ce = F.cross_entropy(logits, target) + probas = F.softmax(logits, dim=1) + one_hot = F.one_hot(target, num_classes=logits.shape[1]).permute(0, 3, 1, 2).float() + dims = (0, 2, 3) + dice = 1.0 - ((2.0 * torch.sum(probas * one_hot, dims) + 1e-7) / (torch.sum(probas + one_hot, dims) + 1e-7)).mean() + loss = ce + dice + return {"loss": loss, "cross_entropy": ce.detach(), "dice_loss": dice.detach()} + + +class ELGCNetAdapter(TwoTensorLogitAdapter): + model_name = "elgcnet" + notes_or_failure_reason = "ELGCNet models.networks.define_G with net_G=ELGCNet and dec_embed_dim=256." + model_class_path = "model_repos/elgcnet/models/networks.py:define_G" + output_format = "two-channel logits [B, 2, H, W]" + checkpoint_format = "upstream model_G_state_dict, raw state dict, or CD-Models model_state_dict" + final_output_for_metrics = "raw two-channel logits" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "model_repos" / "elgcnet" + with repo_import_context(repo): + networks = importlib.import_module("models.networks") + args = SimpleNamespace(net_G="ELGCNet", dec_embed_dim=256) + model = networks.define_G(args, gpu_ids=[]) + return model.to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, dict) and "model_G_state_dict" in checkpoint: + state = checkpoint["model_G_state_dict"] + elif isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state = checkpoint["model_state_dict"] + elif isinstance(checkpoint, dict) and all(torch.is_tensor(v) for v in checkpoint.values()): + state = checkpoint + else: + raise RuntimeError(f"ELGCNet checkpoint {checkpoint_path} has no recognized state dict.") + model.load_state_dict(state, strict=True) + + +class DSIFNAdapter(BaseModelAdapter): + supports_inprocess_eval = True + supports_unified_eval = True + supports_unified_training = True + supports_flops = True + supports_amp_training = False + notes_or_failure_reason = "IFNet/pytorch version DSIFN with two VGG16 feature towers; upstream validation uses preds[-1]." + model_class_path = "IFNet/pytorch version/DSIFN.py:DSIFN" + input_format = "two tensors (A, B), each [B, 3, H, W]" + output_format = "tuple of five one-channel sigmoid maps" + checkpoint_format = "raw state dict, CD-Models model_state_dict, or upstream state_dict" + final_output_for_metrics = "tuple[-1], resized to target mask then thresholded" + + def __init__(self, model_name: str) -> None: + super().__init__(model_name) + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "IFNet" / "pytorch version" + with repo_import_context(repo): + import torchvision.models as tv_models + + original_vgg16 = tv_models.vgg16 + + def vgg16_no_download(*args: Any, **kwargs: Any) -> nn.Module: + kwargs.pop("pretrained", None) + kwargs["weights"] = None + return original_vgg16(*args, **kwargs) + + tv_models.vgg16 = vgg16_no_download + try: + module = importlib.import_module("DSIFN") + model = module.DSIFN(module.vgg16_base(), module.vgg16_base()) + finally: + tv_models.vgg16 = original_vgg16 + return model.to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state = checkpoint["model_state_dict"] + if any(str(key).startswith("net.") for key in state): + model.load_state_dict(state, strict=True) + return + elif isinstance(checkpoint, dict) and "state_dict" in checkpoint: + state = checkpoint["state_dict"] + elif isinstance(checkpoint, dict) and all(torch.is_tensor(v) for v in checkpoint.values()): + state = checkpoint + else: + raise RuntimeError(f"{self.model_name} checkpoint {checkpoint_path} has no recognized state dict.") + model.load_state_dict(state, strict=True) + + def forward(self, model: nn.Module, batch: tuple, device: torch.device) -> Any: + a, b, _mask, _names = batch + return model(a.to(device, non_blocking=True), b.to(device, non_blocking=True)) + + def normalize_output(self, raw_output: Any, batch: tuple, dataset_config: dict) -> NormalizedOutput: + if not isinstance(raw_output, (list, tuple)) or not raw_output: + raise RuntimeError(f"{self.model_name} expected DSIFN tuple output, got {type(raw_output)!r}.") + score = raw_output[-1].detach().cpu() + target = batch[2] + if score.shape[-2:] != target.shape[-2:]: + score = F.interpolate(score, size=target.shape[-2:], mode="bilinear", align_corners=True) + threshold = float(dataset_config.get("eval", {}).get("threshold", 0.5)) + binary, score = normalize_binary_prediction(score, threshold=threshold) + return NormalizedOutput(binary=binary, score=score, metric_tensor=score) + + def get_dummy_inputs(self, dataset_config: dict, device: torch.device) -> tuple: + size = int(dataset_config.get("img_size", dataset_config.get("image_size", 256))) + return ( + torch.zeros(1, 3, size, size, device=device), + torch.zeros(1, 3, size, size, device=device), + ) + + def get_threshold_mode(self) -> str: + return "threshold" + + def compute_loss(self, raw_output: Any, batch: tuple, model_config: dict, dataset_config: dict, device: torch.device) -> dict[str, torch.Tensor]: + if not isinstance(raw_output, (list, tuple)) or not raw_output: + raise RuntimeError(f"{self.model_name} training expects five DSIFN outputs.") + target = batch[2].to(device, non_blocking=True).float() + repo = ROOT / "IFNet" / "pytorch version" + with repo_import_context(repo): + loss_module = importlib.import_module("loss") + cd_loss = loss_module.cd_loss + total = torch.zeros((), device=device) + for pred in raw_output: + pred = pred.float() + if pred.shape[-2:] != target.shape[-2:]: + pred = F.interpolate(pred, size=target.shape[-2:], mode="bilinear", align_corners=True) + total = total + cd_loss(pred.squeeze(1), target.squeeze(1)) + loss = total / len(raw_output) + return {"loss": loss, "dsifn_loss": loss.detach()} + + +class SChangerModule(nn.Module): + def __init__(self, net: nn.Module) -> None: + super().__init__() + self.net = net + + def forward(self, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + return self.net((a, b)) + + +class SChangerAdapter(TwoTensorLogitAdapter): + model_name = "schanger" + notes_or_failure_reason = "SChanger-base from SChanger/cd/SChanger.py; eval forward returns sigmoid probability map." + model_class_path = "SChanger/cd/SChanger.py:SChanger" + output_format = "one-channel sigmoid probability map [B, 1, H, W] in eval mode" + checkpoint_format = "raw state dict, CD-Models model_state_dict, or upstream state_dict" + final_output_for_metrics = "eval-mode sigmoid probability thresholded with dataset eval threshold" + + def get_threshold_mode(self) -> str: + return "threshold" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "SChanger" + with repo_import_context(repo): + module = importlib.import_module("cd.SChanger") + net = module.SChanger( + num_classes=1, + input_channels=3, + c_list=[8 * 3, 8 * 4, 8 * 6, 8 * 8, 8 * 13, 8 * 15], + dropout=0.2, + ) + return SChangerModule(net).to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state = checkpoint["model_state_dict"] + if any(str(key).startswith(("net.", "trainer.")) for key in state): + model.load_state_dict(state, strict=True) + return + elif isinstance(checkpoint, dict) and "state_dict" in checkpoint: + state = checkpoint["state_dict"] + elif isinstance(checkpoint, dict) and all(torch.is_tensor(v) for v in checkpoint.values()): + state = checkpoint + else: + raise RuntimeError(f"SChanger checkpoint {checkpoint_path} has no recognized state dict.") + model.net.load_state_dict(state, strict=True) + + def compute_loss(self, raw_output: Any, batch: tuple, model_config: dict, dataset_config: dict, device: torch.device) -> dict[str, torch.Tensor]: + target = batch[2].to(device, non_blocking=True).float() + output = raw_output + if output.ndim != 4: + raise RuntimeError(f"SChanger training expected [B,C,H,W] output, got {tuple(output.shape)}.") + total = torch.zeros((), device=device) + for idx in range(output.shape[1]): + total = total + F.binary_cross_entropy_with_logits(output[:, idx:idx + 1], target) + loss = total / output.shape[1] + return {"loss": loss, "deep_supervision_bce": loss.detach()} + + +class Change3DBCDModule(nn.Module): + def __init__(self, trainer: nn.Module) -> None: + super().__init__() + self.trainer = trainer + + def forward(self, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + return self.trainer.update_bcd(a, b) + + +class Change3DAdapter(TwoTensorLogitAdapter): + model_name = "change3d" + notes_or_failure_reason = "Change3D Trainer.update_bcd with X3D-L encoder and ChangeDecoder sigmoid output." + model_class_path = "Change3D/model/trainer.py:Trainer" + output_format = "one-channel sigmoid probability map [B, 1, H, W]" + checkpoint_format = "raw Trainer state dict, CD-Models model_state_dict, or upstream state_dict" + final_output_for_metrics = "update_bcd sigmoid probability thresholded with dataset eval threshold" + + def get_threshold_mode(self) -> str: + return "threshold" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "Change3D" + size = int(dataset_config.get("img_size", dataset_config.get("image_size", 256))) + pretrained = repo / "model" / "X3D_L.pyth" + args = SimpleNamespace( + dataset=str(dataset_config.get("name", "")).upper() + "_CD", + in_height=size, + in_width=size, + num_perception_frame=1, + num_class=1, + pretrained=str(pretrained) if pretrained.exists() else None, + batch_size=int(dataset_config.get("batch_size", 1)), + lr=float(model_config.get("lr", 0.0002)), + lr_mode="poly", + max_steps=1, + max_epochs=1, + step_loss=100, + ) + with repo_import_context(repo): + module = importlib.import_module("model.trainer") + trainer = module.Trainer(args) + return Change3DBCDModule(trainer).to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + checkpoint = torch.load(checkpoint_path, map_location=device) + if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state = checkpoint["model_state_dict"] + elif isinstance(checkpoint, dict) and "state_dict" in checkpoint: + state = checkpoint["state_dict"] + elif isinstance(checkpoint, dict) and all(torch.is_tensor(v) for v in checkpoint.values()): + state = checkpoint + else: + raise RuntimeError(f"Change3D checkpoint {checkpoint_path} has no recognized state dict.") + model.trainer.load_state_dict(state, strict=True) + + def compute_loss(self, raw_output: Any, batch: tuple, model_config: dict, dataset_config: dict, device: torch.device) -> dict[str, torch.Tensor]: + target = batch[2].to(device, non_blocking=True).float() + output = raw_output.float() + if output.shape[-2:] != target.shape[-2:]: + output = F.interpolate(output, size=target.shape[-2:], mode="bilinear", align_corners=True) + bce = F.binary_cross_entropy(output.clamp(1e-6, 1 - 1e-6), target) + smooth = 1.0 + intersection = torch.sum(output * target) + dice = 1.0 - ((2.0 * intersection + smooth) / (torch.sum(output) + torch.sum(target) + smooth)) + loss = bce + dice + return {"loss": loss, "bce_loss": bce.detach(), "dice_loss": dice.detach()} + + +class ChangeMambaAdapter(TwoTensorLogitAdapter): + model_name = "changemamba" + supports_unified_training = False + notes_or_failure_reason = "ChangeMambaBCD with VMamba backbone; upstream training checkpoint stores weights under the model key." + model_class_path = "model_repos/ChangeMamba/changedetection/models/ChangeMambaBCD.py:ChangeMambaBCD" + output_format = "two-channel logits [B, 2, H, W]" + checkpoint_format = "ChangeMamba training checkpoint with model key, state_dict key, or raw state dict" + final_output_for_metrics = "raw two-channel logits" + + def build_model(self, model_config: dict, dataset_config: dict, device: torch.device) -> nn.Module: + repo = ROOT / "model_repos" / "ChangeMamba" + variant = str(model_config.get("vmamba_variant", "small")).lower() + cfg_name = { + "tiny": "vssm_tiny_224_0229flex.yaml", + "small": "vssm_small_224.yaml", + "base": "vssm_base_224.yaml", + }.get(variant) + if cfg_name is None: + raise ValueError(f"Unsupported ChangeMamba VMamba variant: {variant}") + cfg_path = repo / "changedetection" / "configs" / "vssm1" / cfg_name + args = SimpleNamespace( + cfg=str(cfg_path), + opts=None, + batch_size=int(dataset_config.get("batch_size", 8)), + data_path=None, + zip=None, + cache_mode=None, + pretrained=None, + encoder_pretrained_path=None, + model_checkpoint_path=None, + resume=None, + resume_training_path=None, + accumulation_steps=None, + use_checkpoint=None, + disable_amp=None, + output=None, + tag=None, + eval=None, + throughput=None, + enable_amp=None, + fused_layernorm=None, + optim=None, + ) + with repo_import_context(repo): + config_module = importlib.import_module("changedetection.configs.config") + utils_module = importlib.import_module("changedetection.script.script_utils") + model_module = importlib.import_module("changedetection.models.ChangeMambaBCD") + config = config_module.get_config(args) + model = model_module.ChangeMambaBCD(pretrained=None, **utils_module.get_vssm_kwargs(config)) + return model.to(device) + + def load_checkpoint(self, model: nn.Module, checkpoint_path: Path, device: torch.device) -> None: + # Load large upstream ChangeMamba training archives on CPU first. + # Direct CUDA deserialization can fail with PytorchStreamReader read + # errors on otherwise valid zip checkpoints, while load_state_dict will + # copy the extracted tensors into the already-placed CUDA model. + checkpoint = torch.load(checkpoint_path, map_location="cpu") + with repo_import_context(ROOT / "model_repos" / "ChangeMamba"): + checkpoint_module = importlib.import_module("changedetection.checkpoints") + state = checkpoint_module.extract_model_state_dict(checkpoint) + model.load_state_dict(state, strict=True) + + +_SUPPORTED = { + "fc_ef": FCAdapter, + "fc_siam_conc": FCAdapter, + "fc_siam_diff": FCAdapter, + "bifa": BiFAAdapter, + "bit_cd": BITAdapter, + "changeformer": ChangeFormerAdapter, + "changemamba": ChangeMambaAdapter, + "change3d": Change3DAdapter, + "siam_nestedunet": SiamNestedUNetAdapter, + "stanet": STANetAdapter, + "tinycd": TinyCDAdapter, + "dsamnet": DSAMNetAdapter, + "dsifn": DSIFNAdapter, + "ifnet": DSIFNAdapter, + "cgnet": CGNetAdapter, + "hanet": HANetAdapter, + "elgcnet": ELGCNetAdapter, + "schanger": SChangerAdapter, +} + + +_UNSUPPORTED_REASONS = { + "cdmamba": "CDMamba requires the cd-mamba-ssm environment with CUDA-visible mamba_ssm/Triton; cd-mamba-ssm is not available in this shell, and Mamba reports torch.cuda.is_available() == False.", + "changer": "Changer command construction is wired through Open-CD/OpenMMLab; verify runtime dependencies in cd-openmmlab before treating it as trainable.", + "rsm_cd": "RSM-CD requires the cd-mamba-ssm environment and VMamba/selective-scan CUDA extensions; cd-mamba-ssm is not available in this shell.", +} + + +def get_model_adapter(model_name: str) -> BaseModelAdapter: + if model_name in _SUPPORTED: + cls = _SUPPORTED[model_name] + return cls(model_name) if cls in {FCAdapter, DSIFNAdapter} else cls() + return UnsupportedAdapter(model_name, _UNSUPPORTED_REASONS.get(model_name, "No adapter registered.")) diff --git a/utils/profiling.py b/utils/profiling.py new file mode 100644 index 0000000000000000000000000000000000000000..74a9424095650956c76d3fa18ce098dedc7598fe --- /dev/null +++ b/utils/profiling.py @@ -0,0 +1,149 @@ +from __future__ import annotations + +import importlib.util +import threading +import time +from contextlib import AbstractContextManager +from dataclasses import dataclass, field +from typing import Callable + +import torch + + +class ProfilingUnavailable(RuntimeError): + pass + + +def count_parameters(model: torch.nn.Module) -> dict[str, float]: + total = sum(p.numel() for p in model.parameters()) + trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) + return { + "params": float(total), + "params_m": total / 1_000_000.0, + "trainable_params": float(trainable), + "trainable_params_m": trainable / 1_000_000.0, + } + + +def count_flops(model: torch.nn.Module, input_factory: Callable[[], tuple], device: torch.device) -> dict[str, object]: + model_was_training = model.training + model.eval() + inputs = tuple(x.to(device) if torch.is_tensor(x) else x for x in input_factory()) + shape = [list(x.shape) for x in inputs if torch.is_tensor(x)] + try: + if importlib.util.find_spec("thop") is not None: + from thop import profile + + flops, _ = profile(model, inputs=inputs, verbose=False) + return { + "flops": float(flops), + "flops_g": float(flops) / 1_000_000_000.0, + "flops_input_shape": shape, + "flops_library": "thop", + } + if importlib.util.find_spec("fvcore") is not None: + from fvcore.nn import FlopCountAnalysis + + flops = FlopCountAnalysis(model, inputs).total() + return { + "flops": float(flops), + "flops_g": float(flops) / 1_000_000_000.0, + "flops_input_shape": shape, + "flops_library": "fvcore", + } + if importlib.util.find_spec("ptflops") is not None: + raise ProfilingUnavailable( + "ptflops is installed, but this repository needs a two-input adapter before it can be used safely." + ) + raise ProfilingUnavailable("Install one FLOPs backend: thop or fvcore.") + finally: + model.train(model_was_training) + + +@dataclass +class GpuProfiler(AbstractContextManager): + device: torch.device + interval_s: float = 0.1 + required: bool = False + _stop: threading.Event = field(default_factory=threading.Event, init=False) + _thread: threading.Thread | None = field(default=None, init=False) + _samples_util: list[float] = field(default_factory=list, init=False) + _samples_mem: list[float] = field(default_factory=list, init=False) + _nvml: object | None = field(default=None, init=False) + _handle: object | None = field(default=None, init=False) + _error: str | None = field(default=None, init=False) + + def __enter__(self) -> "GpuProfiler": + if self.device.type != "cuda": + self._error = "CUDA is not available; GPU utilization profiling was not run." + if self.required: + raise ProfilingUnavailable(self._error) + return self + if importlib.util.find_spec("pynvml") is None: + self._error = "pynvml is not installed; install pynvml for GPU utilization profiling." + if self.required: + raise ProfilingUnavailable(self._error) + return self + import pynvml + + self._nvml = pynvml + pynvml.nvmlInit() + index = self.device.index if self.device.index is not None else torch.cuda.current_device() + self._handle = pynvml.nvmlDeviceGetHandleByIndex(index) + torch.cuda.reset_peak_memory_stats(self.device) + self._thread = threading.Thread(target=self._sample_loop, daemon=True) + self._thread.start() + return self + + def _sample_loop(self) -> None: + assert self._nvml is not None and self._handle is not None + while not self._stop.is_set(): + util = self._nvml.nvmlDeviceGetUtilizationRates(self._handle) + mem = self._nvml.nvmlDeviceGetMemoryInfo(self._handle) + self._samples_util.append(float(util.gpu)) + self._samples_mem.append(float(mem.used) / (1024.0**3)) + time.sleep(self.interval_s) + + def __exit__(self, exc_type, exc, tb) -> bool: + self._stop.set() + if self._thread is not None: + self._thread.join(timeout=2.0) + if self._nvml is not None: + self._nvml.nvmlShutdown() + return False + + def summary(self) -> dict[str, object]: + if self.device.type == "cuda": + index = self.device.index if self.device.index is not None else torch.cuda.current_device() + base = { + "gpu_index": int(index), + "gpu_name": torch.cuda.get_device_name(self.device), + "gpu_mem_allocated_peak_gb": torch.cuda.max_memory_allocated(self.device) / (1024.0**3), + "gpu_mem_reserved_peak_gb": torch.cuda.max_memory_reserved(self.device) / (1024.0**3), + } + else: + base = { + "gpu_index": None, + "gpu_name": None, + "gpu_mem_allocated_peak_gb": None, + "gpu_mem_reserved_peak_gb": None, + } + if not self._samples_util: + base.update({ + "gpu_util_mean": None, + "gpu_util_max": None, + "gpu_util_min": None, + "gpu_mem_used_mean_gb": None, + "gpu_mem_used_max_gb": None, + "gpu_profiling_error": self._error, + }) + return base + base.update({ + "gpu_util_mean": sum(self._samples_util) / len(self._samples_util), + "gpu_util_max": max(self._samples_util), + "gpu_util_min": min(self._samples_util), + "gpu_mem_used_mean_gb": sum(self._samples_mem) / len(self._samples_mem), + "gpu_mem_used_max_gb": max(self._samples_mem), + "gpu_profiling_error": None, + }) + return base diff --git a/utils/qualitative.py b/utils/qualitative.py new file mode 100644 index 0000000000000000000000000000000000000000..9a92b77c3494ed598a335a47a3c6ca8e918838dc --- /dev/null +++ b/utils/qualitative.py @@ -0,0 +1,188 @@ +from __future__ import annotations + +import json +import random +import re +from pathlib import Path +from typing import Iterable + +import numpy as np +import torch +from PIL import Image, ImageDraw + +from datasets.cd_dataset import CDDataset + + +ROOT = Path(__file__).resolve().parents[1] + + +def safe_sample_id(sample_id: str) -> str: + return re.sub(r"[^A-Za-z0-9_.-]+", "_", str(sample_id)) + + +def _boundary_density(mask: np.ndarray) -> float: + mask = mask.astype(bool) + if not mask.any(): + return 0.0 + horiz = np.zeros_like(mask, dtype=bool) + vert = np.zeros_like(mask, dtype=bool) + horiz[:, 1:] = mask[:, 1:] != mask[:, :-1] + vert[1:, :] = mask[1:, :] != mask[:-1, :] + return float((horiz | vert).sum()) / float(mask.size) + + +def qualitative_manifest_path(dataset_name: str) -> Path: + return ROOT / "results" / "qualitative_samples" / dataset_name / "sample_manifest.json" + + +def select_or_load_manifest( + dataset_cfg: dict, + count: int = 20, + seed: int = 3407, + force: bool = False, +) -> dict: + dataset_name = dataset_cfg["name"] + path = qualitative_manifest_path(dataset_name) + if path.exists() and not force: + with path.open("r", encoding="utf-8") as f: + return json.load(f) + + ds = CDDataset(dataset_cfg["data_root"], "test", cfg=dataset_cfg, normalize=False, return_format="tuple") + rng = random.Random(seed) + rows = [] + for index, (a_path, b_path, mask_path, sample_id) in enumerate(ds.samples): + if not (a_path.is_file() and b_path.is_file() and mask_path.is_file()): + continue + mask = np.asarray(Image.open(mask_path).convert("L").resize((ds.image_size, ds.image_size), Image.NEAREST)) + binary = mask > (0 if mask.max() <= 1 else ds.threshold) + ratio = float(binary.mean()) + boundary = _boundary_density(binary) + if ratio <= 0.0 or ratio >= 0.85 or boundary <= 0.0: + continue + rows.append({ + "index": index, + "sample_id": sample_id, + "a_path": str(a_path), + "b_path": str(b_path), + "mask_path": str(mask_path), + "changed_ratio": ratio, + "boundary_density": boundary, + "tie": rng.random(), + }) + + if len(ds) >= count and len(rows) < count: + raise RuntimeError( + f"Only {len(rows)} visually useful test samples found for {dataset_name}, " + f"but {count} are required from {len(ds)} total test samples." + ) + + rows = sorted(rows, key=lambda r: (-r["boundary_density"], -r["changed_ratio"], r["sample_id"], r["tie"])) + selected = rows[: min(count, len(rows))] + for rank, row in enumerate(selected, start=1): + row["rank"] = rank + row.pop("tie", None) + path.parent.mkdir(parents=True, exist_ok=True) + manifest = { + "dataset": dataset_name, + "split": "test", + "seed": seed, + "requested_count": count, + "selected_count": len(selected), + "total_test_samples": len(ds), + "selection_rule": "non-empty mask, changed_ratio < 0.85, positive boundary density, sorted deterministically", + "samples": selected, + } + with path.open("w", encoding="utf-8") as f: + json.dump(manifest, f, indent=2, sort_keys=True) + return manifest + + +def manifest_ids(manifest: dict) -> set[str]: + return {str(row["sample_id"]) for row in manifest.get("samples", [])} + + +def denormalize(tensor: torch.Tensor, mean: list[float], std: list[float]) -> torch.Tensor: + if tensor.ndim == 3: + mean_t = torch.tensor(mean, dtype=tensor.dtype, device=tensor.device).view(3, 1, 1) + std_t = torch.tensor(std, dtype=tensor.dtype, device=tensor.device).view(3, 1, 1) + else: + mean_t = torch.tensor(mean, dtype=tensor.dtype, device=tensor.device).view(1, 3, 1, 1) + std_t = torch.tensor(std, dtype=tensor.dtype, device=tensor.device).view(1, 3, 1, 1) + return tensor * std_t + mean_t + + +def tensor_to_rgb_image(tensor: torch.Tensor) -> Image.Image: + if tensor.ndim == 4: + tensor = tensor[0] + arr = tensor.detach().cpu().float().clamp(0, 1).numpy() + if arr.shape[0] == 1: + arr = np.repeat(arr, 3, axis=0) + arr = np.transpose(arr[:3], (1, 2, 0)) + return Image.fromarray((arr * 255).astype(np.uint8), mode="RGB") + + +def mask_to_image(mask: torch.Tensor | np.ndarray) -> Image.Image: + if torch.is_tensor(mask): + arr = mask.detach().cpu().numpy() + else: + arr = mask + arr = np.squeeze(arr) + arr = (arr > 0).astype(np.uint8) * 255 + return Image.fromarray(arr, mode="L").convert("RGB") + + +def overlay_image(gt: torch.Tensor, pred: torch.Tensor) -> Image.Image: + gt_arr = np.squeeze(gt.detach().cpu().numpy()).astype(bool) + pred_arr = np.squeeze(pred.detach().cpu().numpy()).astype(bool) + rgb = np.zeros((*gt_arr.shape, 3), dtype=np.uint8) + rgb[gt_arr & pred_arr] = (255, 255, 255) + rgb[pred_arr & ~gt_arr] = (255, 64, 64) + rgb[gt_arr & ~pred_arr] = (64, 160, 255) + return Image.fromarray(rgb, mode="RGB") + + +def save_binary_prediction(pred: torch.Tensor, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + mask_to_image(pred).convert("L").save(path) + + +def save_probability_map(prob: torch.Tensor, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + arr = np.squeeze(prob.detach().cpu().float().clamp(0, 1).numpy()) + Image.fromarray((arr * 255).astype(np.uint8), mode="L").save(path) + + +def save_visual_panel( + a: torch.Tensor, + b: torch.Tensor, + gt: torch.Tensor, + pred: torch.Tensor, + out_path: Path, + prob: torch.Tensor | None = None, +) -> None: + tiles = [ + ("A", tensor_to_rgb_image(a)), + ("B", tensor_to_rgb_image(b)), + ("GT", mask_to_image(gt)), + ("Pred", mask_to_image(pred)), + ("Overlay", overlay_image(gt, pred)), + ] + if prob is not None: + tiles.insert(4, ("Score", mask_to_image((prob >= 0.5).float()))) + tile_w, tile_h = tiles[0][1].size + label_h = 24 + panel = Image.new("RGB", (tile_w * len(tiles), tile_h + label_h), "white") + draw = ImageDraw.Draw(panel) + for i, (label, img) in enumerate(tiles): + x = i * tile_w + panel.paste(img.resize((tile_w, tile_h), Image.NEAREST), (x, label_h)) + draw.text((x + 6, 5), label, fill=(0, 0, 0)) + out_path.parent.mkdir(parents=True, exist_ok=True) + panel.save(out_path) + + +def rank_for_sample(manifest: dict, sample_id: str) -> int | None: + for row in manifest.get("samples", []): + if str(row["sample_id"]) == str(sample_id): + return int(row["rank"]) + return None diff --git a/utils/results.py b/utils/results.py new file mode 100644 index 0000000000000000000000000000000000000000..a2ef54cca6e2c43229a467d4c1649314e3f8d22e --- /dev/null +++ b/utils/results.py @@ -0,0 +1,46 @@ +from __future__ import annotations + +import csv +import json +from datetime import datetime, timezone +from pathlib import Path +from typing import Mapping + + +ROOT = Path(__file__).resolve().parents[1] + + +def write_json(data: Mapping, path: str | Path) -> Path: + path = Path(path) + if not path.is_absolute(): + path = ROOT / path + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8") as f: + json.dump(data, f, indent=2, sort_keys=True) + return path + + +def append_result(row: Mapping, path: str | Path = "results/results.csv") -> Path: + path = Path(path) + if not path.is_absolute(): + path = ROOT / path + path.parent.mkdir(parents=True, exist_ok=True) + payload = dict(row) + payload.setdefault("timestamp_utc", datetime.now(timezone.utc).isoformat()) + exists = path.exists() + fieldnames = sorted(payload) + if exists: + with path.open("r", newline="", encoding="utf-8") as f: + existing = csv.DictReader(f) + fieldnames = sorted(set(existing.fieldnames or []) | set(fieldnames)) + rows = [] + if exists: + with path.open("r", newline="", encoding="utf-8") as f: + rows = list(csv.DictReader(f)) + rows.append(payload) + with path.open("w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(rows) + return path + diff --git a/utils/results_writer.py b/utils/results_writer.py new file mode 100644 index 0000000000000000000000000000000000000000..db1ed89c10199907c8add13128ea47c74171e494 --- /dev/null +++ b/utils/results_writer.py @@ -0,0 +1,84 @@ +from __future__ import annotations + +import csv +import json +from pathlib import Path +from typing import Mapping + + +ROOT = Path(__file__).resolve().parents[1] +METRIC_COLUMNS = [ + "Model", + "Dataset", + "F1", + "IoU", + "mIoU", + "Precision", + "Recall", + "BF1", + "OA", + "Kappa", + "ParamsM", + "FLOPsG", + "FPS", + "GPUUtilMean", + "GPUMemPeakGB", + "BestEpoch", +] + + +def save_metrics( + model_name: str, + dataset_name: str, + split: str, + metrics_dict: Mapping, + output_dir: str | Path = "results", +) -> Path: + base = Path(output_dir) + if not base.is_absolute(): + base = ROOT / base + out_dir = base / model_name / dataset_name + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / f"metrics_{split}.json" + payload = dict(metrics_dict) + payload.setdefault("model", model_name) + payload.setdefault("dataset", dataset_name) + payload.setdefault("split", split) + with path.open("w", encoding="utf-8") as f: + json.dump(payload, f, indent=2, sort_keys=True) + return path + + +def append_to_comparison_table(results_dir: str | Path = "results") -> Path: + base = Path(results_dir) + if not base.is_absolute(): + base = ROOT / base + rows = [] + for path in sorted(base.glob("*/*/metrics_test.json")): + with path.open("r", encoding="utf-8") as f: + metrics = json.load(f) + rows.append({ + "Model": metrics.get("model", path.parents[1].name), + "Dataset": metrics.get("dataset", path.parent.name), + "F1": metrics.get("f1", metrics.get("F1", "")), + "IoU": metrics.get("iou", metrics.get("IoU", "")), + "mIoU": metrics.get("miou", metrics.get("mIoU", "")), + "Precision": metrics.get("precision", metrics.get("Precision", "")), + "Recall": metrics.get("recall", metrics.get("Recall", "")), + "BF1": metrics.get("bf1", metrics.get("BF1", "")), + "OA": metrics.get("oa", metrics.get("OA", "")), + "Kappa": metrics.get("kappa", metrics.get("Kappa", "")), + "ParamsM": metrics.get("params_m", ""), + "FLOPsG": metrics.get("flops_g", ""), + "FPS": metrics.get("fps", ""), + "GPUUtilMean": metrics.get("gpu_util_mean", ""), + "GPUMemPeakGB": metrics.get("gpu_mem_reserved_peak_gb", metrics.get("gpu_mem_allocated_peak_gb", "")), + "BestEpoch": metrics.get("best_epoch", metrics.get("epoch", "")), + }) + out = base / "comparison_table.csv" + base.mkdir(parents=True, exist_ok=True) + with out.open("w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=METRIC_COLUMNS) + writer.writeheader() + writer.writerows(rows) + return out diff --git a/utils/unified_evaluator.py b/utils/unified_evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..060aae12152b9226e083165d409300284f85d659 --- /dev/null +++ b/utils/unified_evaluator.py @@ -0,0 +1,343 @@ +from __future__ import annotations + +import time +from datetime import datetime, timezone +from pathlib import Path +from typing import Callable + +import torch +from torch.utils.data import DataLoader, Subset + +from datasets.cd_dataset import CDDataset +from utils.metrics import BinaryMetrics, BoundaryMetrics, normalize_binary_prediction +from utils.model_adapters import BaseModelAdapter +from utils.dataset_cache import dataloader_kwargs +from utils.profiling import GpuProfiler, ProfilingUnavailable, count_flops, count_parameters +from utils.qualitative import ( + denormalize, + manifest_ids, + rank_for_sample, + safe_sample_id, + save_binary_prediction, + save_probability_map, + save_visual_panel, + select_or_load_manifest, +) +from utils.results_writer import append_to_comparison_table, save_metrics + + +ROOT = Path(__file__).resolve().parents[1] + + +def load_state_dict(checkpoint_path: Path) -> dict: + checkpoint = torch.load(checkpoint_path, map_location="cpu") + if isinstance(checkpoint, dict): + for key in ("model_state_dict", "state_dict", "model"): + if key in checkpoint and isinstance(checkpoint[key], dict): + return checkpoint[key] + if all(torch.is_tensor(v) for v in checkpoint.values()): + return checkpoint + raise RuntimeError(f"Checkpoint {checkpoint_path} does not contain a recognized PyTorch state_dict.") + + +def evaluate_torch_model( + *, + model_name: str, + dataset_cfg: dict, + model: torch.nn.Module, + checkpoint_path: Path, + forward_fn: Callable[[torch.nn.Module, torch.Tensor, torch.Tensor], torch.Tensor], + device: torch.device, + batch_size: int | None = None, + max_batches: int | None = None, + strict_profiling: bool = True, + output_dir: Path | None = None, +) -> tuple[dict, int]: + dataset_name = dataset_cfg["name"] + out_dir = output_dir or ROOT / "results" / model_name / dataset_name + pred_dir = out_dir / "predictions" / "test" + prob_dir = out_dir / "predictions" / "test_prob" + visual_dir = out_dir / "visuals" / "selected_20" + eval_cfg = dataset_cfg.get("eval", {}) + threshold = float(eval_cfg.get("threshold", 0.5)) + boundary_tolerance = int(eval_cfg.get("boundary_tolerance", 2)) + + state = load_state_dict(checkpoint_path) + model.load_state_dict(state, strict=True) + model.to(device) + model.eval() + + ds = CDDataset(dataset_cfg["data_root"], "test", cfg=dataset_cfg, return_format="tuple") + if max_batches is not None: + ds_for_loader = Subset(ds, range(min(len(ds), max_batches * int(batch_size or dataset_cfg.get("batch_size", 1))))) + else: + ds_for_loader = ds + loader = DataLoader( + ds_for_loader, + batch_size=int(batch_size or dataset_cfg.get("batch_size", 8)), + shuffle=False, + **dataloader_kwargs(dataset_cfg, torch.cuda.is_available()), + ) + manifest = select_or_load_manifest(dataset_cfg) + selected = manifest_ids(manifest) + + metrics = BinaryMetrics(threshold=threshold) + boundary = BoundaryMetrics(tolerance=boundary_tolerance) + param_metrics = count_parameters(model) + flops_metrics: dict[str, object] + profiling_errors: list[str] = [] + try: + image_size = int(dataset_cfg.get("img_size", 256)) + flops_metrics = count_flops( + model, + lambda: ( + torch.zeros(1, 3, image_size, image_size), + torch.zeros(1, 3, image_size, image_size), + ), + device, + ) + except ProfilingUnavailable as exc: + flops_metrics = { + "flops": None, + "flops_g": None, + "flops_input_shape": [[1, 3, int(dataset_cfg.get("img_size", 256)), int(dataset_cfg.get("img_size", 256))]] * 2, + "flops_library": None, + "flops_error": str(exc), + } + profiling_errors.append(str(exc)) + + warmup_batches = min(2, len(loader)) + timed_images = 0 + model_time = 0.0 + end_to_end_start = time.perf_counter() + mean_a = dataset_cfg.get("mean_a", [0.485, 0.456, 0.406]) + std_a = dataset_cfg.get("std_a", [0.229, 0.224, 0.225]) + mean_b = dataset_cfg.get("mean_b", mean_a) + std_b = dataset_cfg.get("std_b", std_a) + + with torch.inference_mode(), GpuProfiler(device=device, required=False) as gpu_profiler: + for batch_idx, (a, b, mask, names) in enumerate(loader): + a = a.to(device, non_blocking=True) + b = b.to(device, non_blocking=True) + mask_device = mask.to(device, non_blocking=True) + if device.type == "cuda": + torch.cuda.synchronize(device) + start = time.perf_counter() + outputs = forward_fn(model, a, b) + if device.type == "cuda": + torch.cuda.synchronize(device) + elapsed = time.perf_counter() - start + pred, prob = normalize_binary_prediction(outputs.detach().cpu(), threshold=threshold) + metrics.update(outputs.detach().cpu(), mask) + boundary.update(pred, mask) + if batch_idx >= warmup_batches: + model_time += elapsed + timed_images += int(a.shape[0]) + + for i, sample_id in enumerate(names): + clean_id = safe_sample_id(str(sample_id)) + pred_i = pred[i] + save_binary_prediction(pred_i, pred_dir / f"{clean_id}_pred.png") + prob_i = prob[i] if prob is not None else None + if prob_i is not None: + save_probability_map(prob_i, prob_dir / f"{clean_id}_prob.png") + if str(sample_id) in selected: + rank = rank_for_sample(manifest, str(sample_id)) + a_vis = denormalize(a[i].detach().cpu(), mean_a, std_a) + b_vis = denormalize(b[i].detach().cpu(), mean_b, std_b) + save_visual_panel( + a_vis, + b_vis, + mask[i], + pred_i, + visual_dir / f"{rank:02d}_{clean_id}_panel.png", + prob=prob_i, + ) + del mask_device + end_to_end_time = time.perf_counter() - end_to_end_start + + split_metrics = metrics.compute() + split_metrics.update(boundary.compute()) + split_metrics.update(param_metrics) + split_metrics.update(flops_metrics) + split_metrics.update(gpu_profiler.summary()) + + if split_metrics.get("gpu_profiling_error"): + profiling_errors.append(str(split_metrics["gpu_profiling_error"])) + fps_model_only = timed_images / model_time if model_time > 0 else None + fps_end_to_end = len(ds_for_loader) / end_to_end_time if end_to_end_time > 0 else None + status = "complete" if not (strict_profiling and profiling_errors) else "incomplete" + split_metrics.update({ + "model": model_name, + "dataset": dataset_name, + "split": "test", + "checkpoint": str(checkpoint_path), + "threshold": threshold, + "fps": fps_model_only, + "fps_model_only": fps_model_only, + "fps_end_to_end": fps_end_to_end, + "num_timed_images": timed_images, + "warmup_batches": warmup_batches, + "timing_device": str(device), + "test_num_samples": len(ds_for_loader), + "visual_sample_manifest": str(ROOT / "results" / "qualitative_samples" / dataset_name / "sample_manifest.json"), + "prediction_dir": str(pred_dir), + "visual_dir": str(visual_dir), + "timestamp": datetime.now(timezone.utc).isoformat(), + "status": status, + "profiling_errors": profiling_errors, + }) + save_metrics(model_name, dataset_name, "test", split_metrics) + append_to_comparison_table() + return split_metrics, 0 if status == "complete" else 1 + + +def evaluate_with_adapter( + *, + model_name: str, + dataset_cfg: dict, + model_config: dict, + adapter: BaseModelAdapter, + checkpoint_path: Path, + device: torch.device, + batch_size: int | None = None, + max_batches: int | None = None, + strict_profiling: bool = True, + output_dir: Path | None = None, +) -> tuple[dict, int]: + if not adapter.supports_inprocess_eval: + raise RuntimeError(f"{model_name} does not support in-process evaluation: {adapter.notes_or_failure_reason}") + dataset_name = dataset_cfg["name"] + out_dir = output_dir or ROOT / "results" / model_name / dataset_name + pred_dir = out_dir / "predictions" / "test" + prob_dir = out_dir / "predictions" / "test_prob" + visual_dir = out_dir / "visuals" / "selected_20" + eval_cfg = dataset_cfg.get("eval", {}) + threshold = float(eval_cfg.get("threshold", 0.5)) + boundary_tolerance = int(eval_cfg.get("boundary_tolerance", 2)) + + model = adapter.build_model(model_config, dataset_cfg, device) + adapter.load_checkpoint(model, checkpoint_path, device) + model.to(device) + model.eval() + + ds = CDDataset(dataset_cfg["data_root"], "test", cfg=dataset_cfg, return_format="tuple") + if max_batches is not None: + ds_for_loader = Subset(ds, range(min(len(ds), max_batches * int(batch_size or dataset_cfg.get("batch_size", 1))))) + else: + ds_for_loader = ds + loader = DataLoader( + ds_for_loader, + batch_size=int(batch_size or dataset_cfg.get("batch_size", 8)), + shuffle=False, + **dataloader_kwargs(dataset_cfg, torch.cuda.is_available()), + ) + manifest = select_or_load_manifest(dataset_cfg) + selected = manifest_ids(manifest) + + metrics = BinaryMetrics(threshold=threshold) + boundary = BoundaryMetrics(tolerance=boundary_tolerance) + param_metrics = count_parameters(model) + profiling_errors: list[str] = [] + try: + if not adapter.supports_flops: + raise ProfilingUnavailable(f"{model_name} adapter does not support FLOPs: {adapter.notes_or_failure_reason}") + flops_metrics = count_flops(model, lambda: adapter.get_dummy_inputs(dataset_cfg, device), device) + except ProfilingUnavailable as exc: + flops_metrics = { + "flops": None, + "flops_g": None, + "flops_input_shape": None, + "flops_library": None, + "flops_error": str(exc), + } + profiling_errors.append(str(exc)) + + warmup_batches = min(2, len(loader)) + timed_images = 0 + model_time = 0.0 + end_to_end_start = time.perf_counter() + mean_a = dataset_cfg.get("mean_a", [0.485, 0.456, 0.406]) + std_a = dataset_cfg.get("std_a", [0.229, 0.224, 0.225]) + mean_b = dataset_cfg.get("mean_b", mean_a) + std_b = dataset_cfg.get("std_b", std_a) + + with torch.inference_mode(), GpuProfiler(device=device, required=False) as gpu_profiler: + for batch_idx, batch in enumerate(loader): + if device.type == "cuda": + torch.cuda.synchronize(device) + start = time.perf_counter() + raw_output = adapter.forward(model, batch, device) + if device.type == "cuda": + torch.cuda.synchronize(device) + elapsed = time.perf_counter() - start + a, b, mask, names = batch + normalized = adapter.normalize_output(raw_output, batch, dataset_cfg) + metrics.update(normalized.metric_tensor, mask) + boundary.update(normalized.binary, mask) + if batch_idx >= warmup_batches: + model_time += elapsed + timed_images += int(a.shape[0]) + + for i, sample_id in enumerate(names): + clean_id = safe_sample_id(str(sample_id)) + pred_i = normalized.binary[i] + save_binary_prediction(pred_i, pred_dir / f"{clean_id}_pred.png") + prob_i = normalized.score[i] if normalized.score is not None else None + if prob_i is not None: + save_probability_map(prob_i, prob_dir / f"{clean_id}_prob.png") + if str(sample_id) in selected: + rank = rank_for_sample(manifest, str(sample_id)) + a_vis = denormalize(a[i].detach().cpu(), mean_a, std_a) + b_vis = denormalize(b[i].detach().cpu(), mean_b, std_b) + save_visual_panel( + a_vis, + b_vis, + mask[i], + pred_i, + visual_dir / f"{rank:02d}_{clean_id}_panel.png", + prob=prob_i, + ) + end_to_end_time = time.perf_counter() - end_to_end_start + + split_metrics = metrics.compute() + split_metrics.update(boundary.compute()) + split_metrics.update(param_metrics) + split_metrics.update(flops_metrics) + split_metrics.update(gpu_profiler.summary()) + if split_metrics.get("gpu_profiling_error"): + profiling_errors.append(str(split_metrics["gpu_profiling_error"])) + fps_model_only = timed_images / model_time if model_time > 0 else None + fps_end_to_end = len(ds_for_loader) / end_to_end_time if end_to_end_time > 0 else None + status = "complete" if not (strict_profiling and profiling_errors) else "incomplete" + split_metrics.update({ + "model": model_name, + "dataset": dataset_name, + "split": "test", + "checkpoint": str(checkpoint_path), + "threshold": threshold, + "fps": fps_model_only, + "fps_model_only": fps_model_only, + "fps_end_to_end": fps_end_to_end, + "num_timed_images": timed_images, + "warmup_batches": warmup_batches, + "timing_device": str(device), + "test_num_samples": len(ds_for_loader), + "visual_sample_manifest": str(ROOT / "results" / "qualitative_samples" / dataset_name / "sample_manifest.json"), + "prediction_dir": str(pred_dir), + "visual_dir": str(visual_dir), + "timestamp": datetime.now(timezone.utc).isoformat(), + "status": status, + "profiling_errors": profiling_errors, + "adapter": { + "model_class_path": adapter.model_class_path, + "input_format": adapter.input_format, + "output_format": adapter.output_format, + "checkpoint_format": adapter.checkpoint_format, + "final_output_for_metrics": adapter.final_output_for_metrics, + "notes": adapter.notes_or_failure_reason, + }, + }) + save_metrics(model_name, dataset_name, "test", split_metrics) + append_to_comparison_table() + return split_metrics, 0 if status == "complete" else 1 diff --git a/utils/unified_trainer.py b/utils/unified_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..e3f43c9921ab5f01559cf0a9df6e46eea62e3871 --- /dev/null +++ b/utils/unified_trainer.py @@ -0,0 +1,697 @@ +from __future__ import annotations + +import csv +import json +import time +from copy import deepcopy +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +import torch +from torch.utils.data import DataLoader +from tqdm import tqdm + +from datasets.cd_dataset import CDDataset +from utils.config_loader import load_dataset_config, load_model_config +from utils.dataset_cache import apply_dataloader_cli_overrides, dataloader_kwargs, dataloader_policy_lines, dataset_runtime_summary, print_dataloader_policy +from utils.gpu_utils import print_gpu_diagnostics, resolve_gpu +from utils.metrics import BinaryMetrics, BoundaryMetrics, normalize_binary_prediction +from utils.model_adapters import BaseModelAdapter, get_model_adapter +from utils.profiling import GpuProfiler, ProfilingUnavailable, count_flops, count_parameters +from utils.qualitative import ( + denormalize, + manifest_ids, + rank_for_sample, + safe_sample_id, + save_binary_prediction, + save_probability_map, + save_visual_panel, + select_or_load_manifest, +) +from utils.results_writer import append_to_comparison_table, save_metrics + + +ROOT = Path(__file__).resolve().parents[1] + + +def _json_dump(path: Path, payload: Any) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8") as f: + json.dump(payload, f, indent=2, sort_keys=True) + + +def _append_jsonl(path: Path, payload: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("a", encoding="utf-8") as f: + f.write(json.dumps(payload, sort_keys=True) + "\n") + + +def _threshold_sweep_enabled(dataset_cfg: dict, split: str) -> bool: + eval_cfg = dataset_cfg.get("eval", {}) + if split == "val": + return bool(eval_cfg.get("sweep_val_threshold", True)) + if split == "test": + return bool(eval_cfg.get("sweep_test_threshold", False)) + return bool(eval_cfg.get("sweep_threshold", True)) + + +def _thresholds(dataset_cfg: dict, split: str) -> list[float]: + eval_cfg = dataset_cfg.get("eval", {}) + if not _threshold_sweep_enabled(dataset_cfg, split): + return [float(eval_cfg.get("threshold", 0.5))] + if "thresholds" in eval_cfg: + return [float(x) for x in eval_cfg["thresholds"]] + start = float(eval_cfg.get("threshold_min", 0.05)) + stop = float(eval_cfg.get("threshold_max", 0.95)) + step = float(eval_cfg.get("threshold_step", 0.05)) + values = [] + current = start + while current <= stop + 1e-9: + values.append(round(current, 4)) + current += step + return values + + +def _cfg_with_threshold(dataset_cfg: dict, threshold: float) -> dict: + cfg = deepcopy(dataset_cfg) + cfg.setdefault("eval", {})["threshold"] = float(threshold) + return cfg + + +def _last_tensor(raw_output: Any) -> torch.Tensor | None: + value = raw_output[-1] if isinstance(raw_output, (list, tuple)) and raw_output else raw_output + return value if torch.is_tensor(value) else None + + +def _has_nonfinite_tensor(value: Any) -> bool: + if torch.is_tensor(value): + return not bool(torch.isfinite(value.detach()).all().item()) + if isinstance(value, (list, tuple)): + return any(_has_nonfinite_tensor(item) for item in value) + if isinstance(value, dict): + return any(_has_nonfinite_tensor(item) for item in value.values()) + return False + + +def _batch_stats(raw_output: Any, normalized, mask: torch.Tensor) -> dict[str, float | None]: + binary = normalized.binary.float() + target = (mask > 0).float() + score = normalized.score + raw_tensor = _last_tensor(raw_output) + return { + "gt_positive_ratio": float(target.mean().item()), + "pred_positive_ratio": float(binary.mean().item()), + "mean_prob": float(score.mean().item()) if score is not None else None, + "logit_mean": float(raw_tensor.detach().float().mean().item()) if raw_tensor is not None else None, + "logit_std": float(raw_tensor.detach().float().std().item()) if raw_tensor is not None and raw_tensor.numel() > 1 else None, + } + + +def _loader(dataset_cfg: dict, split: str, batch_size: int, shuffle: bool, drop_last: bool = False) -> DataLoader: + ds = CDDataset(dataset_cfg["data_root"], split, cfg=dataset_cfg, return_format="tuple") + return DataLoader( + ds, + batch_size=batch_size, + shuffle=shuffle, + **dataloader_kwargs(dataset_cfg, torch.cuda.is_available()), + drop_last=drop_last, + ) + + +def _write_trajectory(metrics_dir: Path, trajectory: list[dict]) -> None: + _json_dump(metrics_dir / "trajectory.json", trajectory) + if not trajectory: + return + columns = [ + "epoch", + "train_loss", + "val_threshold", + "val_f1", + "val_iou", + "val_miou", + "val_precision", + "val_recall", + "val_oa", + "val_bf1", + "val_gt_positive_ratio", + "val_pred_positive_ratio", + "val_mean_prob", + "diagnostic_test_f1", + "diagnostic_test_iou", + "val_test_f1_gap", + "val_test_iou_gap", + "best_val_f1_so_far", + "best_val_epoch_or_iter_so_far", + "checkpoint_path", + ] + with (metrics_dir / "trajectory.csv").open("w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=columns, extrasaction="ignore") + writer.writeheader() + writer.writerows(trajectory) + + +def _evaluate_split( + *, + model_name: str, + model: torch.nn.Module, + adapter: BaseModelAdapter, + dataset_cfg: dict, + loader: DataLoader, + device: torch.device, + threshold: float, + split: str, + out_dir: Path, + save_outputs: bool, + checkpoint_path: Path | None = None, +) -> dict: + cfg = _cfg_with_threshold(dataset_cfg, threshold) + boundary = BoundaryMetrics(tolerance=int(dataset_cfg.get("eval", {}).get("boundary_tolerance", 2))) + metrics = BinaryMetrics(threshold=threshold) + pred_dir = out_dir / "predictions" / split + prob_dir = out_dir / "predictions" / f"{split}_prob" + visual_dir = out_dir / "visuals" / "selected_20" + manifest = select_or_load_manifest(dataset_cfg) if save_outputs and split == "test" else {"samples": []} + selected = manifest_ids(manifest) + mean_a = dataset_cfg.get("mean_a", [0.485, 0.456, 0.406]) + std_a = dataset_cfg.get("std_a", [0.229, 0.224, 0.225]) + mean_b = dataset_cfg.get("mean_b", mean_a) + std_b = dataset_cfg.get("std_b", std_a) + + n_samples = 0 + model_time = 0.0 + end_to_end_start = time.perf_counter() + stat_sums = {"gt_positive_ratio": 0.0, "pred_positive_ratio": 0.0, "mean_prob": 0.0} + stat_counts = {"mean_prob": 0} + + model.eval() + with torch.inference_mode(), GpuProfiler(device=device, required=False) as gpu_profiler: + for batch in loader: + if device.type == "cuda": + torch.cuda.synchronize(device) + start = time.perf_counter() + raw = adapter.forward(model, batch, device) + if device.type == "cuda": + torch.cuda.synchronize(device) + elapsed = time.perf_counter() - start + a, b, mask, names = batch + normalized = adapter.normalize_output(raw, batch, cfg) + metrics.update(normalized.metric_tensor, mask) + boundary.update(normalized.binary, mask) + n_samples += int(mask.shape[0]) + model_time += elapsed + stats = _batch_stats(raw, normalized, mask) + stat_sums["gt_positive_ratio"] += float(stats["gt_positive_ratio"] or 0.0) * int(mask.shape[0]) + stat_sums["pred_positive_ratio"] += float(stats["pred_positive_ratio"] or 0.0) * int(mask.shape[0]) + if stats["mean_prob"] is not None: + stat_sums["mean_prob"] += float(stats["mean_prob"]) * int(mask.shape[0]) + stat_counts["mean_prob"] += int(mask.shape[0]) + + if save_outputs: + for i, sample_id in enumerate(names): + clean_id = safe_sample_id(str(sample_id)) + pred_i = normalized.binary[i] + save_binary_prediction(pred_i, pred_dir / f"{clean_id}_pred.png") + prob_i = normalized.score[i] if normalized.score is not None else None + if prob_i is not None: + save_probability_map(prob_i, prob_dir / f"{clean_id}_prob.png") + if split == "test" and str(sample_id) in selected: + rank = rank_for_sample(manifest, str(sample_id)) + save_visual_panel( + denormalize(a[i].detach().cpu(), mean_a, std_a), + denormalize(b[i].detach().cpu(), mean_b, std_b), + mask[i], + pred_i, + visual_dir / f"{rank:02d}_{clean_id}_panel.png", + prob=prob_i, + ) + + elapsed_total = time.perf_counter() - end_to_end_start + result = metrics.compute() + result.update(boundary.compute()) + result.update(gpu_profiler.summary()) + result.update({ + "model": model_name, + "dataset": dataset_cfg["name"], + "split": split, + "threshold": threshold, + "threshold_mode": adapter.get_threshold_mode(), + "gt_positive_ratio": stat_sums["gt_positive_ratio"] / max(n_samples, 1), + "pred_positive_ratio": stat_sums["pred_positive_ratio"] / max(n_samples, 1), + "mean_prob": stat_sums["mean_prob"] / stat_counts["mean_prob"] if stat_counts["mean_prob"] else None, + "fps": n_samples / model_time if model_time > 0 else None, + "fps_model_only": n_samples / model_time if model_time > 0 else None, + "fps_end_to_end": n_samples / elapsed_total if elapsed_total > 0 else None, + "sample_count": n_samples, + "test_sample_count": n_samples if split == "test" else None, + "checkpoint": str(checkpoint_path) if checkpoint_path else None, + "timestamp": datetime.now(timezone.utc).isoformat(), + "status": "complete", + }) + return result + + +def _metrics_from_cached_predictions( + *, + model_name: str, + dataset_cfg: dict, + cached: list[tuple[torch.Tensor, torch.Tensor]], + threshold: float, + split: str, + n_samples: int, + model_time: float, + elapsed_total: float, + gpu_summary: dict[str, object], +) -> dict: + metrics = BinaryMetrics(threshold=threshold) + boundary = BoundaryMetrics(tolerance=int(dataset_cfg.get("eval", {}).get("boundary_tolerance", 2))) + stat_sums = {"gt_positive_ratio": 0.0, "pred_positive_ratio": 0.0, "mean_prob": 0.0} + stat_counts = {"mean_prob": 0} + + for metric_tensor, mask in cached: + binary, score = normalize_binary_prediction(metric_tensor, threshold=threshold) + metrics.update(metric_tensor, mask) + boundary.update(binary, mask) + batch_size = int(mask.shape[0]) + target = (mask > 0).float() + stat_sums["gt_positive_ratio"] += float(target.mean().item()) * batch_size + stat_sums["pred_positive_ratio"] += float(binary.float().mean().item()) * batch_size + if score is not None: + stat_sums["mean_prob"] += float(score.float().mean().item()) * batch_size + stat_counts["mean_prob"] += batch_size + + result = metrics.compute() + result.update(boundary.compute()) + result.update(gpu_summary) + result.update({ + "model": model_name, + "dataset": dataset_cfg["name"], + "split": split, + "threshold": threshold, + "threshold_mode": "threshold", + "gt_positive_ratio": stat_sums["gt_positive_ratio"] / max(n_samples, 1), + "pred_positive_ratio": stat_sums["pred_positive_ratio"] / max(n_samples, 1), + "mean_prob": stat_sums["mean_prob"] / stat_counts["mean_prob"] if stat_counts["mean_prob"] else None, + "fps": n_samples / model_time if model_time > 0 else None, + "fps_model_only": n_samples / model_time if model_time > 0 else None, + "fps_end_to_end": n_samples / elapsed_total if elapsed_total > 0 else None, + "sample_count": n_samples, + "test_sample_count": n_samples if split == "test" else None, + "checkpoint": None, + "timestamp": datetime.now(timezone.utc).isoformat(), + "status": "complete", + "sweep_cache": True, + }) + return result + + +def _threshold_sweep( + *, + model_name: str, + model: torch.nn.Module, + adapter: BaseModelAdapter, + dataset_cfg: dict, + loader: DataLoader, + device: torch.device, + split: str, + out_dir: Path, +) -> dict: + if adapter.get_threshold_mode() == "argmax": + threshold = adapter.get_threshold(dataset_cfg) + metrics = _evaluate_split( + model_name=model_name, + model=model, + adapter=adapter, + dataset_cfg=dataset_cfg, + loader=loader, + device=device, + threshold=threshold, + split=split, + out_dir=out_dir, + save_outputs=False, + ) + return { + "threshold_mode": "argmax", + "selected_threshold": threshold, + "best_f1": metrics["f1"], + "results": [metrics], + } + cached: list[tuple[torch.Tensor, torch.Tensor]] = [] + n_samples = 0 + model_time = 0.0 + end_to_end_start = time.perf_counter() + model.eval() + with torch.inference_mode(), GpuProfiler(device=device, required=False) as gpu_profiler: + for batch in loader: + if device.type == "cuda": + torch.cuda.synchronize(device) + start = time.perf_counter() + raw = adapter.forward(model, batch, device) + if device.type == "cuda": + torch.cuda.synchronize(device) + model_time += time.perf_counter() - start + _a, _b, mask, _names = batch + normalized = adapter.normalize_output(raw, batch, dataset_cfg) + cached.append((normalized.metric_tensor.detach().cpu(), mask.detach().cpu())) + n_samples += int(mask.shape[0]) + elapsed_total = time.perf_counter() - end_to_end_start + gpu_summary = gpu_profiler.summary() + + results = [] + best = None + for threshold in _thresholds(dataset_cfg, split): + metrics = _metrics_from_cached_predictions( + model_name=model_name, + dataset_cfg=dataset_cfg, + threshold=threshold, + split=split, + cached=cached, + n_samples=n_samples, + model_time=model_time, + elapsed_total=elapsed_total, + gpu_summary=gpu_summary, + ) + results.append(metrics) + if best is None or metrics["f1"] > best["f1"]: + best = metrics + assert best is not None + return { + "threshold_mode": "threshold", + "selected_threshold": float(best["threshold"]), + "best_f1": best["f1"], + "results": results, + } + + +def train_with_adapter(model_name: str, args) -> int: + dataset_cfg = load_dataset_config(args.dataset) + apply_dataloader_cli_overrides(dataset_cfg, args) + model_cfg = load_model_config(model_name) + if args.epochs is not None: + model_cfg["num_epochs"] = int(args.epochs) + if args.lr is not None: + model_cfg["lr"] = float(args.lr) + batch_size = int(args.batch_size or dataset_cfg.get("batch_size", 8)) + adapter = get_model_adapter(model_name) + if not adapter.supports_unified_training: + raise RuntimeError(f"{model_name} does not support unified training: {adapter.notes_or_failure_reason}") + + if args.dry_run: + print(f"[DRY-RUN] unified model={model_name} dataset={dataset_cfg['name']} root={dataset_cfg['data_root']}") + print(f"[DATASET] {dataset_runtime_summary(dataset_cfg)}") + print_dataloader_policy(dataset_cfg, torch.cuda.is_available()) + return 0 + + gpu_resolution = resolve_gpu(args.gpu) + print_gpu_diagnostics(gpu_resolution) + device = torch.device(gpu_resolution.local_device) + + out_dir = Path(args.output_dir) if args.output_dir else ROOT / "results" / model_name / dataset_cfg["name"] + if not out_dir.is_absolute(): + out_dir = ROOT / out_dir + ckpt_dir = out_dir / "checkpoints" + log_dir = out_dir / "logs" + metrics_dir = out_dir / "metrics" + for path in (ckpt_dir, log_dir, metrics_dir, out_dir / "predictions" / "test", out_dir / "visuals" / "selected_20"): + path.mkdir(parents=True, exist_ok=True) + + train_loader = _loader(dataset_cfg, "train", batch_size, shuffle=True, drop_last=True) + val_loader = _loader(dataset_cfg, "val", batch_size, shuffle=False) + test_loader = _loader(dataset_cfg, "test", batch_size, shuffle=False) + print_dataloader_policy(dataset_cfg, torch.cuda.is_available()) + model = adapter.build_model(model_cfg, dataset_cfg, device) + optimizer = adapter.build_optimizer(model, model_cfg) + scheduler = adapter.build_scheduler(optimizer, model_cfg) + amp_enabled = device.type == "cuda" and bool(getattr(adapter, "supports_amp_training", True)) + scaler = torch.amp.GradScaler("cuda", enabled=amp_enabled) + epochs = int(model_cfg.get("num_epochs", 200)) + best_path = ckpt_dir / "best_model.pth" + latest_path = ckpt_dir / "latest.pth" + best_f1 = -1.0 + best_epoch = 0 + selected_threshold = adapter.get_threshold(dataset_cfg) + trajectory: list[dict] = [] + train_history: list[dict] = [] + val_history: list[dict] = [] + start_epoch = 1 + + if args.resume and latest_path.exists(): + checkpoint = torch.load(latest_path, map_location=device) + adapter.load_checkpoint(model, latest_path, device) + if isinstance(checkpoint, dict) and "optimizer_state_dict" in checkpoint: + optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + if scheduler is not None and isinstance(checkpoint, dict) and "scheduler_state_dict" in checkpoint: + scheduler.load_state_dict(checkpoint["scheduler_state_dict"]) + start_epoch = int(checkpoint.get("epoch", 0)) + 1 + best_f1 = float(checkpoint.get("best_val_f1", best_f1)) + best_epoch = int(checkpoint.get("best_epoch", 0)) + selected_threshold = float(checkpoint.get("selected_threshold", selected_threshold)) + + if args.eval_only: + adapter.load_checkpoint(model, best_path, device) + test_metrics = _evaluate_split( + model_name=model_name, + model=model, + adapter=adapter, + dataset_cfg=dataset_cfg, + loader=test_loader, + device=device, + threshold=selected_threshold, + split="test", + out_dir=out_dir, + save_outputs=True, + checkpoint_path=best_path, + ) + save_metrics(model_name, dataset_cfg["name"], "test", test_metrics) + _json_dump(metrics_dir / "test_metrics.json", test_metrics) + append_to_comparison_table() + return 0 + + train_log = log_dir / "train.log" + with train_log.open("w", encoding="utf-8") as log: + log.write(f"# Unified Mamba-CD-style training: {model_name}/{dataset_cfg['name']}\n") + log.write(f"# Dataset: {dataset_runtime_summary(dataset_cfg)}\n") + for line in dataloader_policy_lines(dataset_cfg, torch.cuda.is_available()): + log.write(line + "\n") + if dataset_cfg.get("io_warning"): + print(f"[DATASET-WARNING] {dataset_cfg['io_warning']}") + log.write(f"# Warning: {dataset_cfg['io_warning']}\n") + for epoch in range(start_epoch, epochs + 1): + model.train() + total_loss = 0.0 + component_sums: dict[str, float] = {} + batch_count = 0 + progress = tqdm(train_loader, desc=f"{model_name} epoch {epoch}/{epochs}", dynamic_ncols=True) + for batch_idx, batch in enumerate(progress, start=1): + optimizer.zero_grad(set_to_none=True) + with torch.amp.autocast("cuda", enabled=amp_enabled): + raw = adapter.forward(model, batch, device) + if _has_nonfinite_tensor(raw): + raise RuntimeError( + f"{model_name}/{dataset_cfg['name']} produced non-finite model output " + f"at epoch {epoch}, batch {batch_idx}." + ) + with torch.amp.autocast("cuda", enabled=False): + loss_dict = adapter.compute_loss(raw, batch, model_cfg, dataset_cfg, device) + loss = loss_dict["loss"] + if not bool(torch.isfinite(loss.detach()).all().item()): + components = { + key: float(value.detach().float().cpu().item()) + for key, value in loss_dict.items() + if torch.is_tensor(value) and value.numel() == 1 + } + raise RuntimeError( + f"{model_name}/{dataset_cfg['name']} produced non-finite loss " + f"at epoch {epoch}, batch {batch_idx}: {components}" + ) + scaler.scale(loss).backward() + clip = model_cfg.get("grad_clip", model_cfg.get("gradient_clip", None)) + if clip is not None: + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), float(clip)) + scaler.step(optimizer) + scaler.update() + total_loss += float(loss.detach().cpu().item()) + batch_count += 1 + for key, value in loss_dict.items(): + component_sums[key] = component_sums.get(key, 0.0) + float(value.detach().cpu().item()) + lr = optimizer.param_groups[0]["lr"] + gpu_mem = torch.cuda.max_memory_allocated(device) / (1024.0 ** 3) if device.type == "cuda" else 0.0 + progress.set_postfix(loss=f"{loss.item():.4f}", lr=f"{lr:.2e}", best_f1=f"{best_f1:.4f}", gpu_gb=f"{gpu_mem:.2f}") + if scheduler is not None: + scheduler.step() + + train_loss = total_loss / max(batch_count, 1) + train_row = { + "epoch": epoch, + "train_loss": train_loss, + "lr": optimizer.param_groups[0]["lr"], + "timestamp": datetime.now(timezone.utc).isoformat(), + } + for key, value in component_sums.items(): + train_row[key] = value / max(batch_count, 1) + train_history.append(train_row) + + sweep = _threshold_sweep( + model_name=model_name, + model=model, + adapter=adapter, + dataset_cfg=dataset_cfg, + loader=val_loader, + device=device, + split="val", + out_dir=out_dir, + ) + selected_threshold = float(sweep["selected_threshold"]) + _json_dump(metrics_dir / f"val_threshold_sweep_epoch_{epoch}.json", sweep) + val_metrics = dict(max(sweep["results"], key=lambda row: row.get("f1", -1.0))) + val_metrics["checkpoint"] = str(latest_path) + is_best = val_metrics["f1"] > best_f1 + if is_best: + best_f1 = float(val_metrics["f1"]) + best_epoch = epoch + metadata = { + "model": model_name, + "dataset": dataset_cfg["name"], + "epoch": epoch, + "best_epoch": best_epoch, + "best_val_f1": best_f1, + "selected_threshold": selected_threshold, + "threshold_mode": adapter.get_threshold_mode(), + "timestamp": datetime.now(timezone.utc).isoformat(), + } + adapter.save_checkpoint(model, optimizer, scheduler, latest_path, metadata) + if is_best: + adapter.save_checkpoint(model, optimizer, scheduler, best_path, metadata) + val_payload = dict(val_metrics) + val_payload.update({"epoch": epoch, "best_epoch": best_epoch, "selected_threshold": selected_threshold}) + val_history.append(val_payload) + save_metrics(model_name, dataset_cfg["name"], "val", val_payload) + _json_dump(metrics_dir / "val_history.json", val_history) + _json_dump(metrics_dir / "train_history.json", train_history) + trajectory_row = { + "epoch": epoch, + "train_loss": train_loss, + "val_threshold": selected_threshold, + "val_f1": val_payload["f1"], + "val_iou": val_payload["iou"], + "val_miou": val_payload["miou"], + "val_precision": val_payload["precision"], + "val_recall": val_payload["recall"], + "val_oa": val_payload["oa"], + "val_bf1": val_payload.get("bf1"), + "val_gt_positive_ratio": val_payload.get("gt_positive_ratio"), + "val_pred_positive_ratio": val_payload.get("pred_positive_ratio"), + "val_mean_prob": val_payload.get("mean_prob"), + "diagnostic_test_f1": None, + "diagnostic_test_iou": None, + "val_test_f1_gap": None, + "val_test_iou_gap": None, + "best_val_f1_so_far": best_f1, + "best_val_epoch_or_iter_so_far": best_epoch, + "checkpoint_path": str(best_path if is_best else latest_path), + } + trajectory.append(trajectory_row) + _write_trajectory(metrics_dir, trajectory) + warning = "" + if val_payload["recall"] == 0: + warning += " zero_recall" + if val_payload.get("pred_positive_ratio", 0) == 0: + warning += " all_background_prediction" + line = ( + f"[Epoch {epoch:03d}/{epochs:03d}] train_loss={train_loss:.4f} " + f"val_F1={val_payload['f1']:.4f} val_IoU={val_payload['iou']:.4f} " + f"val_Prec={val_payload['precision']:.4f} val_Rec={val_payload['recall']:.4f} " + f"threshold={selected_threshold} best_F1={best_f1:.4f}{' BEST' if is_best else ''}{warning}" + ) + print(line, flush=True) + log.write(line + "\n") + log.flush() + + adapter.load_checkpoint(model, best_path, device) + checkpoint = torch.load(best_path, map_location=device) + selected_threshold = float(checkpoint.get("selected_threshold", selected_threshold)) if isinstance(checkpoint, dict) else selected_threshold + test_metrics = _evaluate_split( + model_name=model_name, + model=model, + adapter=adapter, + dataset_cfg=dataset_cfg, + loader=test_loader, + device=device, + threshold=selected_threshold, + split="test", + out_dir=out_dir, + save_outputs=True, + checkpoint_path=best_path, + ) + test_metrics.update(count_parameters(model)) + try: + if not adapter.supports_flops: + raise ProfilingUnavailable(f"{model_name} adapter does not support FLOPs.") + test_metrics.update(count_flops(model, lambda: adapter.get_dummy_inputs(dataset_cfg, device), device)) + except ProfilingUnavailable as exc: + test_metrics.update({"flops": None, "flops_g": None, "flops_error": str(exc)}) + test_metrics.update({ + "best_epoch": best_epoch, + "selected_threshold": selected_threshold, + "checkpoint_path": str(best_path), + "official_selection_metric": "validation_f1", + }) + save_metrics(model_name, dataset_cfg["name"], "test", test_metrics) + _json_dump(metrics_dir / "test_metrics.json", test_metrics) + + if _threshold_sweep_enabled(dataset_cfg, "test"): + test_sweep = _threshold_sweep( + model_name=model_name, + model=model, + adapter=adapter, + dataset_cfg=dataset_cfg, + loader=test_loader, + device=device, + split="test", + out_dir=out_dir, + ) + test_sweep.update({ + "diagnostic_only": True, + "warning": "Test threshold sweep is diagnostic only and was not used for model selection.", + "official_validation_selected_threshold": selected_threshold, + "official_test_metrics": test_metrics, + }) + else: + test_sweep = { + "diagnostic_only": True, + "skipped": True, + "reason": "Set eval.sweep_test_threshold: true in the dataset config to enable diagnostic test threshold sweeps.", + "official_validation_selected_threshold": selected_threshold, + "official_test_metrics": test_metrics, + } + _json_dump(metrics_dir / "test_threshold_sweep.json", test_sweep) + + best_val = max(val_history, key=lambda row: row.get("f1", -1.0)) if val_history else {} + overfit = { + "best_validation_epoch": best_epoch, + "best_validation_f1": best_f1, + "official_test_f1_at_validation_best_checkpoint": test_metrics.get("f1"), + "validation_test_f1_gap": (best_val.get("f1", 0.0) - test_metrics.get("f1", 0.0)) if best_val else None, + "validation_test_iou_gap": (best_val.get("iou", 0.0) - test_metrics.get("iou", 0.0)) if best_val else None, + "gap_warning": bool(best_val and (best_val.get("f1", 0.0) - test_metrics.get("f1", 0.0)) > 0.1), + "diagnostic_test_during_training_enabled": False, + "note": "Test metrics were not used for checkpoint selection.", + } + _json_dump(metrics_dir / "overfit_diagnostics.json", overfit) + if trajectory: + trajectory[-1]["val_test_f1_gap"] = overfit["validation_test_f1_gap"] + trajectory[-1]["val_test_iou_gap"] = overfit["validation_test_iou_gap"] + _write_trajectory(metrics_dir, trajectory) + append_to_comparison_table() + _append_jsonl(ROOT / "results" / "training_log.jsonl", { + "model": model_name, + "dataset": dataset_cfg["name"], + "status": "complete", + "best_epoch": best_epoch, + "best_val_f1": best_f1, + "test_f1": test_metrics.get("f1"), + "timestamp": datetime.now(timezone.utc).isoformat(), + "trainer": "unified", + }) + return 0 diff --git a/utils/weight_downloader.py b/utils/weight_downloader.py new file mode 100644 index 0000000000000000000000000000000000000000..861af5c1998f8ba2beb2bb827e55cbab8101202d --- /dev/null +++ b/utils/weight_downloader.py @@ -0,0 +1,258 @@ +from __future__ import annotations + +import argparse +import hashlib +import importlib +import json +import subprocess +import sys +import urllib.error +import urllib.request +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] + +WEIGHT_REGISTRY = { + "vmamba_tiny": { + "filename": "vssmtiny_dp01_ckpt_epoch_292.pth", + "zenodo_record": "14037770", + "zenodo_url": "https://zenodo.org/records/14037770/files/vssmtiny_dp01_ckpt_epoch_292.pth", + "gdrive_id": "160PXughGMNZ1GyByspLFS68sfUdrQE2N", + "local_dir": "model_repos/ChangeMamba/pretrained_weight", + "sha256": None, + }, + "vmamba_small": { + "filename": "vssmsmall_dp03_ckpt_epoch_238.pth", + "zenodo_record": "14037770", + "zenodo_url": "https://zenodo.org/records/14037770/files/vssmsmall_dp03_ckpt_epoch_238.pth", + "gdrive_id": "1dxHtFEgeJ9KL5WiLlvQOZK5jSEEd2Nmz", + "local_dir": "model_repos/ChangeMamba/pretrained_weight", + "sha256": None, + }, + "vmamba_base": { + "filename": "vssmbase_dp06_ckpt_epoch_241.pth", + "zenodo_record": "14037770", + "zenodo_url": "https://zenodo.org/records/14037770/files/vssmbase_dp06_ckpt_epoch_241.pth", + "gdrive_id": "1kUHSBDoFvFG58EmwWurdSVZd8gyKWYfr", + "local_dir": "model_repos/ChangeMamba/pretrained_weight", + "sha256": None, + }, +} + +TIMM_MODEL_ALIASES = { + "efficientnet_b4": ("efficientnet_b4", "tf_efficientnet_b4", "tf_efficientnet_b4_ns"), + "mit_b0": ("mit_b0", "segformer_b0"), + "mit_b1": ("mit_b1", "segformer_b1"), +} + +TORCHVISION_WEIGHT_ENUMS = { + "resnet18": "ResNet18_Weights", + "resnet50": "ResNet50_Weights", + "vgg16": "VGG16_Weights", +} + + +def _log_download(row: dict) -> None: + path = ROOT / "results" / "download_log.jsonl" + path.parent.mkdir(parents=True, exist_ok=True) + payload = {"timestamp_utc": datetime.now(timezone.utc).isoformat(), **row} + with path.open("a", encoding="utf-8") as f: + f.write(json.dumps(payload, sort_keys=True) + "\n") + + +def _sha256(filepath: str | Path) -> str: + h = hashlib.sha256() + with Path(filepath).open("rb") as f: + for block in iter(lambda: f.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def _manifest_path(local_dir: Path) -> Path: + return local_dir / "weights_manifest.json" + + +def _load_manifest(local_dir: Path) -> dict: + path = _manifest_path(local_dir) + if not path.exists(): + return {} + with path.open("r", encoding="utf-8") as f: + return json.load(f) + + +def _save_manifest(local_dir: Path, manifest: dict) -> None: + with _manifest_path(local_dir).open("w", encoding="utf-8") as f: + json.dump(manifest, f, indent=2, sort_keys=True) + + +def _ensure_module(module: str, package: str | None = None): + try: + return importlib.import_module(module) + except ImportError: + subprocess.run([sys.executable, "-m", "pip", "install", package or module], check=True) + return importlib.import_module(module) + + +def ensure_weights(weight_key: str, verbose: bool = True) -> str: + if weight_key not in WEIGHT_REGISTRY: + raise KeyError(f"Unknown weight key {weight_key!r}. Known keys: {sorted(WEIGHT_REGISTRY)}") + spec = WEIGHT_REGISTRY[weight_key] + local_dir = ROOT / spec["local_dir"] + local_dir.mkdir(parents=True, exist_ok=True) + dest = local_dir / spec["filename"] + manifest = _load_manifest(local_dir) + + if dest.exists(): + expected = spec.get("sha256") or manifest.get(spec["filename"], {}).get("sha256") + if not _verify_sha256(str(dest), expected): + raise RuntimeError(f"SHA256 mismatch for existing weight: {dest}") + if verbose: + print(f"[WeightDownloader] Present: {dest}") + return str(dest.resolve()) + + ok = _download_from_zenodo(spec["zenodo_url"], str(dest)) + if not ok: + ok = _download_from_gdrive(spec["gdrive_id"], str(dest)) + if not ok or not dest.exists(): + raise RuntimeError(f"Failed to download {weight_key} to {dest}") + + digest = _sha256(dest) + manifest[spec["filename"]] = {"sha256": digest, "weight_key": weight_key} + _save_manifest(local_dir, manifest) + print(f"[WeightDownloader] Downloaded {dest}") + print(f"[WeightDownloader] SHA256 {digest}") + return str(dest.resolve()) + + +def _download_from_zenodo(url: str, dest_path: str) -> bool: + try: + tqdm = _ensure_module("tqdm").tqdm + with urllib.request.urlopen(url) as response: + total = int(response.headers.get("Content-Length", "0")) + if getattr(response, "status", 200) != 200: + _log_download({"method": "zenodo", "url": url, "success": False, "status": response.status}) + return False + with open(dest_path, "wb") as f, tqdm(total=total, unit="B", unit_scale=True, desc=Path(dest_path).name) as bar: + while True: + chunk = response.read(1024 * 1024) + if not chunk: + break + f.write(chunk) + bar.update(len(chunk)) + _log_download({"method": "zenodo", "url": url, "success": True, "file_size": Path(dest_path).stat().st_size}) + return True + except (urllib.error.URLError, OSError, subprocess.CalledProcessError) as exc: + _log_download({"method": "zenodo", "url": url, "success": False, "error": str(exc)}) + return False + + +def _download_from_gdrive(file_id: str, dest_path: str) -> bool: + try: + gdown = _ensure_module("gdown") + url = f"https://drive.google.com/uc?id={file_id}" + result = gdown.download(url, dest_path, quiet=False) + ok = result is not None and Path(dest_path).exists() + _log_download({"method": "gdrive", "file_id": file_id, "success": ok, "file_size": Path(dest_path).stat().st_size if ok else 0}) + return ok + except (OSError, subprocess.CalledProcessError) as exc: + _log_download({"method": "gdrive", "file_id": file_id, "success": False, "error": str(exc)}) + return False + + +def _verify_sha256(filepath: str, expected: str | None) -> bool: + if expected is None: + return True + actual = _sha256(filepath) + if actual != expected: + print(f"[WeightDownloader] WARNING: SHA256 mismatch for {filepath}: expected {expected}, got {actual}") + return False + return True + + +def list_all_weights() -> None: + print("Weight Key | Filename | Local Path | Status") + print("--- | --- | --- | ---") + for key, spec in WEIGHT_REGISTRY.items(): + path = ROOT / spec["local_dir"] / spec["filename"] + print(f"{key} | {spec['filename']} | {path} | {'present' if path.exists() else 'missing'}") + + +def ensure_timm_weight(model_name: str, required: bool = True) -> bool: + timm = _ensure_module("timm") + print(f"[WeightDownloader] Ensuring timm weights for: {model_name}") + candidates = TIMM_MODEL_ALIASES.get(model_name, (model_name,)) + errors = [] + for candidate in candidates: + try: + model = timm.create_model(candidate, pretrained=True, num_classes=0) + del model + _log_download({"method": "timm", "model_name": model_name, "resolved_model": candidate, "success": True}) + print(f"[WeightDownloader] {model_name} weights ready via timm model {candidate}.") + return True + except RuntimeError as exc: + if "Unknown model" not in str(exc): + raise + errors.append(str(exc)) + + version = getattr(timm, "__version__", "unknown") + message = ( + f"timm {version} does not provide {model_name} " + f"(tried: {', '.join(candidates)})." + ) + _log_download({ + "method": "timm", + "model_name": model_name, + "success": False, + "required": required, + "error": "; ".join(errors) or message, + }) + if required: + raise RuntimeError(message) + print(f"[WeightDownloader] WARNING: {message} Skipping optional warmup.") + return False + + +def ensure_torchvision_weight(model_name: str) -> None: + tv_models = _ensure_module("torchvision.models", "torchvision") + print(f"[WeightDownloader] Ensuring torchvision weights for: {model_name}") + builder = getattr(tv_models, model_name) + enum_name = TORCHVISION_WEIGHT_ENUMS.get( + model_name, + "".join(part.capitalize() for part in model_name.split("_")) + "_Weights", + ) + weights_enum = getattr(tv_models, enum_name, None) + if weights_enum is None: + builder(pretrained=True) + _log_download({"method": "torchvision", "model_name": model_name, "success": True, "weights": "pretrained=True"}) + print(f"[WeightDownloader] {model_name} weights ready.") + return + weights = weights_enum.DEFAULT + builder(weights=weights) + _log_download({"method": "torchvision", "model_name": model_name, "success": True, "weights": str(weights)}) + print(f"[WeightDownloader] {model_name} weights ready.") + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--prefetch-all", action="store_true") + parser.add_argument("--list", action="store_true") + args = parser.parse_args() + if args.list: + list_all_weights() + if args.prefetch_all: + for key in WEIGHT_REGISTRY: + ensure_weights(key, verbose=True) + ensure_timm_weight("efficientnet_b4") + ensure_timm_weight("mit_b0", required=False) + ensure_timm_weight("mit_b1", required=False) + ensure_torchvision_weight("resnet18") + ensure_torchvision_weight("resnet50") + ensure_torchvision_weight("vgg16") + print("[WeightDownloader] All weights prefetched successfully.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main())