From 2a7d4e5ef2b83edac92d2aec7c3a7e18d1fe3800 Mon Sep 17 00:00:00 2001 From: bw4sz Date: Thu, 14 May 2026 08:35:54 -0700 Subject: [PATCH] Add OSBS mortality comparison pipeline. Squash the local feature branch into a clean PR branch without .env or lightning_logs artifacts. --- .github/workflows/pytest.yml | 39 +- .gitignore | 16 +- README.md | 268 +- SLURM/crown_plot_array.sh | 38 + SLURM/experiment.sh | 28 - SLURM/osbs_inference.sh | 30 + SLURM/osbs_mortality.sh | 30 + SLURM/train_experiment.sh | 69 + abundance.py | 16 +- config.smoke.example.yml | 30 + config.smoke.local.yml | 18 + config.yml | 97 +- create_prediction_shp.py | 11 +- data/external/.gitkeep | 0 data/interim/.gitkeep | 0 data/raw/README.md | 50 + deeptreeattention.egg-info/PKG-INFO | 216 + deeptreeattention.egg-info/SOURCES.txt | 56 + .../dependency_links.txt | 1 + deeptreeattention.egg-info/entry_points.txt | 6 + deeptreeattention.egg-info/requires.txt | 25 + deeptreeattention.egg-info/top_level.txt | 1 + .../unified_hierarchical_cnn_publication.png | Bin 0 -> 997105 bytes environment.yml | 4 +- experiments/.gitkeep | 0 mine.py | 53 +- notebooks/crop_random_tile.py | 33 +- notebooks/sample_multinomial.py | 10 +- predict.py | 165 +- pyproject.toml | 64 + requirements.txt | 4 +- results/.gitkeep | 0 sample_multinomial.py | 9 +- setup.py | 14 +- src/__init__.py | 1 + src/data.py | 176 +- src/experiment_tracking.py | 83 + src/generate.py | 178 +- src/local_smoke_logger.py | 62 + src/main.py | 110 +- src/megaplot.py | 5 +- src/models/__init__.py | 1 + src/models/dead.py | 78 +- src/models/metadata.py | 13 +- src/models/multi_stage.py | 950 +++-- src/multinomial.py | 35 +- src/neon_download.py | 88 + src/pipelines/__init__.py | 1 + src/pipelines/crown_one_plot.py | 40 + src/pipelines/download_neon_aop.py | 63 + src/pipelines/merge_crown_boxes.py | 34 + src/pipelines/osbs_inference.py | 240 ++ src/pipelines/osbs_mortality.py | 622 +++ src/pipelines/update_neon_vst.py | 113 + src/predict.py | 85 +- src/start_cluster.py | 101 - src/utils.py | 69 +- src/visualize.py | 2 + src/vst_update.py | 220 + tests/conftest.py | 27 +- tests/data/annotations.csv | 14 + tests/data/boxes/HARV_001_boxes.cpg | 1 + tests/data/boxes/HARV_001_boxes.dbf | Bin 0 -> 17780 bytes tests/data/boxes/HARV_001_boxes.shp | Bin 0 -> 7580 bytes tests/data/boxes/HARV_001_boxes.shx | Bin 0 -> 540 bytes tests/data/boxes/HARV_010_boxes.cpg | 1 + tests/data/boxes/HARV_010_boxes.dbf | Bin 0 -> 19052 bytes tests/data/boxes/HARV_010_boxes.shp | Bin 0 -> 8124 bytes tests/data/boxes/HARV_010_boxes.shx | Bin 0 -> 572 bytes tests/data/canopy_points.cpg | 1 + tests/data/canopy_points.dbf | Bin 0 -> 53013 bytes tests/data/canopy_points.shp | Bin 0 -> 520 bytes tests/data/canopy_points.shx | Bin 0 -> 220 bytes tests/data/novel_species.csv | 3 + tests/data/test.csv | 7 + tests/data/train.csv | 6 + tests/test_dead.py | 3 - tests/test_generate.py | 46 +- tests/test_osbs_inference.py | 48 + tests/test_osbs_mortality.py | 111 + tests/test_predict.py | 6 +- train.py | 403 +- train_dead.py | 29 +- uv.lock | 3605 +++++++++++++++++ 84 files changed, 7632 insertions(+), 1450 deletions(-) create mode 100644 SLURM/crown_plot_array.sh delete mode 100644 SLURM/experiment.sh create mode 100755 SLURM/osbs_inference.sh create mode 100644 SLURM/osbs_mortality.sh create mode 100755 SLURM/train_experiment.sh create mode 100644 config.smoke.example.yml create mode 100644 config.smoke.local.yml create mode 100644 data/external/.gitkeep create mode 100644 data/interim/.gitkeep create mode 100644 data/raw/README.md create mode 100644 deeptreeattention.egg-info/PKG-INFO create mode 100644 deeptreeattention.egg-info/SOURCES.txt create mode 100644 deeptreeattention.egg-info/dependency_links.txt create mode 100644 deeptreeattention.egg-info/entry_points.txt create mode 100644 deeptreeattention.egg-info/requires.txt create mode 100644 deeptreeattention.egg-info/top_level.txt create mode 100644 docs/figures/unified_hierarchical_cnn_publication.png create mode 100644 experiments/.gitkeep create mode 100644 pyproject.toml create mode 100644 results/.gitkeep create mode 100644 src/experiment_tracking.py create mode 100644 src/local_smoke_logger.py create mode 100644 src/neon_download.py create mode 100644 src/pipelines/__init__.py create mode 100644 src/pipelines/crown_one_plot.py create mode 100644 src/pipelines/download_neon_aop.py create mode 100644 src/pipelines/merge_crown_boxes.py create mode 100644 src/pipelines/osbs_inference.py create mode 100644 src/pipelines/osbs_mortality.py create mode 100644 src/pipelines/update_neon_vst.py delete mode 100644 src/start_cluster.py create mode 100644 src/vst_update.py create mode 100644 tests/data/annotations.csv create mode 100644 tests/data/boxes/HARV_001_boxes.cpg create mode 100644 tests/data/boxes/HARV_001_boxes.dbf create mode 100644 tests/data/boxes/HARV_001_boxes.shp create mode 100644 tests/data/boxes/HARV_001_boxes.shx create mode 100644 tests/data/boxes/HARV_010_boxes.cpg create mode 100644 tests/data/boxes/HARV_010_boxes.dbf create mode 100644 tests/data/boxes/HARV_010_boxes.shp create mode 100644 tests/data/boxes/HARV_010_boxes.shx create mode 100644 tests/data/canopy_points.cpg create mode 100644 tests/data/canopy_points.dbf create mode 100644 tests/data/canopy_points.shp create mode 100644 tests/data/canopy_points.shx create mode 100644 tests/data/novel_species.csv create mode 100644 tests/data/test.csv create mode 100644 tests/data/train.csv create mode 100644 tests/test_osbs_inference.py create mode 100644 tests/test_osbs_mortality.py create mode 100644 uv.lock diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 368178615..d6799b6e0 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -13,43 +13,28 @@ jobs: fail-fast: false matrix: python-version: - # - 3.6 - - 3.9 - # - 3.8 + - "3.11" os: - "ubuntu-latest" runs-on: "${{ matrix.os }}" - # use bash everywhere - defaults: - run: - shell: "bash -l {0}" - steps: - name: "Checkout code" - uses: "actions/checkout@v2" + uses: "actions/checkout@v4" - - name: "Cache conda" - uses: "actions/cache@v1" - env: - # Increase this value to reset cache if env.yml has not changed - CACHE_NUMBER: 0 + - name: "Setup Python" + uses: "actions/setup-python@v5" with: - path: "~/conda_pkgs_dir" - key: "${{ matrix.os }}-conda-${{ matrix.python-version }}-${{ env.CACHE_NUMBER }}-${{ hashFiles('enviroment.yml') }}" + python-version: "${{ matrix.python-version }}" - - name: "Setup conda" - uses: "conda-incubator/setup-miniconda@v2" + - name: "Setup uv" + uses: "astral-sh/setup-uv@v6" with: - activate-environment: "DeepForest" - environment-file: "environment.yml" - python-version: "${{ matrix.python-version }}" - channels: conda-forge,spyder-ide - allow-softlinks: true - channel-priority: flexible - show-channel-urls: true - use-only-tar-bz2: true + enable-cache: true + + - name: "Install dependencies" + run: "uv sync --extra dev" - name: "Run tests" - run: "pytest -v" + run: "uv run pytest -v" diff --git a/.gitignore b/.gitignore index 11d3d6dc3..6e7a07578 100644 --- a/.gitignore +++ b/.gitignore @@ -1,11 +1,23 @@ +config.local.yml +.smoke_train_overrides.yml +results/** +!results/.gitkeep + .DS_Store project.wpr project.wpu *.h5 __pycache__ -data/raw/ -data/processed/ +# Large or machine-local artifacts (keep README / .gitkeep under data/*) +data/processed/** +data/external/** +!data/external/.gitkeep +!data/interim/.gitkeep +data/raw/**/*.csv +data/raw/**/*.zip +!data/raw/README.md *.tif *.png +!docs/figures/*.png *.wpr *.wpu diff --git a/README.md b/README.md index 562fe3093..b7a6c401e 100644 --- a/README.md +++ b/README.md @@ -1,201 +1,189 @@ -DeepTreeAttention -============================== +# DeepTreeAttention -[![Github Actions](https://github.com/Weecology/DeepTreeAttention/actions/workflows/pytest.yml/badge.svg)](https://github.com/Weecology/DeepTreeAttention/actions/) +[Github Actions](https://github.com/Weecology/DeepTreeAttention/actions/) -Tree Species Prediction for the National Ecological Observatory Network (NEON) +Tree species classification for **National Ecological Observatory Network (NEON)** imagery, implementing Hang et al. 2020 ([Hyperspectral Image Classification with Attention Aided CNNs](https://arxiv.org/abs/2005.11977)) with a PyTorch Lightning training stack. -Implementation of Hang et al. 2020 [Hyperspectral Image Classification with Attention Aided CNNs](https://arxiv.org/abs/2005.11977) for tree species prediction. +This README is organized around the lifecycle you described: **raw points → generated tensors → training → evaluation → inference → reporting**. Dask has been removed in favor of **single-node sequential code** plus **plain SLURM job arrays** for embarrassingly parallel stages (per-plot crowns, large I/O batches, and so on). -# Model Architecture +--- -![](www/model.png) +## 0. Environment (uv) -Project Organization ------------- - - ├── LICENSE - ├── README.md <- The top-level README for developers using this project. - ├── data - │   ├── processed <- The final, canonical data sets for modeling. - │   └── raw <- The original, immutable data dump. - │ - ├── environment.yml <- Conda requirements - │ - ├── setup.py <- makes project pip installable (pip install -e .) so src can be imported - ├── src <- Source code for use in this project. - │   ├── Models <- Model Architectures +```bash +uv sync --extra dev +uv run pytest -v +``` --------- +Use `uv run …` for every CLI invocation so the locked environment is respected. -# Workflow -There are three main parts to this project, a 1) data module, a 2) model module, and 3) a trainer module. Usually the data_module is created to hold the train and test split and keep track of data generation reproducibility. Then a model architecture is created and pass to the model module along with the data module. Finally the model module is passed to the trainer. +--- -``` -#1) -data_module = data.TreeData(csv_file="data/raw/neon_vst_data_2021.csv", regenerate=False, client=client) +## 1. Data layout and the “HPC handoff” -#2) -model = -m = main.TreeModel(model=model, bands=data_module.config["bands"], classes=data_module.num_classes,label_dict=data_module.species_label_dict) -#3 -trainer = Trainer() -trainer.fit(m, datamodule=data_module) -``` +| Stage | Location | What it is | +| ----------------------------------------- | ------------------------------------------------ | -------------------------------------------------------------------------------------------------- | +| **Upstream (not in git)** | e.g. `/orange/ewhite/NeonData/...` on HiPerGator | Full NEON mirror; too large to vendor. | +| **Shareable inputs** | `data/raw/` | VST (or similar) stem tables, AOI shapefiles. See `data/raw/README.md`. | +| **Intermediary bundle (commit or rsync)** | `data/interim/` | Filtered stem points (`canopy_points.shp`), optional per-plot crown boxes before merge, manifests. | +| **Heavy rasters** | `data/external/neon_aop/` | RGB / HSI / CHM tiles from `**neonutilities`** downloads or selective `rsync` from the mirror. | +| **Training tensors** | `data/processed//` or `config["data_dir"]` | HSI crops, `train.csv` / `test.csv`, `crowns.shp`. | -## Pytorch Lightning Data Module (data.TreeData) -This repo contains a pytorch lightning data module for reproducibility. The goal of the project is to make it easy to share with others within our research group, but we welcome contributions from outside the community. While all data is public, it is VERY large (>20TB) and cannot be easily shared. If you want to reproduce this work, you will need to download the majority of NEON's camera, HSI and CHM data and change the paths in the config file. For the 'raw' NEON tree stem data see data/raw/neon_vst_2021.csv. The data module starts from this state, which are x,y locations for each tree. It then performs the following actions as an end-to-end workflow. +**Practical handoff from HiPerGator:** export the VST CSV you trust, copy it to `data/raw/`, then either (a) run `deeptree-download-aop` (below) into `data/external/neon_aop/`, or (b) `rsync` only the DP3 products you need into the same tree. Point `rgb_sensor_pool`, `HSI_sensor_pool`, and `CHM_pool` in `config.yml` at that mirror. You no longer need to regenerate crops for every experiment if you set `use_data_commit` to a frozen directory name or reuse `data_dir` with `replace: false` after the first successful build. -1. Filters the data to represent trees over 3m with sufficient number of training samples -2. Extract the LiDAR derived canopy height and compares it to the field measured height. Trees that are below the canopy are excluded based on the min_CHM_diff parameter in the config. -3. Splits the training and test x,y data such that field plots are either in training or test. -4. For each x,y stem location the crown is predicted by the tree detection algorithm (DeepForest - https://deepforest.readthedocs.io/). -5. Crops of each tree crown are created and divided into pixel windows for pixel-level prediction. +--- -This workflow does not need to be run on every experiment. If you are satisifed with the current train/test split and data generation process, set regenerate=False +## 2. Download AOP tiles (`neonutilities`) -``` -data_module = data.TreeData(csv_file="data/raw/neon_vst_data_2021.csv", regenerate=False) -data_module.setup() -``` +The Python `**neonutilities`** package exposes `by_tile_aop`, equivalent to the R `neonUtilities::byTileAOP` helper. This repository wraps it for stem coordinates. -## Pytorch Lightning Training Module (data.TreeModel) +1. Obtain a NEON API token and export it (optional for tiny pulls; recommended otherwise): + ```bash + export NEON_API_TOKEN="your_token" + ``` +2. After you have `canopy_points.shp` (from the filtering stage in `TreeData`), set `data_layout.canopy_points_shp` in `config.yml` **or** pass `--points`. +3. Run: + ```bash + uv run deeptree-download-aop --config config.yml + ``` + Defaults live under `neon_download` in `config.yml` (site, years, DP3 IDs for RGB, hyperspectral, CHM, buffer in meters). -Training is handled by the TreeModel class which loads a model from the models folder, reads the config file and runs the training. The evaluation metrics and images are computed and put of the comet dashboard +--- -``` -m = main.TreeModel(model=Hang2020.vanilla_CNN, bands=data_module.config["bands"], classes=data_module.num_classes,label_dict=data_module.species_label_dict) - -trainer = Trainer( - gpus=data_module.config["gpus"], - fast_dev_run=data_module.config["fast_dev_run"], - max_epochs=data_module.config["epochs"], - accelerator=data_module.config["accelerator"], - logger=comet_logger) - -trainer.fit(m, datamodule=data_module) -``` +## 3. Generate datasets (0 — data generation) -## Alive/Dead Filtering +Pipeline code paths: -As part of the prediction pipeline, RGB crops are scored as either 'Alive', meanining they have leaves during presumed leaf-on season, or 'Dead', meaning they do not have leaves. -To finetune the resent50 model, see src/models/dead.py. The classified data for the Alive/Dead crops can be found in data/raw/dead_train and dead/raw/dead_test. +- `src/data.py` — filter VST, CHM checks, megaplot merge hooks, train/test split. +- `src/generate.py` — DeepForest crowns, hyperspectral crops, optional H5→TIF conversion via `src/neon_paths.py`. -### Dev Guide +**Local / single job:** instantiate `data.TreeData` with `use_data_commit: null` and your `config.yml`, as in `train.py`. -In general, major changes or improvements should be made on a new git branch. Only core improvements should be made on the main branch. If a change leads to higher scores, please create a pull request. Any pull requests are expected to have pytest unit tests (see tests/) that cover major use cases. +**Parallel crowns (SLURM):** each array task runs one plot: -## Model Architectures +```bash +uv run python -m src.pipelines.crown_one_plot \ + --canopy-points data/interim/canopy_points.shp \ + --plot OSBS_001 \ + --rgb-glob "data/external/neon_aop/**/DP3.30010.001/**/Camera/**/*.tif" \ + --savedir data/interim/boxes \ + --raw-box-savedir data/interim/raw_boxes +``` -The TreeModel class takes in a create model function +After all tasks finish, merge: -``` -m = main.TreeModel(model=Hang2020.vanilla_CNN) +```bash +uv run deeptree-merge-crown-boxes \ + --boxes-dir data/interim/boxes \ + --out data/interim/crowns.shp ``` -Any model can be specified provided it follows the following input and output arguments +Submit `SLURM/crown_plot_array.sh` (tune `#SBATCH` directives, `plots.txt`, and `REPO_ROOT`). -``` -class myModel(Module): - """ - Model description - """ - def __init__(self, bands, classes): - super(myModel, self).__init__() - - - def forward(self, x): - - class_scores = F.softmax(x) - - return class_scores -``` +> **Note:** Passing a Dask `Client` into `points_to_crowns`, `generate_crops`, or `train_test_split` now raises a clear error. Parallelize with SLURM (or your own outer loop), not an in-Python Dask cluster. -### Extending the model +--- -To create a model that takes in new inputs, I strongly recommend sub-classing the existing TreeData and TreeModel classes. For an example, see the MetadataModel in models/metadata.py +## 4. Training (1) -``` -#Subclass of the training model -class MetadataModel(main.TreeModel): - """Subclass the core model and update the training loop to take two inputs""" - def __init__(self, model, sites,classes, label_dict, config): - super(MetadataModel,self).__init__(model=model,classes=classes,label_dict=label_dict, config=config) - - def training_step(self, batch, batch_idx): - """Train on a loaded dataset - """ - #allow for empty data if data augmentation is generated - inputs, y = batch - images = inputs["HSI"] - metadata = inputs["site"] - y_hat = self.model.forward(images, metadata) - loss = F.cross_entropy(y_hat, y) - - return loss +**Experiment identity (Comet + SLURM)** — you no longer pass git branch/sha as required positionals. The trainer auto-detects git (branch, SHA, dirty flag) and uploads a merged `**config.merged.yml`**, optional `**git_diff_uncommitted.patch**`, and a `**src/**` code bundle to Comet when logging is enabled. + +```bash +# Human-readable Comet experiment name (recommended) +export DEEPTREE_EXPERIMENT_NAME=osbs-baseline-epoch70 +uv run python train.py --config config.yml --experiment-name "${DEEPTREE_EXPERIMENT_NAME}" + +# Or rely on auto naming (UTC timestamp + short SHA, or SLURM_JOB_ID on clusters) +uv run python train.py --config config.yml +# Optional YAML merged last (keep one file per ablation under e.g. experiments/) +uv run python train.py --config config.yml --overrides experiments/my_ablation.yml -n osbs-lr-sweep-001 ``` -## Getting Started (UF - collaboration) +Environment variables (highest priority first for the display name): `**DEEPTREE_EXPERIMENT_NAME**`, `**COMET_EXPERIMENT_NAME**`, then `**--experiment-name**`, then a default from `**SLURM_JOB_ID**` or timestamp. -This section is meant solely for members of the idtrees group who have access to the data. +`train.py` reads `config.yml` (+ `config.local.yml` if present), logs to Comet when `**use_comet: true**` and `**COMET_API_KEY**` / `**COMET_KEY**` are set, and writes checkpoints under `**checkpoint_dir**`. `raw_vst_csv` defaults to `data/raw/neon_vst_data_2022.csv` but can be overridden in YAML. -1) Fork this repo and install the conda environment. +**SLURM (queued GPU training)** — from the repo checkout, set **`EXPERIMENT_NAME`** (forwarded as **`DEEPTREE_EXPERIMENT_NAME`**), optionally **`DEEPTREE_OVERRIDES`** and **`DEEPTREE_CONFIG`**, then: -``` -conda env create -f=environment.yml -conda activate DeepTreeAttention +```bash +cd /path/to/DeepTreeAttention +EXPERIMENT_NAME=osbs-baseline sbatch SLURM/train_experiment.sh ``` -2) Update the config.yml +**`REPO_ROOT`** defaults to **`SLURM_SUBMIT_DIR`** (your shell’s current directory when you run `sbatch`), so you normally **do not** export it if you `cd` into the project first. Set **`REPO_ROOT=/path/to/DeepTreeAttention`** only when you submit from another directory. -Currently, only members of the ewhite group have permissions to the raw NEON data. +Edit `#SBATCH` lines in `SLURM/train_experiment.sh` for your partition/account. The job runs from a **shared checkout** so you can **`git pull`** or edit **`experiments/*.yml`** between submissions; each job still logs the **resolved config** and **git SHA** to Comet for apples-to-apples comparison. -For example: +--- -``` -rgb_sensor_pool: /orange/ewhite/NeonData/*/DP3.30010.001/**/Camera/**/*.tif -``` +## 5. Evaluation (2) -This is not a problem, just set +Evaluation is integrated in the Lightning `TreeModel` / `MultiStage` path (validation metrics, crown-level scoring). Point `use_data_commit` at a frozen processed directory to re-score without touching generation. -``` -regenerate: False -``` +--- -and it will bypass these steps and use the existing train/test split (e.g. data/processed/train.csv) +## 6. Inference (3) -You will need to set the correct crop directories +**OSBS RGB tile inference** is configured solely through `inference_osbs` in `config.yml`. +```bash +uv run python predict.py --config config.yml +# or +uv run deeptree-infer-osbs --config config.yml ``` -crop_dir: /blue/ewhite/b.weinstein/DeepTreeAttention/crops/ -``` -To wherever the crops are saved. This is currently -``` -/orange/idtrees-collab/DeepTreeAttention/crops/ -``` +HiPerGator template: `SLURM/osbs_inference.sh`. -I highly recommend making a comet login. Change +**OSBS mortality comparison** is configured through `osbs_mortality` in `config.yml`. It downloads AOI-intersecting RGB tiles when requested, runs the detector plus alive/dead cropmodel, optionally runs species inference, and writes tile-level CSV/GPKG outputs plus GeoTIFF rasters for dead counts and dead-count change. +```bash +uv run deeptree-osbs-mortality --config config.yml ``` -#Comet dashboard -comet_workspace: bw4sz -``` -to your usename and add a [.comet.config file](https://www.comet.ml/docs/python-sdk/advanced/#non-interactive-setup) to authenticate. -3) Submit a job +HiPerGator template: `SLURM/osbs_mortality.sh`. -Submit a SLURM job +--- +## 7. Reporting and analysis (4) + +- Comet dashboards (when `comet_ml` is configured). +- Scripts such as `abundance.py`, `create_prediction_shp.py`, and `src/multinomial.py` now use `ThreadPoolExecutor` instead of Dask for light parallelism over shapefiles. + +--- + +## Project map + +```text +├── config.yml # Central configuration (+ neon_download / data_layout) +├── data/ +│ ├── raw/README.md # What belongs in raw inputs + rsync hints +│ ├── external/ # Downloaded / rsync'd NEON tiles (.gitkeep only) +│ └── interim/ # Optional canonical intermediate artifacts +├── experiments/ # Optional YAML fragments for ``--overrides`` (one ablation per file) +├── SLURM/ # Job scripts (``train_experiment.sh``, inference, crown array) +├── src/ +│ ├── data.py # Lightning TreeData + filtering +│ ├── experiment_tracking.py # Git metadata + default experiment names +│ ├── generate.py # Crowns + crops +│ ├── neon_download.py # neonutilities helpers +│ └── pipelines/ # CLIs (inference, download, crown worker, merge) +├── train.py # Full training driver +├── predict.py # OSBS inference entry +└── pyproject.toml # Dependencies + `deeptree-*` console scripts ``` -sbatch SLURM/experiment.sh -``` -4) Look at the comet repo for results +--- + +## Open questions / follow-up work -The metrics tab has the Micro and Macro Accuracy. +1. **Megaplot + IFAS branches** — logic is dense; consider isolating into a small submodule with explicit tests. +2. **CHM product ID per NEON revision** — confirm `DP3.30015.001` matches your mirror layout (`CanopyHeightModelGtif`). +3. **Comet as the sole reproducibility anchor** — `use_data_commit` ties runs to Comet artifact IDs; consider replacing with explicit semantic version tags on `data/processed//`. +4. **Dead-tree filtering in `TreeData`** — references `self.predicted_dead` in a logging block; verify that attribute is always defined on your code path before relying on those images in Comet. +Training uses Lightning 2 `Trainer(accelerator=…, devices=…)` (see `devices` / `accelerator` in `config.yml`; legacy `gpus` is still read as a device count when `devices` is omitted). +Contributions: branch per feature, add/adjust pytest coverage for anything you touch, keep diffs focused. \ No newline at end of file diff --git a/SLURM/crown_plot_array.sh b/SLURM/crown_plot_array.sh new file mode 100644 index 000000000..8785c4672 --- /dev/null +++ b/SLURM/crown_plot_array.sh @@ -0,0 +1,38 @@ +#!/bin/bash +#SBATCH --job-name=dt_crown_plot +#SBATCH --account=ewhite +#SBATCH --cpus-per-task=2 +#SBATCH --mem=16G +#SBATCH --time=04:00:00 +#SBATCH --output=logs/crown_plot_%A_%a.out +#SBATCH --error=logs/crown_plot_%A_%a.err +##SBATCH --array=1-50%10 + +# One task per line in plots.txt (plotID). Example: +# awk 'NR>1{print $1}' plots_export.csv | sort -u > plots.txt # if first column is plotID +# Then: #SBATCH --array=1-$(wc -l < plots.txt)%10 + +set -euo pipefail +REPO_ROOT="${REPO_ROOT:-$HOME/DeepTreeAttention}" +export CONFIG_PATH="${CONFIG_PATH:-$REPO_ROOT/config.yml}" +CANOPY_POINTS="${CANOPY_POINTS:-$REPO_ROOT/data/interim/canopy_points.shp}" +PLOTS_FILE="${PLOTS_FILE:-$REPO_ROOT/plots.txt}" + +cd "$REPO_ROOT" +mkdir -p logs + +LINE_NO="${SLURM_ARRAY_TASK_ID:?Set SLURM_ARRAY_TASK_ID or submit with sbatch --array}" +PLOT="$(sed -n "${LINE_NO}p" "$PLOTS_FILE")" +if [[ -z "${PLOT}" ]]; then + echo "No plot on line ${LINE_NO} of ${PLOTS_FILE}" + exit 1 +fi + +RGB_GLOB="$(uv run python -c "import os; from src import utils; print(utils.read_config(os.environ['CONFIG_PATH'])['rgb_sensor_pool'])")" + +uv run python -m src.pipelines.crown_one_plot \ + --canopy-points "$CANOPY_POINTS" \ + --plot "$PLOT" \ + --rgb-glob "$RGB_GLOB" \ + --savedir "${CROWN_BOX_DIR:-$REPO_ROOT/data/interim/boxes}" \ + --raw-box-savedir "${RAW_BOX_DIR:-$REPO_ROOT/data/interim/raw_boxes}" diff --git a/SLURM/experiment.sh b/SLURM/experiment.sh deleted file mode 100644 index 1b498ddcc..000000000 --- a/SLURM/experiment.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=DeepTreeAttention # Job name -#SBATCH --mail-type=END # Mail events -#SBATCH --mail-user=benweinstein2010@gmail.com # Where to send mail -#SBATCH --account=ewhite -#SBATCH --nodes=1 # Number of MPI ran -#SBATCH --cpus-per-task=1 -#SBATCH --mem=50GB -#SBATCH --time=48:00:00 #Time limit hrs:min:sec -#SBATCH --output=/home/b.weinstein/logs/DeepTreeAttention_%j.out # Standard output and error log -#SBATCH --error=/home/b.weinstein/logs/DeepTreeAttention_%j.err -#SBATCH --partition=gpu -#SBATCH --gpus=1 - -ulimit -c 0 - -module load git gcc - -git checkout $1 - -source activate DeepTreeAttention - -cd ~/DeepTreeAttention/ - -#get branch and commit name -branch_name=$((git symbolic-ref HEAD 2>/dev/null || echo "(unnamed branch)")|cut -d/ -f3-) -commit=$(git log --pretty=format:'%H' -n 1) -python train.py $branch_name $commit diff --git a/SLURM/osbs_inference.sh b/SLURM/osbs_inference.sh new file mode 100755 index 000000000..9a3e017f6 --- /dev/null +++ b/SLURM/osbs_inference.sh @@ -0,0 +1,30 @@ +#!/bin/bash +#SBATCH --job-name=osbs_infer +#SBATCH --mail-type=END +#SBATCH --mail-user=benweinstein2010@gmail.com +#SBATCH --account=ewhite +#SBATCH --nodes=1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=64GB +#SBATCH --time=48:00:00 +#SBATCH --output=/home/b.weinstein/logs/osbs_inference_%j.out +#SBATCH --error=/home/b.weinstein/logs/osbs_inference_%j.err +#SBATCH --partition=gpu +#SBATCH --gpus=1 + +# OSBS tile inference (detection + species). Configure inference_osbs in config.yml, then submit. + +set -euo pipefail + +ulimit -c 0 + +REPO_ROOT="${REPO_ROOT:-${HOME}/DeepTreeAttention}" +CONFIG_PATH="${CONFIG_PATH:-${REPO_ROOT}/config.yml}" + +module load git gcc 2>/dev/null || true +source activate DeepTreeAttention + +cd "${REPO_ROOT}" +export PYTHONPATH="${REPO_ROOT}:${PYTHONPATH:-}" + +python -m src.pipelines.osbs_inference --config "${CONFIG_PATH}" diff --git a/SLURM/osbs_mortality.sh b/SLURM/osbs_mortality.sh new file mode 100644 index 000000000..c82d48d6c --- /dev/null +++ b/SLURM/osbs_mortality.sh @@ -0,0 +1,30 @@ +#!/bin/bash +#SBATCH --job-name=osbs_mortality +#SBATCH --mail-type=END +#SBATCH --mail-user=benweinstein2010@gmail.com +#SBATCH --account=ewhite +#SBATCH --nodes=1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=96GB +#SBATCH --time=72:00:00 +#SBATCH --output=/home/b.weinstein/logs/osbs_mortality_%j.out +#SBATCH --error=/home/b.weinstein/logs/osbs_mortality_%j.err +#SBATCH --partition=gpu +#SBATCH --gpus=1 + +# OSBS tile-scale mortality comparison. Configure osbs_mortality in config.yml, then submit. + +set -euo pipefail + +ulimit -c 0 + +REPO_ROOT="${REPO_ROOT:-${HOME}/DeepTreeAttention}" +CONFIG_PATH="${CONFIG_PATH:-${REPO_ROOT}/config.yml}" + +module load git gcc 2>/dev/null || true +source activate DeepTreeAttention + +cd "${REPO_ROOT}" +export PYTHONPATH="${REPO_ROOT}:${PYTHONPATH:-}" + +python -m src.pipelines.osbs_mortality --config "${CONFIG_PATH}" diff --git a/SLURM/train_experiment.sh b/SLURM/train_experiment.sh new file mode 100755 index 000000000..6e771ce68 --- /dev/null +++ b/SLURM/train_experiment.sh @@ -0,0 +1,69 @@ +#!/bin/bash +# Submit a queued GPU training run with a stable experiment identity for Comet. +# +# Usage (from login node, repo checked out on shared FS): +# cd /path/to/DeepTreeAttention +# export EXPERIMENT_NAME=osbs-epoch70-bs128-$(date +%Y%m%d) +# # optional: export DEEPTREE_OVERRIDES=... DEEPTREE_CONFIG=... +# sbatch SLURM/train_experiment.sh +# +# REPO_ROOT defaults to SLURM_SUBMIT_DIR (your cwd when you ran sbatch). Override +# if you submit from elsewhere: REPO_ROOT=/path/to/DeepTreeAttention sbatch ... +# +# Comet: set COMET_API_KEY (and optionally COMET_WORKSPACE) in the environment +# or load them from a secrets file before sbatch. DEEPTREE_EXPERIMENT_NAME is +# forwarded so the Comet UI name matches your SLURM intent. + +#SBATCH --job-name=dt-train +#SBATCH --account=ewhite +#SBATCH --nodes=1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=64G +#SBATCH --time=12:00:00 +#SBATCH --partition=gpu +#SBATCH --gpus=1 +#SBATCH --output=/home/b.weinstein/logs/train_%x_%j.out +#SBATCH --error=/home/b.weinstein/logs/train_%x_%j.err +#SBATCH --partition=hpg-turin +#SBATCH --ntasks-per-node=1 +#SBATCH --gpus=1 + +set -euo pipefail + +REPO_ROOT="${REPO_ROOT:-${SLURM_SUBMIT_DIR:-}}" +if [[ -z "${REPO_ROOT}" ]]; then + echo "[train_experiment] Set REPO_ROOT or run sbatch from the repo: cd .../DeepTreeAttention && sbatch SLURM/train_experiment.sh" >&2 + exit 1 +fi +if [[ ! -f "${REPO_ROOT}/train.py" ]]; then + echo "[train_experiment] REPO_ROOT=${REPO_ROOT} is not the repo root (no train.py). cd into DeepTreeAttention or set REPO_ROOT." >&2 + exit 1 +fi +cd "${REPO_ROOT}" + +mkdir -p logs + +# Stable name for Comet + log files (override when submitting) +export DEEPTREE_EXPERIMENT_NAME="${EXPERIMENT_NAME:-train-${SLURM_JOB_ID}}" + +# Optional merged config fragment (same as train.py --overrides) +OVERRIDES_ARGS=() +if [[ -n "${DEEPTREE_OVERRIDES:-}" ]]; then + OVERRIDES_ARGS=(--overrides "${DEEPTREE_OVERRIDES}") +fi + +CONFIG_PATH="${DEEPTREE_CONFIG:-config.yml}" + +# Use uv if available (recommended); fall back to python on the module path. +if command -v uv >/dev/null 2>&1; then + RUN=(uv run python train.py --config "${CONFIG_PATH}" "${OVERRIDES_ARGS[@]}" --experiment-name "${DEEPTREE_EXPERIMENT_NAME}") +else + RUN=(python train.py --config "${CONFIG_PATH}" "${OVERRIDES_ARGS[@]}" --experiment-name "${DEEPTREE_EXPERIMENT_NAME}") +fi + +echo "[train_experiment] SLURM_JOB_ID=${SLURM_JOB_ID:-}" +echo "[train_experiment] DEEPTREE_EXPERIMENT_NAME=${DEEPTREE_EXPERIMENT_NAME}" +echo "[train_experiment] REPO_ROOT=${REPO_ROOT}" +echo "[train_experiment] running: ${RUN[*]}" + +exec "${RUN[@]}" diff --git a/abundance.py b/abundance.py index e35604ea6..2ba65a847 100644 --- a/abundance.py +++ b/abundance.py @@ -1,11 +1,11 @@ #Plot abundance distribution +from concurrent.futures import ThreadPoolExecutor, as_completed from glob import glob import os import pandas as pd import geopandas as gpd -from src import start_cluster -client = start_cluster.start(cpus=75,mem_size="10GB") +_IO_WORKERS = min(32, (os.cpu_count() or 4) * 4) ##Same data @@ -43,9 +43,9 @@ def read_shp(path): print(files) if len(files) == 0: continue - counts = [] - futures = client.map(read_shp,files) - counts = [x.result() for x in futures] + with ThreadPoolExecutor(max_workers=_IO_WORKERS) as ex: + futures = [ex.submit(read_shp, f) for f in files] + counts = [f.result() for f in as_completed(futures)] total_counts = pd.Series() for ser in counts: total_counts = total_counts.add(ser, fill_value=0) @@ -89,9 +89,9 @@ def read_shp(path): print(files) if len(files) == 0: continue - counts = [] - futures = client.map(read_shp,files) - counts = [x.result() for x in futures] + with ThreadPoolExecutor(max_workers=_IO_WORKERS) as ex: + futures = [ex.submit(read_shp, f) for f in files] + counts = [f.result() for f in as_completed(futures)] total_counts = pd.Series() for ser in counts: total_counts = total_counts.add(ser, fill_value=0) diff --git a/config.smoke.example.yml b/config.smoke.example.yml new file mode 100644 index 000000000..867614aef --- /dev/null +++ b/config.smoke.example.yml @@ -0,0 +1,30 @@ +# Copy to ``config.local.yml`` or pass ``--overrides config.smoke.example.yml`` with ``train.py``. +# If your ``use_data_commit`` folder has split CSVs like ``train__[\"OSBS\"].csv`` instead of +# ``train.csv`` / ``test.csv``, set ``processed_train_csv`` and ``processed_test_csv`` (relative to that folder). + +epochs: 1 +workers: 0 +preload_images: false +batch_size: 32 + +# Lightning: keep runs tiny (metrics on partial data are not meaningful) +limit_train_batches: 2 +limit_val_batches: 2 +limit_predict_batches: 4 + +use_comet: false +checkpoint_dir: results/checkpoints + +# CPU smoke (set accelerator: auto, devices: 1 for a short GPU check) +accelerator: cpu +devices: 1 +accelerator: cpu + +# Faster crown detection during OSBS inference smoke (optional) +deepforest_dead_cropmodel_name: null + +inference_osbs: + tile_limit: 1 + predict_limit_batches: 2 + # After a smoke train, point this at results/checkpoints/.pt + # species_checkpoint: results/checkpoints/smoke.pt diff --git a/config.smoke.local.yml b/config.smoke.local.yml new file mode 100644 index 000000000..f1f565b2f --- /dev/null +++ b/config.smoke.local.yml @@ -0,0 +1,18 @@ +data_dir: data/processed +use_data_commit: 4c02ae98bd774aa494fadb3508ae84ba +processed_train_csv: train_dd0adf605011f67ea3e3626231a9713a04a9e85e_['OSBS'].csv +processed_test_csv: test_dd0adf605011f67ea3e3626231a9713a04a9e85e_['OSBS'].csv + +epochs: 1 +workers: 0 +preload_images: false +batch_size: 16 + +limit_train_batches: 2 +limit_val_batches: 2 +limit_predict_batches: 2 + +use_comet: false +checkpoint_dir: results/checkpoints +accelerator: cpu +devices: 1 diff --git a/config.yml b/config.yml index feeb31bfd..979210264 100644 --- a/config.yml +++ b/config.yml @@ -1,4 +1,32 @@ -#Config +# Config — paths below default to UF HiPerGator shared storage. For a portable checkout, +# copy to ``config.local.yml`` (untracked) or override globs to point under ``data/external/neon_aop/``. + +# Vegetation structure (stem) table used by ``train.py`` when building a new dataset +raw_vst_csv: data/raw/neon_vst_data_2022.csv + +# When ``use_data_commit`` points at a bundle with split CSVs like ``train__[\"OSBS\"].csv``, set these +# (relative to the commit directory unless absolute). Default null uses ``train.csv`` / ``test.csv``. +processed_train_csv: /blue/ewhite/b.weinstein/DeepTreeAttention/4c02ae98bd774aa494fadb3508ae84ba/train_dd0adf605011f67ea3e3626231a9713a04a9e85e_['OSBS'].csv +processed_test_csv: /blue/ewhite/b.weinstein/DeepTreeAttention/4c02ae98bd774aa494fadb3508ae84ba/test_dd0adf605011f67ea3e3626231a9713a04a9e85e_['OSBS'].csv + +# Optional layout for reproducible local mirrors (see README and ``data/raw/README.md``) +data_layout: + sensor_download_root: data/external/neon_aop + # After running the filter stage, point downloads here: + canopy_points_shp: null + +# NEON AOP API downloads (``neonutilities.by_tile_aop``). Requires ``NEON_API_TOKEN`` for large pulls. +neon_download: + site: OSBS + years: [2021, 2022] + buffer_m: 50 + check_size: false + token_env: NEON_API_TOKEN + save_root: data/external/neon_aop + products: + rgb: DP3.30010.001 + hsi: DP3.30006.001 + chm: DP3.30015.001 ### Data Generation #glob path to sensor data, recursive wildcards allowed @@ -29,10 +57,10 @@ megaplot_dir: /orange/idtrees-collab/megaplot/ #Crop generation, whether to make a new dataset and customize which parts to recreate #Checkout data artifact from comet -use_data_commit: 67ec871c49cf472c8e1ae70b185addb1 +use_data_commit: 4c02ae98bd774aa494fadb3508ae84ba #Make new dataset -data_dir: /blue/ewhite/b.weinstein/DeepTreeAttention/ +data_dir: /blue/ewhite/b.weinstein/DeepTreeAttention convert_h5: True #Overwrite existing crops replace: True @@ -48,13 +76,14 @@ evergreen_ceiling: 70 # Data loader #resized Pixel size of the crowns. Square crops around each pixel of size x are used image_size: 11 -preload_images: True +preload_images: False workers: 20 sampling_ceiling: 200 #Network Parameters pretrain_state_dict: -gpus: 1 +# Lightning 2 Trainer: prefer ``devices`` + ``accelerator`` (``gpus`` still supported as a devices count). +devices: 1 batch_size: 128 bands: 349 @@ -70,6 +99,13 @@ accelerator: auto epochs: 70 min_loss_weight: 10 +# Optional Lightning caps (null = full run). See config.smoke.example.yml for workflow smoke tests. +limit_train_batches: null +limit_val_batches: null +limit_predict_batches: null +checkpoint_dir: results/checkpoints +use_comet: true + #Evaluation config #Top k class recall score top_k: 4 @@ -79,11 +115,60 @@ plot_n_individuals: 0 #Predict predict_batch_size: 64 dead_threshold: 0.95 -prediction_crop_dir: /blue/ewhite/b.weinstein/DeepTreeAttention/results/crops/ +prediction_crop_dir: results/prediction_crops/ +# Optional DeepForest CropModel for alive/dead labels at crown-detection time +deepforest_dead_cropmodel_name: weecology/cropmodel-deadtrees #Comet dashboard comet_workspace: bw4sz +# OSBS RGB inference (all tiles for ``year`` under rgb_sensor_pool that intersect AOI) +# Run: python -m src.pipelines.osbs_inference --config config.yml +inference_osbs: + site: OSBS + year: 2025 + species_checkpoint: /blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/121d61eefcd848dc97cc52bc82012c3b_OSBS.pt + aoi_path: data/raw/OSBSBoundary/OSBS_boundary.shp + results_root: /blue/ewhite/b.weinstein/DeepTreeAttention/results/osbs_inference/ + # Optional: cap tiles for debugging (omit or null for all AOI-intersecting tiles) + tile_limit: null + # Optional: cap species-model predict batches per tile (Lightning Trainer) + predict_limit_batches: null + # Optional legacy Lightning alive/dead checkpoint (otherwise DeepForest cropmodel from deepforest_dead_cropmodel_name is used when present) + dead_model_path: null + filter_dead: true + +# OSBS tile-scale mortality comparison. +# Run: python -m src.pipelines.osbs_mortality --config config.yml +osbs_mortality: + site: OSBS + years: [2017, 2025] + baseline_year: 2017 + comparison_year: 2025 + aoi_path: data/raw/OSBSBoundary/OSBS_boundary.shp + results_root: /blue/ewhite/b.weinstein/DeepTreeAttention/results/osbs_mortality/ + rgb_sensor_pool: /orange/ewhite/NeonData/OSBS/DP3.30010.001/**/Camera/**/*.tif + # Download missing AOI-intersecting RGB tiles before inference. For local mirrors, + # point save_root under data/external/neon_aop and set rgb_sensor_pool to that tree. + download_rgb: true + download_years: [2025] + save_root: data/external/neon_aop + token_env: NEON_API_TOKEN + include_provisional: true + check_size: false + aop_epsg: 32617 + tile_size_m: 1000 + # Optional debug caps + tile_limit: null + predict_limit_batches: null + # Reuse the OSBS species checkpoint above unless overridden here. + species_checkpoint: null + run_species: true + dead_model_path: null + filter_dead: true + dead_threshold: 0.95 + high_mortality_dead_change: 10 + ## Alive Dead Model Training dead: train_dir: data/raw/dead_train/ diff --git a/create_prediction_shp.py b/create_prediction_shp.py index b195023fa..e5bcf6371 100644 --- a/create_prediction_shp.py +++ b/create_prediction_shp.py @@ -1,11 +1,11 @@ #Plot abundance distribution +from concurrent.futures import ThreadPoolExecutor, as_completed from glob import glob import os import pandas as pd import geopandas as gpd -from src import start_cluster -client = start_cluster.start(cpus=100,mem_size="5GB") +_IO_WORKERS = min(32, (os.cpu_count() or 4) * 4) #Same data @@ -40,7 +40,6 @@ def read_shp(path): return intersects -futures = [] for species_model_path in species_model_paths: print(species_model_path) basename = os.path.splitext(os.path.basename(species_model_path))[0] @@ -49,9 +48,9 @@ def read_shp(path): print(files) if len(files) == 0: continue - counts = [] - futures = client.map(read_shp,files) - shps = [x.result() for x in futures] + with ThreadPoolExecutor(max_workers=_IO_WORKERS) as ex: + futures = [ex.submit(read_shp, f) for f in files] + shps = [f.result() for f in as_completed(futures)] combined_shps = pd.concat(shps) gpd_boundary = gpd.GeoDataFrame(combined_shps, geometry="geometry") gpd_boundary = gpd_boundary.reset_index(drop=True) diff --git a/data/external/.gitkeep b/data/external/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/data/interim/.gitkeep b/data/interim/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/data/raw/README.md b/data/raw/README.md new file mode 100644 index 000000000..513e47373 --- /dev/null +++ b/data/raw/README.md @@ -0,0 +1,50 @@ +# Raw and canonical field data + +This folder holds **small, shareable** inputs that define *what* to model. Large NEON AOP imagery should not live in git; use `data/external/` (see repository README) or your HPC mirror. + +## Recommended layout + +| Path | Description | +|------|-------------| +| `neon_vst.csv` | NEON Vegetation Structure (`vst`) table export (or subset) used as the stem/point source. Configure `raw_vst_csv` in `config.yml`. | +| `OSBSBoundary/` | Optional shapefiles for inference AOI (see `inference_osbs.aoi_path` in `config.yml`). | + +## Intermediary step between HPC dump and this repo + +On HiPerGator (or another host) you typically have a full NEON mirror under paths like `/orange/ewhite/NeonData/...`. **Treat that as upstream storage.** The reproducible hand-off into this repository is: + +1. **Export the VST (or megaplot) table** you actually trained on (CSV is enough for `filter_data()` in `src/data.py`). +2. **Option A — download AOP locally** with `neonutilities` (see top-level README, `deeptree-download-aop`) into `data/external/neon_aop/`, then point `rgb_sensor_pool`, `HSI_sensor_pool`, and `CHM_pool` at globs under that tree. +3. **Option B — rsync only the tiles you need** from the HPC mirror into `data/external/neon_aop/` (or another path), using the same basename/geoindex conventions NEON uses so existing `neon_paths.find_sensor_path` logic still works. + +After that, set `sensor_download_root` in `config.yml` (or override the `*_pool` globs) and you can run training without touching the full 20+ TB archive. + +## Updating woody vegetation structure (VST) + +You can refresh the canonical VST table and compare against the 2022 baseline with: + +```bash +export NEON_API_TOKEN=... # recommended to avoid API rate limits +deeptree-update-vst \ + --baseline-csv data/raw/neon_vst_data_2022.csv \ + --output-csv data/raw/neon_vst_data_latest.csv \ + --summary-csv results/vst_additions_by_site_since_2022.csv \ + --new-ids-csv results/vst_new_individual_ids_not_in_2022.csv +``` + +Notes: + +- By default, this updater includes provisional releases and requests records from `2022-01` onward. +- The summary csv reports total rows and unique `individualID` deltas per site. +- Some sites may fail due transient upstream package issues; failed site codes are printed in the JSON summary. + +## Example rsync (replace user, host, and remote paths) + +```bash +mkdir -p data/external/neon_aop +rsync -avz --progress \ + USER@hpg.rc.ufl.edu:/orange/ewhite/NeonData/OSBS/DP3.30010.001/ \ + data/external/neon_aop/OSBS/DP3.30010.001/ +``` + +Repeat for `DP3.30006.001` (hyperspectral) and your CHM product path as needed. diff --git a/deeptreeattention.egg-info/PKG-INFO b/deeptreeattention.egg-info/PKG-INFO new file mode 100644 index 000000000..f336a973b --- /dev/null +++ b/deeptreeattention.egg-info/PKG-INFO @@ -0,0 +1,216 @@ +Metadata-Version: 2.4 +Name: deeptreeattention +Version: 0.1.0 +Summary: Tree species prediction for National Ecological Observatory Network data. +Author: Ben Weinstein +License: MIT +Requires-Python: <3.13,>=3.10 +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: comet_ml +Requires-Dist: python-dotenv +Requires-Dist: deepforest>=2.0.0 +Requires-Dist: descartes +Requires-Dist: geopandas +Requires-Dist: h5py +Requires-Dist: matplotlib +Requires-Dist: neonutilities +Requires-Dist: numpy +Requires-Dist: packaging>=26.1 +Requires-Dist: pandas +Requires-Dist: pytorch_lightning +Requires-Dist: PyYAML +Requires-Dist: rasterio +Requires-Dist: rasterstats +Requires-Dist: scikit-image +Requires-Dist: scikit-learn +Requires-Dist: Shapely +Requires-Dist: torch +Requires-Dist: torchmetrics +Requires-Dist: torchvision +Provides-Extra: dev +Requires-Dist: pytest; extra == "dev" +Requires-Dist: ruff; extra == "dev" +Dynamic: license-file + +# DeepTreeAttention + +[Github Actions](https://github.com/Weecology/DeepTreeAttention/actions/) + +Tree species classification for **National Ecological Observatory Network (NEON)** imagery, implementing Hang et al. 2020 ([Hyperspectral Image Classification with Attention Aided CNNs](https://arxiv.org/abs/2005.11977)) with a PyTorch Lightning training stack. + +This README is organized around the lifecycle you described: **raw points → generated tensors → training → evaluation → inference → reporting**. Dask has been removed in favor of **single-node sequential code** plus **plain SLURM job arrays** for embarrassingly parallel stages (per-plot crowns, large I/O batches, and so on). + +--- + +## 0. Environment (uv) + +```bash +uv sync --extra dev +uv run pytest -v +``` + +Use `uv run …` for every CLI invocation so the locked environment is respected. + +--- + +## 1. Data layout and the “HPC handoff” + + +| Stage | Location | What it is | +| ----------------------------------------- | ------------------------------------------------ | -------------------------------------------------------------------------------------------------- | +| **Upstream (not in git)** | e.g. `/orange/ewhite/NeonData/...` on HiPerGator | Full NEON mirror; too large to vendor. | +| **Shareable inputs** | `data/raw/` | VST (or similar) stem tables, AOI shapefiles. See `data/raw/README.md`. | +| **Intermediary bundle (commit or rsync)** | `data/interim/` | Filtered stem points (`canopy_points.shp`), optional per-plot crown boxes before merge, manifests. | +| **Heavy rasters** | `data/external/neon_aop/` | RGB / HSI / CHM tiles from `**neonutilities`** downloads or selective `rsync` from the mirror. | +| **Training tensors** | `data/processed//` or `config["data_dir"]` | HSI crops, `train.csv` / `test.csv`, `crowns.shp`. | + + +**Practical handoff from HiPerGator:** export the VST CSV you trust, copy it to `data/raw/`, then either (a) run `deeptree-download-aop` (below) into `data/external/neon_aop/`, or (b) `rsync` only the DP3 products you need into the same tree. Point `rgb_sensor_pool`, `HSI_sensor_pool`, and `CHM_pool` in `config.yml` at that mirror. You no longer need to regenerate crops for every experiment if you set `use_data_commit` to a frozen directory name or reuse `data_dir` with `replace: false` after the first successful build. + +--- + +## 2. Download AOP tiles (`neonutilities`) + +The Python `**neonutilities`** package exposes `by_tile_aop`, equivalent to the R `neonUtilities::byTileAOP` helper. This repository wraps it for stem coordinates. + +1. Obtain a NEON API token and export it (optional for tiny pulls; recommended otherwise): + ```bash + export NEON_API_TOKEN="your_token" + ``` +2. After you have `canopy_points.shp` (from the filtering stage in `TreeData`), set `data_layout.canopy_points_shp` in `config.yml` **or** pass `--points`. +3. Run: + ```bash + uv run deeptree-download-aop --config config.yml + ``` + Defaults live under `neon_download` in `config.yml` (site, years, DP3 IDs for RGB, hyperspectral, CHM, buffer in meters). + +--- + +## 3. Generate datasets (0 — data generation) + +Pipeline code paths: + +- `src/data.py` — filter VST, CHM checks, megaplot merge hooks, train/test split. +- `src/generate.py` — DeepForest crowns, hyperspectral crops, optional H5→TIF conversion via `src/neon_paths.py`. + +**Local / single job:** instantiate `data.TreeData` with `use_data_commit: null` and your `config.yml`, as in `train.py`. + +**Parallel crowns (SLURM):** each array task runs one plot: + +```bash +uv run python -m src.pipelines.crown_one_plot \ + --canopy-points data/interim/canopy_points.shp \ + --plot OSBS_001 \ + --rgb-glob "data/external/neon_aop/**/DP3.30010.001/**/Camera/**/*.tif" \ + --savedir data/interim/boxes \ + --raw-box-savedir data/interim/raw_boxes +``` + +After all tasks finish, merge: + +```bash +uv run deeptree-merge-crown-boxes \ + --boxes-dir data/interim/boxes \ + --out data/interim/crowns.shp +``` + +Submit `SLURM/crown_plot_array.sh` (tune `#SBATCH` directives, `plots.txt`, and `REPO_ROOT`). + +> **Note:** Passing a Dask `Client` into `points_to_crowns`, `generate_crops`, or `train_test_split` now raises a clear error. Parallelize with SLURM (or your own outer loop), not an in-Python Dask cluster. + +--- + +## 4. Training (1) + +**Experiment identity (Comet + SLURM)** — you no longer pass git branch/sha as required positionals. The trainer auto-detects git (branch, SHA, dirty flag) and uploads a merged `**config.merged.yml`**, optional `**git_diff_uncommitted.patch**`, and a `**src/**` code bundle to Comet when logging is enabled. + +```bash +# Human-readable Comet experiment name (recommended) +export DEEPTREE_EXPERIMENT_NAME=osbs-baseline-epoch70 +uv run python train.py --config config.yml --experiment-name "${DEEPTREE_EXPERIMENT_NAME}" + +# Or rely on auto naming (UTC timestamp + short SHA, or SLURM_JOB_ID on clusters) +uv run python train.py --config config.yml + +# Optional YAML merged last (keep one file per ablation under e.g. experiments/) +uv run python train.py --config config.yml --overrides experiments/my_ablation.yml -n osbs-lr-sweep-001 +``` + +Environment variables (highest priority first for the display name): `**DEEPTREE_EXPERIMENT_NAME**`, `**COMET_EXPERIMENT_NAME**`, then `**--experiment-name**`, then a default from `**SLURM_JOB_ID**` or timestamp. + +`train.py` reads `config.yml` (+ `config.local.yml` if present), logs to Comet when `**use_comet: true**` and `**COMET_API_KEY**` / `**COMET_KEY**` are set, and writes checkpoints under `**checkpoint_dir**`. `raw_vst_csv` defaults to `data/raw/neon_vst_data_2022.csv` but can be overridden in YAML. + +**SLURM (queued GPU training)** — from the repo checkout, set **`EXPERIMENT_NAME`** (forwarded as **`DEEPTREE_EXPERIMENT_NAME`**), optionally **`DEEPTREE_OVERRIDES`** and **`DEEPTREE_CONFIG`**, then: + +```bash +cd /path/to/DeepTreeAttention +EXPERIMENT_NAME=osbs-baseline sbatch SLURM/train_experiment.sh +``` + +**`REPO_ROOT`** defaults to **`SLURM_SUBMIT_DIR`** (your shell’s current directory when you run `sbatch`), so you normally **do not** export it if you `cd` into the project first. Set **`REPO_ROOT=/path/to/DeepTreeAttention`** only when you submit from another directory. + +Edit `#SBATCH` lines in `SLURM/train_experiment.sh` for your partition/account. The job runs from a **shared checkout** so you can **`git pull`** or edit **`experiments/*.yml`** between submissions; each job still logs the **resolved config** and **git SHA** to Comet for apples-to-apples comparison. + +--- + +## 5. Evaluation (2) + +Evaluation is integrated in the Lightning `TreeModel` / `MultiStage` path (validation metrics, crown-level scoring). Point `use_data_commit` at a frozen processed directory to re-score without touching generation. + +--- + +## 6. Inference (3) + +**OSBS RGB tile inference** is configured solely through `inference_osbs` in `config.yml`. + +```bash +uv run python predict.py --config config.yml +# or +uv run deeptree-infer-osbs --config config.yml +``` + +HiPerGator template: `SLURM/osbs_inference.sh`. + +--- + +## 7. Reporting and analysis (4) + +- Comet dashboards (when `comet_ml` is configured). +- Scripts such as `abundance.py`, `create_prediction_shp.py`, and `src/multinomial.py` now use `ThreadPoolExecutor` instead of Dask for light parallelism over shapefiles. + +--- + +## Project map + +```text +├── config.yml # Central configuration (+ neon_download / data_layout) +├── data/ +│ ├── raw/README.md # What belongs in raw inputs + rsync hints +│ ├── external/ # Downloaded / rsync'd NEON tiles (.gitkeep only) +│ └── interim/ # Optional canonical intermediate artifacts +├── experiments/ # Optional YAML fragments for ``--overrides`` (one ablation per file) +├── SLURM/ # Job scripts (``train_experiment.sh``, inference, crown array) +├── src/ +│ ├── data.py # Lightning TreeData + filtering +│ ├── experiment_tracking.py # Git metadata + default experiment names +│ ├── generate.py # Crowns + crops +│ ├── neon_download.py # neonutilities helpers +│ └── pipelines/ # CLIs (inference, download, crown worker, merge) +├── train.py # Full training driver +├── predict.py # OSBS inference entry +└── pyproject.toml # Dependencies + `deeptree-*` console scripts +``` + +--- + +## Open questions / follow-up work + +1. **Megaplot + IFAS branches** — logic is dense; consider isolating into a small submodule with explicit tests. +2. **CHM product ID per NEON revision** — confirm `DP3.30015.001` matches your mirror layout (`CanopyHeightModelGtif`). +3. **Comet as the sole reproducibility anchor** — `use_data_commit` ties runs to Comet artifact IDs; consider replacing with explicit semantic version tags on `data/processed//`. +4. **Dead-tree filtering in `TreeData`** — references `self.predicted_dead` in a logging block; verify that attribute is always defined on your code path before relying on those images in Comet. + +Training uses Lightning 2 `Trainer(accelerator=…, devices=…)` (see `devices` / `accelerator` in `config.yml`; legacy `gpus` is still read as a device count when `devices` is omitted). + +Contributions: branch per feature, add/adjust pytest coverage for anything you touch, keep diffs focused. diff --git a/deeptreeattention.egg-info/SOURCES.txt b/deeptreeattention.egg-info/SOURCES.txt new file mode 100644 index 000000000..47725592b --- /dev/null +++ b/deeptreeattention.egg-info/SOURCES.txt @@ -0,0 +1,56 @@ +LICENSE +README.md +pyproject.toml +setup.py +deeptreeattention.egg-info/PKG-INFO +deeptreeattention.egg-info/SOURCES.txt +deeptreeattention.egg-info/dependency_links.txt +deeptreeattention.egg-info/entry_points.txt +deeptreeattention.egg-info/requires.txt +deeptreeattention.egg-info/top_level.txt +src/CHM.py +src/Hyperspectral.py +src/__init__.py +src/augmentation.py +src/data.py +src/experiment_tracking.py +src/generate.py +src/local_smoke_logger.py +src/main.py +src/megaplot.py +src/metrics.py +src/multinomial.py +src/neon_download.py +src/neon_paths.py +src/patches.py +src/predict.py +src/utils.py +src/visualize.py +src/vst_update.py +src/models/Hang2020.py +src/models/__init__.py +src/models/dead.py +src/models/metadata.py +src/models/multi_stage.py +src/models/year.py +src/pipelines/__init__.py +src/pipelines/crown_one_plot.py +src/pipelines/download_neon_aop.py +src/pipelines/merge_crown_boxes.py +src/pipelines/osbs_inference.py +src/pipelines/update_neon_vst.py +tests/test_CHM.py +tests/test_Hang2020.py +tests/test_augmentation.py +tests/test_data.py +tests/test_dead.py +tests/test_generate.py +tests/test_megaplot.py +tests/test_metadata.py +tests/test_metrics.py +tests/test_multi_stage.py +tests/test_multinomial.py +tests/test_osbs_inference.py +tests/test_patches.py +tests/test_predict.py +tests/test_year.py \ No newline at end of file diff --git a/deeptreeattention.egg-info/dependency_links.txt b/deeptreeattention.egg-info/dependency_links.txt new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/deeptreeattention.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/deeptreeattention.egg-info/entry_points.txt b/deeptreeattention.egg-info/entry_points.txt new file mode 100644 index 000000000..a44902985 --- /dev/null +++ b/deeptreeattention.egg-info/entry_points.txt @@ -0,0 +1,6 @@ +[console_scripts] +deeptree-crown-one-plot = src.pipelines.crown_one_plot:main +deeptree-download-aop = src.pipelines.download_neon_aop:main +deeptree-infer-osbs = src.pipelines.osbs_inference:main +deeptree-merge-crown-boxes = src.pipelines.merge_crown_boxes:main +deeptree-update-vst = src.pipelines.update_neon_vst:main diff --git a/deeptreeattention.egg-info/requires.txt b/deeptreeattention.egg-info/requires.txt new file mode 100644 index 000000000..3049148cc --- /dev/null +++ b/deeptreeattention.egg-info/requires.txt @@ -0,0 +1,25 @@ +comet_ml +python-dotenv +deepforest>=2.0.0 +descartes +geopandas +h5py +matplotlib +neonutilities +numpy +packaging>=26.1 +pandas +pytorch_lightning +PyYAML +rasterio +rasterstats +scikit-image +scikit-learn +Shapely +torch 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geo_index.split("_")[1]) < 3290124)): - osbs_tiles.append(rgb_path) - return osbs_tiles + python predict.py + python predict.py --config /path/to/config.yml +Equivalent module invocation: -def convert(rgb_path, hyperspectral_pool, savedir): - #convert .h5 hyperspec tile if needed - basename = os.path.basename(rgb_path) - geo_index = re.search("(\d+_\d+)_image", basename).group(1) - - h5_list = [x for x in hyperspectral_pool if geo_index in x] - tif_paths = [] - for path in h5_list: - year = path.split("/")[6] - tif_basename = os.path.splitext(os.path.basename(rgb_path))[0] + "_hyperspectral_{}.tif".format(year) - tif_path = "{}/{}".format(savedir, tif_basename) - if not os.path.exists(tif_path): - tif_paths.append(neon_paths.convert_h5(path, rgb_path, savedir, year=year)) - else: - tif_paths.append(tif_path) - - return tif_paths + python -m src.pipelines.osbs_inference --config config.yml +""" -#Params -config = data.read_config("config.yml") -config["preload_images"] = False -comet_logger = CometLogger(project_name="DeepTreeAttention2", workspace=config["comet_workspace"], auto_output_logging="simple") -comet_logger.experiment.add_tag("prediction") +from src.pipelines.osbs_inference import main -comet_logger.experiment.log_parameters(config) - -cpu_client = start(cpus=1, mem_size="10GB") - -dead_model_path = "/orange/idtrees-collab/DeepTreeAttention/Dead/snapshots/c4945ae57f4145948531a0059ebd023c.pl" -config["crop_dir"] = "/blue/ewhite/b.weinstein/DeepTreeAttention/67ec871c49cf472c8e1ae70b185addb1" -savedir = config["crop_dir"] - -species_model_paths = ["/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/71f8ba53af2b46049906554457cd5429.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/ac7b4194811c4bdd9291892bccc4e661.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/b629e5365a104320bcec03843e9dd6fd.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/5ac9afabe3f6402a9c312ba4cee5160a.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/46aff76fe2974b72a5d001c555d7c03a.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/63bdab99d6874f038212ac301439e9cc.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/c871ed25dc1c4a3e97cf3b723cf88bb6.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/6d45510824d6442c987b500a156b77d6.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/83f6ede4f90b44ebac6c1ac271ea0939.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/47ee5858b1104214be178389c13bd025.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/1ccdc11bdb9a4ae897377e3e97ce88b9.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/3c7b7fe01eaa4d1b8a1187b792b8de40.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/3b6d9f2367584b3691de2c2beec47beb.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/509ef67c6050471e83199d2e9f4f3f6a.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/ae7abdd50de04bc9970295920f0b9603.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/d2180f54487b45269c1d86398d7f0fb8.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/6f9730cbe9ba4541816f32f297b536cd.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/71f8ba53af2b46049906554457cd5429.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/6a28224a2dba4e4eb7f528d19444ec4e.pt", - "/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/b9c0111b1dc0420b84e3b6b79da4e166.pt"] - -#generate HSI_tif data if needed. -h5_pool = glob(config["HSI_sensor_pool"], recursive=True) -h5_pool = [x for x in h5_pool if not "neon-aop-products" in x] -hyperspectral_pool = glob(config["HSI_tif_dir"]+"*") - -### Step 1 Find RGB Tiles and convert HSI -tiles = find_rgb_files(site="OSBS", config=config) -#tif_futures = cpu_client.map( - #convert, - #tiles, - #hyperspectral_pool=h5_pool, - #savedir=config["HSI_tif_dir"]) -#wait(tif_futures) - -for x in tiles: - basename = os.path.splitext(os.path.basename(x))[0] - shpname = "/blue/ewhite/b.weinstein/DeepTreeAttention/results/crowns/{}.shp".format(basename) - if not os.path.exists(shpname): - try: - crowns = predict.find_crowns(rgb_path=x, config=config, dead_model_path=dead_model_path) - crowns.to_file(shpname) - except Exception as e: - traceback.print_exc() - print("{} failed to build crowns with {}".format(shpname, e)) - continue - -crown_annotations_paths = [] -crown_annotations_futures = [] -for x in tiles: - basename = os.path.splitext(os.path.basename(x))[0] - shpname = "/blue/ewhite/b.weinstein/DeepTreeAttention/results/crowns/{}.shp".format(basename) - try: - crowns = gpd.read_file(shpname) - except: - continue - if not os.path.exists("/blue/ewhite/b.weinstein/DeepTreeAttention/results/crops/{}.shp".format(basename)): - written_file = predict.generate_prediction_crops(crowns, config, as_numpy=True, client=cpu_client) - crown_annotations_paths.append(written_file) - else: - crown_annotations_path = "/blue/ewhite/b.weinstein/DeepTreeAttention/results/crops/{}.shp".format(basename) - crown_annotations_paths.append(crown_annotations_path) - -#Recursive predict to avoid prediction levels that will be later ignored. -trainer = Trainer(gpus=config["gpus"], logger=False, enable_checkpointing=False) - -## Step 2 - Predict Crowns -for species_model_path in species_model_paths: - print(species_model_path) - # Load species model - #Do not preload weights - config["pretrained_state_dict"] = None - m = multi_stage.MultiStage.load_from_checkpoint(species_model_path, config=config) - prediction_dir = os.path.join("/blue/ewhite/b.weinstein/DeepTreeAttention/results/", - os.path.splitext(os.path.basename(species_model_path))[0]) - try: - os.mkdir(prediction_dir) - except: - pass - for x in crown_annotations_paths: - results_shp = os.path.join(prediction_dir, os.path.basename(x)) - if not os.path.exists(results_shp): - print(x) - try: - predict.predict_tile( - crown_annotations=x, - filter_dead=True, - trainer=trainer, - m=m, - savedir=prediction_dir, - config=config) - except Exception as e: - traceback.print_exc() - continue +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..3d6b790cb --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,64 @@ +[build-system] +requires = ["setuptools>=68", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "deeptreeattention" +version = "0.1.0" +description = "Tree species prediction for National Ecological Observatory Network data." +readme = "README.md" +requires-python = ">=3.10,<3.13" +license = { text = "MIT" } +authors = [{ name = "Ben Weinstein" }] +dependencies = [ + "comet_ml", + "python-dotenv", + "deepforest>=2.0.0", + "descartes", + "geopandas", + "h5py", + "matplotlib", + "neonutilities", + "numpy", + "packaging>=26.1", + "pandas", + "pytorch_lightning", + "PyYAML", + "rasterio", + "rasterstats", + "scikit-image", + "scikit-learn", + "Shapely", + "torch", + "torchmetrics", + "torchvision", +] + +[project.optional-dependencies] +dev = ["pytest", "ruff"] + +[project.scripts] +deeptree-infer-osbs = "src.pipelines.osbs_inference:main" +deeptree-osbs-mortality = "src.pipelines.osbs_mortality:main" +deeptree-download-aop = "src.pipelines.download_neon_aop:main" +deeptree-crown-one-plot = "src.pipelines.crown_one_plot:main" +deeptree-merge-crown-boxes = "src.pipelines.merge_crown_boxes:main" +deeptree-update-vst = "src.pipelines.update_neon_vst:main" + +[tool.setuptools.packages.find] +where = ["."] +include = ["src", "src.*"] + +# PyTorch + CUDA 12.8 — GPU nodes cap at driver 12080 (CUDA 12.8); cu130 fails. +[[tool.uv.index]] +name = "pytorch-cu128" +url = "https://download.pytorch.org/whl/cu128" +explicit = true + +[tool.uv.sources] +torch = [ + { index = "pytorch-cu128", marker = "sys_platform != 'darwin'" }, +] +torchvision = [ + { index = "pytorch-cu128", marker = "sys_platform != 'darwin'" }, +] diff --git a/requirements.txt b/requirements.txt index 599f6a8ab..7c56aef98 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,12 +1,10 @@ comet_ml -dask -dask_jobqueue deepforest descartes -distributed geopandas h5py matplotlib +neonutilities numpy pandas pytest diff --git a/results/.gitkeep b/results/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/sample_multinomial.py b/sample_multinomial.py index 1cd955634..baad8147d 100644 --- a/sample_multinomial.py +++ b/sample_multinomial.py @@ -1,8 +1,9 @@ -from src.multinomial import * -from src import start_cluster +from src.multinomial import wrapper import glob import pandas as pd -client = start_cluster.start(cpus=50, mem_size="10GB") for x in range(100): - wrapper(client=client, iteration=x, experiment_key="06ee8e987b014a4d9b6b824ad6d28d83") + wrapper( + iteration=x, + experiment_key="06ee8e987b014a4d9b6b824ad6d28d83", + ) diff --git a/setup.py b/setup.py index d8971ef95..d80227205 100644 --- a/setup.py +++ b/setup.py @@ -1,11 +1,5 @@ -from setuptools import find_packages, setup +from setuptools import setup -setup( - name='src', - packages=find_packages(), - version='0.1.0', - python_requires='>=3.9' - description='Tree Species Prediction for the National Ecological Observatory Network (NEON)', - author='Ben Weinstein', - license='MIT', -) + +# Compatibility shim for tooling that still invokes setup.py directly. +setup() diff --git a/src/__init__.py b/src/__init__.py index e69de29bb..ee35af758 100644 --- a/src/__init__.py +++ b/src/__init__.py @@ -0,0 +1 @@ +"""DeepTreeAttention source package.""" diff --git a/src/data.py b/src/data.py index e51dc01ec..4a7a4af80 100644 --- a/src/data.py +++ b/src/data.py @@ -1,6 +1,5 @@ #Ligthning data module from . import __file__ -from distributed import wait import glob import geopandas as gpd import numpy as np @@ -19,6 +18,14 @@ from torch.utils.data import Dataset import rasterio +def _resolved_split_csv(config, data_dir, key, default_name): + """Resolve ``processed_train_csv`` / ``processed_test_csv`` or default ``train.csv`` / ``test.csv``.""" + rel = config.get(key) + if rel: + return rel if os.path.isabs(rel) else os.path.join(data_dir, rel) + return os.path.join(data_dir, default_name) + + def filter_data(path, config): """Transform raw NEON data into clean shapefile Args: @@ -101,7 +108,7 @@ def filter_data(path, config): shp = shp[~(shp.siteID.isin(["PUUM","ORNL"]))] # There are a couple NEON plots within the OSBS megaplot, make sure they are removed - shp = shp[~shp.plotID.isin(["OSBS_026","OSBS_029","OSBS_039","OSBS_027","OSBS_036"])] + shp = shp[~shp["plotID"].isin(["OSBS_026", "OSBS_029", "OSBS_039", "OSBS_027", "OSBS_036"])] return shp @@ -111,19 +118,19 @@ def sample_plots(shp, min_train_samples=5, min_test_samples=3, iteration = 1): shp: pandas dataframe of filtered tree locations test_fraction: proportion of plots in test datasets min_samples: minimum number of samples per class - iteration: a dummy parameter to make dask submission unique + iteration: reserved for reproducibility hooks (unused) """ #When splitting train/test, only use 1 sample per year for counts. - single_year = shp.groupby("individual").apply(lambda x: x.head(1)) + single_year = shp.groupby("individual", as_index=False).head(1) - plotIDs = list(shp.plotID.unique()) - if len(plotIDs) <=2: - test = shp[shp.plotID == shp.plotID.unique()[0]] - train = shp[shp.plotID == shp.plotID.unique()[1]] + plotIDs = list(shp["plotID"].unique()) + if len(plotIDs) <= 2: + test = shp[shp["plotID"] == shp["plotID"].unique()[0]] + train = shp[shp["plotID"] == shp["plotID"].unique()[1]] return train, test else: - plotIDs = shp[shp.siteID=="OSBS"].plotID.unique() + plotIDs = shp[shp.siteID == "OSBS"]["plotID"].unique() np.random.shuffle(plotIDs) species_to_sample = shp.taxonID.unique() @@ -135,26 +142,36 @@ def sample_plots(shp, min_train_samples=5, min_test_samples=3, iteration = 1): test_plots = [] for plotID in plotIDs: - selected_plot = single_year[single_year.plotID == plotID] + selected_plot = single_year[single_year["plotID"] == plotID] # If any species is missing from min samples, include plot if any([x in species_to_sample for x in selected_plot.taxonID.unique()]): test_plots.append(plotID) - counts = single_year[single_year.plotID.isin(test_plots)].taxonID.value_counts().to_dict() + counts = single_year[single_year["plotID"].isin(test_plots)].taxonID.value_counts().to_dict() species_completed = [key for key, value in counts.items() if value > species_floor[key]] species_to_sample = [x for x in shp.taxonID.unique() if not x in species_completed] #Sample from original multi_year data - test = shp[shp.plotID.isin(test_plots)] - train = shp[~shp.plotID.isin(test.plotID.unique())] + test = shp[shp["plotID"].isin(test_plots)] + train = shp[~shp["plotID"].isin(test["plotID"].unique())] ## Remove fixed boxes from test test = test.loc[~test["box_id"].astype(str).str.contains("fixed").fillna(False)] - testids = test.groupby("individual").apply(lambda x: x.head(1)).groupby("taxonID").filter(lambda x: x.shape[0] >= min_test_samples).individual - test = test[test.individual.isin(testids)] + testids = ( + test.groupby("individual", as_index=False) + .head(1) + .groupby("taxonID") + .filter(lambda x: x.shape[0] >= min_test_samples) + )["individual"] + test = test[test["individual"].isin(testids)] - trainids = train.groupby("individual").apply(lambda x: x.head(1)).groupby("taxonID").filter(lambda x: x.shape[0] >= min_train_samples).individual - train = train[train.individual.isin(trainids)] + trainids = ( + train.groupby("individual", as_index=False) + .head(1) + .groupby("taxonID") + .filter(lambda x: x.shape[0] >= min_train_samples) + )["individual"] + train = train[train["individual"].isin(trainids)] train = train[train.taxonID.isin(test.taxonID)] test = test[test.taxonID.isin(train.taxonID)] @@ -166,10 +183,15 @@ def train_test_split(shp, config, client = None): """Create the train test split Args: shp: a filter pandas dataframe (or geodataframe) - client: optional dask client + client: deprecated, ignored (kept for API compatibility) Returns: None: train.shp and test.shp are written as side effect """ + if client is not None: + raise ValueError( + "Dask client support was removed from train_test_split. " + "Run iterations sequentially or parallelize at the SLURM job level." + ) min_sampled = config["min_train_samples"] + config["min_test_samples"] keep = shp.taxonID.value_counts() > (min_sampled) species_to_keep = keep[keep].index @@ -177,47 +199,21 @@ def train_test_split(shp, config, client = None): print("splitting data into train test. Initial data has {} points from {} species with a min of {} samples".format(shp.shape[0],shp.taxonID.nunique(),min_sampled)) test_species = 0 ties = [] - if client: - futures = [ ] - for x in np.arange(config["iterations"]): - future = client.submit( - sample_plots, - shp=shp, - min_train_samples=config["min_train_samples"], - iteration=x, - min_test_samples=config["min_test_samples"], - ) - futures.append(future) - - wait(futures) - for x in futures: - train, test = x.result() - if test.taxonID.nunique() > test_species: - print("Selected test has {} points and {} species".format(test.shape[0], test.taxonID.nunique())) - saved_train = train - saved_test = test - test_species = test.taxonID.nunique() - ties = [] - ties.append([train, test]) - elif test.taxonID.nunique() == test_species: - ties.append([train, test]) - else: - for x in np.arange(config["iterations"]): - train, test = sample_plots( - shp=shp, - min_train_samples=config["min_train_samples"], - min_test_samples=config["min_test_samples"], - ) - if test.taxonID.nunique() > test_species: - print("Selected test has {} points and {} species".format(test.shape[0], test.taxonID.nunique())) - saved_train = train - saved_test = test - test_species = test.taxonID.nunique() - #reset ties - ties = [] - ties.append([train, test]) - elif test.taxonID.nunique() == test_species: - ties.append([train, test]) + for x in np.arange(config["iterations"]): + train, test = sample_plots( + shp=shp, + min_train_samples=config["min_train_samples"], + min_test_samples=config["min_test_samples"], + ) + if test.taxonID.nunique() > test_species: + print("Selected test has {} points and {} species".format(test.shape[0], test.taxonID.nunique())) + saved_train = train + saved_test = test + test_species = test.taxonID.nunique() + ties = [] + ties.append([train, test]) + elif test.taxonID.nunique() == test_species: + ties.append([train, test]) # The size of the datasets if len(ties) > 1: @@ -255,6 +251,10 @@ def __init__(self, df=None, csv_file=None, config=None, train=True): self.image_paths = self.annotations.groupby("individual").apply(lambda x: x.set_index('tile_year').image_path.to_dict()) if train: self.labels = self.annotations.set_index("individual").label.to_dict() + if "site" in self.annotations.columns: + self.sites = self.annotations.drop_duplicates(subset=["individual"]).set_index("individual")["site"].to_dict() + else: + self.sites = None # Create augmentor self.transformer = augmentation.train_augmentation(image_size=self.image_size) @@ -262,7 +262,16 @@ def __init__(self, df=None, csv_file=None, config=None, train=True): # Pin data to memory if desired if self.config["preload_images"]: - for individual in self.individuals: + n_ind = len(self.individuals) + n_years = len(self.years) + if n_ind > 200: + print( + "[TreeDataset train={}] Preloading {} individuals × {} years from {} …".format( + self.train, n_ind, n_years, self.config["crop_dir"] + ), + flush=True, + ) + for i, individual in enumerate(self.individuals): images = [] ind_annotations = self.image_paths[individual] for year in self.years: @@ -276,6 +285,10 @@ def __init__(self, df=None, csv_file=None, config=None, train=True): image = self.transformer(image) images.append(image) self.image_dict[individual] = images + if n_ind > 200 and (i + 1) % max(1, n_ind // 10) == 0: + print(" … preloaded {}/{} individuals".format(i + 1, n_ind), flush=True) + if n_ind > 200: + print("[TreeDataset train={}] Preload finished ({} individuals).".format(self.train, n_ind), flush=True) def __len__(self): # 0th based index @@ -300,6 +313,9 @@ def __getitem__(self, index): image = self.transformer(image) images.append(image) inputs["HSI"] = images + if self.sites is not None: + site = self.sites.get(individual, 0) + inputs["site"] = torch.tensor(site, dtype=torch.long) if self.train: label = self.labels[individual] @@ -420,17 +436,30 @@ def __init__(self, csv_file, config, HSI=True, metadata=False, client = None, da else: self.crowns = gpd.read_file("{}/crowns.shp".format(self.data_dir)) + crowns_gdf = self.crowns + if "plotID" not in crowns_gdf.columns: + plot_cols = [c for c in crowns_gdf.columns if str(c).lower() == "plotid"] + if plot_cols: + crowns_gdf = crowns_gdf.rename(columns={plot_cols[0]: "plotID"}) + annotations = generate.generate_crops( - self.crowns, + crowns_gdf, savedir=self.config["crop_dir"], sensor_glob=self.config["HSI_sensor_pool"], - convert_h5=self.config["convert_h5"], + convert_h5=self.config["convert_h5"], rgb_glob=self.config["rgb_sensor_pool"], HSI_tif_dir=self.config["HSI_tif_dir"], client=self.client, - replace=self.config["replace"] + replace=self.config["replace"], ) - + + if "plotID" not in annotations.columns: + pts = self.canopy_points + plot_col = next((c for c in pts.columns if str(c).lower() == "plotid"), None) + if plot_col is not None: + plot_map = pts.drop_duplicates(subset=["individual"]).set_index("individual")[plot_col] + annotations["plotID"] = annotations["individual"].map(plot_map) + annotations.to_csv("{}/annotations.csv".format(self.data_dir)) if self.comet_logger: @@ -501,9 +530,22 @@ def __init__(self, csv_file, config, HSI=True, metadata=False, client = None, da ) else: - print("Loading previous run") - self.train = pd.read_csv("{}/train.csv".format(self.data_dir)) - self.test = pd.read_csv("{}/test.csv".format(self.data_dir)) + print("Loading previous run") + train_path = _resolved_split_csv( + self.config, self.data_dir, "processed_train_csv", "train.csv" + ) + test_path = _resolved_split_csv( + self.config, self.data_dir, "processed_test_csv", "test.csv" + ) + for path, label in ((train_path, "train"), (test_path, "test")): + if not os.path.isfile(path): + raise FileNotFoundError( + "Missing {} split CSV at {}. Add train.csv/test.csv under the commit " + "folder, or set processed_train_csv / processed_test_csv in config.yml " + "(paths relative to that folder unless absolute).".format(label, path) + ) + self.train = pd.read_csv(train_path) + self.test = pd.read_csv(test_path) try: self.train["individual"] = self.train["individualID"] diff --git a/src/experiment_tracking.py b/src/experiment_tracking.py new file mode 100644 index 000000000..495ca6f94 --- /dev/null +++ b/src/experiment_tracking.py @@ -0,0 +1,83 @@ +"""Git + Comet helpers for reproducible experiment tracking (no manual branch/sha CLI).""" + +from __future__ import annotations + +import os +import subprocess +from datetime import datetime, timezone +from typing import Any + + +def _run_git(repo_root: str, *args: str) -> str | None: + try: + out = subprocess.check_output( + ["git", *args], + cwd=repo_root, + stderr=subprocess.DEVNULL, + text=True, + ) + return out.strip() or None + except (subprocess.CalledProcessError, FileNotFoundError, OSError): + return None + + +def git_metadata(repo_root: str | None = None) -> dict[str, Any]: + """Return branch, SHAs, dirty flag, and optional short diff (for Comet / SLURM logs).""" + root = repo_root or os.getcwd() + sha = _run_git(root, "rev-parse", "HEAD") + short = _run_git(root, "rev-parse", "--short", "HEAD") + branch = _run_git(root, "rev-parse", "--abbrev-ref", "HEAD") + dirty = False + diff_head = None + if sha: + try: + subprocess.check_call( + ["git", "diff", "--quiet"], + cwd=root, + stderr=subprocess.DEVNULL, + ) + except subprocess.CalledProcessError: + dirty = True + try: + diff_head = subprocess.check_output( + ["git", "diff", "HEAD"], + cwd=root, + stderr=subprocess.DEVNULL, + text=True, + ) + if len(diff_head) > 400_000: + diff_head = diff_head[:400_000] + "\n\n[truncated]\n" + except (subprocess.CalledProcessError, OSError): + diff_head = None + return { + "git_branch": branch or "unknown", + "git_sha": sha or "unknown", + "git_short_sha": short or "unknown", + "git_dirty": dirty, + "git_diff_head": diff_head, + } + + +def default_experiment_name(meta: dict[str, Any]) -> str: + """Human-readable default when user does not pass --experiment-name.""" + slurm = os.environ.get("SLURM_JOB_ID") + if slurm: + job = os.environ.get("SLURM_JOB_NAME", "job") + return f"{job}-{slurm}" + stamp = datetime.now(tz=timezone.utc).strftime("%Y%m%d-%H%M%S") + short = meta.get("git_short_sha") or "unknown" + return f"{short}-{stamp}" + + +def comet_display_name( + explicit: str | None, + meta: dict[str, Any], +) -> str: + """Resolve Comet experiment display name (``name=`` on CometLogger).""" + for key in ("DEEPTREE_EXPERIMENT_NAME", "COMET_EXPERIMENT_NAME"): + v = os.environ.get(key) + if v: + return v + if explicit: + return explicit + return default_experiment_name(meta) diff --git a/src/generate.py b/src/generate.py index 00d120c7a..6774d24cd 100644 --- a/src/generate.py +++ b/src/generate.py @@ -8,12 +8,23 @@ import pandas as pd from src.neon_paths import find_sensor_path, lookup_and_convert, bounds_to_geoindex from src import patches -from distributed import wait from deepforest import main import traceback import warnings warnings.filterwarnings('ignore') + +def _default_deepforest_model(): + """DeepForest 1.x used ``use_release``; 2.x prefers ``load_model`` from Hugging Face.""" + model = main.deepforest() + if hasattr(model, "load_model"): + model.load_model(model_name="weecology/deepforest-tree") + elif hasattr(model, "use_release"): + model.use_release(check_release=False) + else: + raise AttributeError("deepforest model has neither load_model nor use_release") + return model + def predict_trees(deepforest_model, rgb_path, bounds, expand=40): """Predict an rgb path at specific utm bounds Args: @@ -39,7 +50,7 @@ def predict_trees(deepforest_model, rgb_path, bounds, expand=40): #roll to channels last img = np.rollaxis(img, 0,3) - boxes = deepforest_model.predict_image(image = img, return_plot=False) + boxes = deepforest_model.predict_image(image=img) if boxes is None: return boxes @@ -115,8 +126,12 @@ def process_plot(plot_data, rgb_pool, deepforest_model=None): missing_ids = plot_data[~plot_data.individual.isin(merged_boxes.individual)] if not missing_ids.empty: - created_boxes= create_boxes(missing_ids) - merged_boxes = merged_boxes.append(created_boxes) + created_boxes = create_boxes(missing_ids) + merged_boxes = gpd.GeoDataFrame( + pd.concat([merged_boxes, created_boxes], ignore_index=True), + geometry="geometry", + crs=merged_boxes.crs, + ) #If there are multiple boxes per point, take the center box grouped = merged_boxes.groupby("individual") @@ -153,12 +168,10 @@ def process_plot(plot_data, rgb_pool, deepforest_model=None): return merged_boxes, boxes def run(plot, df, savedir, raw_box_savedir, rgb_pool=None, saved_model=None, deepforest_model=None): - """wrapper function for dask, see main.py""" + """Process one plot (used by sequential pipeline and optional SLURM array workers).""" if deepforest_model is None: - from deepforest import main - deepforest_model = main.deepforest() - deepforest_model.use_release(check_release=False) + deepforest_model = _default_deepforest_model() #Filter data and process plot_data = df[df.plotID == plot] @@ -186,53 +199,43 @@ def points_to_crowns( savedir, raw_box_savedir, client=None): - """Prepare NEON field data int + """Prepare NEON field data into crown boxes (sequential; use SLURM array for parallelism). Args: field_data: shp file with location and class of each field collected point rgb_dir: glob to search RGB images savedir: direcory to save predicted bounding boxes raw_box_savedir: directory save all bounding boxes in the image - client: dask client object to use + client: deprecated, ignored (kept for API compatibility) Returns: None: .shp bounding boxes are written to savedir """ + if client is not None: + raise ValueError( + "Dask client support was removed. Run sequentially or use SLURM array jobs " + "(see SLURM/crown_plot_array.sh and src.pipelines.crown_one_plot)." + ) df = gpd.read_file(field_data) plot_names = df.plotID.unique() rgb_pool = glob.glob(rgb_dir, recursive=True) - results = [] - if client: - futures = [] - for plot in plot_names: - future = client.submit( - run, + results = [] + deepforest_model = _default_deepforest_model() + for plot in plot_names: + try: + result = run( plot=plot, df=df, - rgb_pool=rgb_pool, savedir=savedir, - raw_box_savedir=raw_box_savedir + raw_box_savedir=raw_box_savedir, + rgb_pool=rgb_pool, + deepforest_model=deepforest_model, ) - futures.append(future) - - wait(futures) - - for x in futures: - try: - result = x.result() - results.append(result) - except Exception as e: - print(e) - continue - else: - #IMPORTS at runtime due to dask pickling - deepforest_model = main.deepforest() - deepforest_model.use_release(check_release=False) - for plot in plot_names: - try: - result = run(plot=plot, df=df, savedir=savedir, raw_box_savedir=raw_box_savedir, rgb_pool=rgb_pool, deepforest_model=deepforest_model) - results.append(result) - except Exception as e: - print("{} failed with {}".format(plot, e)) + results.append(result) + except Exception as e: + print("{} failed with {}".format(plot, e)) + results = [r for r in results if r is not None] + if not results: + raise ValueError("No plots produced crown predictions; check RGB paths and field data.") results = pd.concat(results) #In case any contrib data has the same CHM and height and sitting in the same deepforest box.Should be rare. @@ -280,13 +283,18 @@ def generate_crops(gdf, sensor_glob, savedir, rgb_glob, client=None, convert_h5= shapefile: a .shp with geometry objects and an taxonID column savedir: path to save image crops img_pool: glob to search remote sensing files. This can be either RGB of .tif hyperspectral data, as long as it can be read by rasterio - client: optional dask client + client: deprecated, ignored (kept for API compatibility; parallelize with SLURM if needed) convert_h5: If HSI data is passed, make sure .tif conversion is complete rgb_glob: glob to search images to match when converting h5s -> tif. HSI_tif_dir: if converting H5 -> tif, where to save .tif files. Only needed if convert_h5 is True Returns: annotations: pandas dataframe of filenames and individual IDs to link with data """ + if client is not None: + raise ValueError( + "Dask client support was removed from generate_crops. " + "Run sequentially or shard work across SLURM tasks." + ) print("There are {} rows in gdf".format(gdf.shape)) gdf = gdf.reset_index(drop=True) @@ -319,56 +327,60 @@ def generate_crops(gdf, sensor_glob, savedir, rgb_glob, client=None, convert_h5= tile_to_path[geo_index] = img_path - filenames = [] - geo_indexes = [] + filenames = [] indexes = [] - if client: - futures = [] - for index, row in gdf.iterrows(): - try: - img_path = tile_to_path[row["geo_index"]] - except: - continue - - for x in img_path: - future = client.submit(write_crop, row=row,img_path=x, savedir=savedir, replace=replace, as_numpy=as_numpy) - futures.append(future) - geo_indexes.append(index) - - wait(futures) - for index, x in enumerate(futures): - try: - filename = x.result() - indexes.append(geo_indexes[index]) - filenames.append(filename) - except: - print("Future failed with {}".format(traceback.print_exc())) - else: - #If no client is passed, loop through each tile and open then once in memory - for geo_index in gdf.geo_index.unique(): - img_path = tile_to_path[geo_index] - - #For each year - for x in img_path: - rasterio_src = rasterio.open(x) - tile_annotations = gdf[gdf.geo_index == geo_index] - - #Write available crops - for index, row in tile_annotations.iterrows(): - try: - filename = write_crop(row=row, savedir=savedir, img_path=x, replace=replace, rasterio_src=rasterio_src, as_numpy=as_numpy) - indexes.append(index) - filenames.append(filename) - except Exception as e: - print("index {} failed with {}".format(index,e)) - continue + for geo_index in gdf.geo_index.unique(): + img_path = tile_to_path[geo_index] + + for x in img_path: + rasterio_src = rasterio.open(x) + tile_annotations = gdf[gdf.geo_index == geo_index] + + for index, row in tile_annotations.iterrows(): + try: + filename = write_crop( + row=row, + savedir=savedir, + img_path=x, + replace=replace, + rasterio_src=rasterio_src, + as_numpy=as_numpy, + ) + indexes.append(index) + filenames.append(filename) + except Exception as e: + print("index {} failed with {}".format(index, e)) + continue annotations = gdf.loc[indexes] print("shape of annotations is {}".format(annotations.shape)) annotations["image_path"] = filenames annotations["tile_year"] = annotations.image_path.apply(lambda x: os.path.splitext(os.path.basename(x))[0].split("_")[-1] ) - annotations = annotations.loc[:,annotations.columns.isin(["individual","geo_index","tile_year","CHM_height","plotID","height","geometry","taxonID","RGB_tile","filename","siteID","image_path","score","box_id"])] + annotations = annotations.loc[ + :, + annotations.columns.isin( + [ + "individual", + "geo_index", + "tile_year", + "CHM_height", + "plotID", + "height", + "geometry", + "taxonID", + "RGB_tile", + "filename", + "siteID", + "image_path", + "score", + "box_id", + ] + ), + ] + if "plotID" not in annotations.columns and "plotID" in gdf.columns: + plot_map = gdf.drop_duplicates(subset=["individual"]).set_index("individual")["plotID"] + annotations["plotID"] = annotations["individual"].map(plot_map) return annotations \ No newline at end of file diff --git a/src/local_smoke_logger.py b/src/local_smoke_logger.py new file mode 100644 index 000000000..e52742c65 --- /dev/null +++ b/src/local_smoke_logger.py @@ -0,0 +1,62 @@ +"""Lightning logger stub for local / CI runs without Comet.""" + +from __future__ import annotations + +from lightning_fabric.utilities import rank_zero_only +from pytorch_lightning.loggers import Logger + + +class _SmokeExperiment: + id = "smoke" + + def get_key(self) -> str: + return "smoke" + + def log_parameter(self, *args, **kwargs): + pass + + def add_tag(self, *args, **kwargs): + pass + + def log_parameters(self, *args, **kwargs): + pass + + def log_table(self, *args, **kwargs): + pass + + def log_metrics(self, *args, **kwargs): + pass + + def log_metric(self, *args, **kwargs): + pass + + def log_confusion_matrix(self, *args, **kwargs): + pass + + +class LocalSmokeLogger(Logger): + """Minimal logger so ``train.py`` can call ``.experiment.*`` without Comet.""" + + def __init__(self) -> None: + super().__init__() + self._experiment = _SmokeExperiment() + + @property + def experiment(self) -> _SmokeExperiment: + return self._experiment + + @property + def name(self) -> str: + return "local_smoke" + + @property + def version(self) -> str: + return "0" + + @rank_zero_only + def log_hyperparams(self, params): + pass + + @rank_zero_only + def log_metrics(self, metrics, step): + pass diff --git a/src/main.py b/src/main.py index e00782ec5..fba9d3b10 100644 --- a/src/main.py +++ b/src/main.py @@ -50,9 +50,14 @@ def __init__(self, model, classes, label_dict, loss_weight=None, config=None, *a self.model = model #Metrics - micro_recall = torchmetrics.Accuracy(average="micro") - macro_recall = torchmetrics.Accuracy(average="macro", num_classes=classes) - top_k_recall = torchmetrics.Accuracy(average="micro",top_k=self.config["top_k"]) + micro_recall = torchmetrics.Accuracy(task="multiclass", num_classes=classes, average="micro") + macro_recall = torchmetrics.Accuracy(task="multiclass", num_classes=classes, average="macro") + top_k_recall = torchmetrics.Accuracy( + task="multiclass", + num_classes=classes, + average="micro", + top_k=self.config["top_k"], + ) self.metrics = torchmetrics.MetricCollection( {"Micro Accuracy":micro_recall, @@ -61,12 +66,12 @@ def __init__(self, model, classes, label_dict, loss_weight=None, config=None, *a }) self.save_hyperparameters(ignore=["loss_weight"]) - - #Weighted loss - if torch.cuda.is_available(): - self.loss_weight = torch.tensor(loss_weight, device="cuda", dtype=torch.float) + + if loss_weight is None: + lw = torch.ones(classes, dtype=torch.float32) else: - self.loss_weight = torch.ones((classes)) + lw = torch.tensor(loss_weight, dtype=torch.float32) + self.register_buffer("loss_weight", lw, persistent=False) def training_step(self, batch, batch_idx): """Train on a loaded dataset @@ -75,7 +80,7 @@ def training_step(self, batch, batch_idx): individual, inputs, y = batch images = inputs["HSI"] y_hat = self.model.forward(images) - loss = F.cross_entropy(y_hat, y, weight=self.loss_weight) + loss = F.cross_entropy(y_hat, y, weight=self.loss_weight) return loss @@ -86,11 +91,10 @@ def validation_step(self, batch, batch_idx): individual, inputs, y = batch images = inputs["HSI"] y_hat = self.model.forward(images) - loss = F.cross_entropy(y_hat, y, weight=self.loss_weight) - - # Log loss and metrics - self.log("val_loss", loss, on_epoch=True) + loss = F.cross_entropy(y_hat, y, weight=self.loss_weight) + self.log("val_loss", loss, on_step=False, on_epoch=True, prog_bar=True) + return loss def on_validation_epoch_end(self): @@ -99,29 +103,36 @@ def on_validation_epoch_end(self): final_micro = torchmetrics.functional.accuracy( preds=torch.tensor(results.pred_label_top1.values), target=torch.tensor(results.label.values), - average="micro") - + task="multiclass", + num_classes=self.classes, + average="micro", + ) + final_macro = torchmetrics.functional.accuracy( preds=torch.tensor(results.pred_label_top1.values), target=torch.tensor(results.label.values), + task="multiclass", + num_classes=self.classes, average="macro", - num_classes=self.classes) + ) - self.log("Epoch Micro Accuracy", final_micro) - self.log("Epoch Macro Accuracy", final_macro) + self.log("Epoch Micro Accuracy", final_micro, on_step=False, on_epoch=True) + self.log("Epoch Macro Accuracy", final_macro, on_step=False, on_epoch=True) # Log results by species taxon_accuracy = torchmetrics.functional.accuracy( preds=torch.tensor(results.pred_label_top1.values), - target=torch.tensor(results.label.values), - average="none", - num_classes=self.classes + target=torch.tensor(results.label.values), + task="multiclass", + num_classes=self.classes, + average="none", ) taxon_precision = torchmetrics.functional.precision( preds=torch.tensor(results.pred_label_top1.values), target=torch.tensor(results.label.values), + task="multiclass", + num_classes=self.classes, average="none", - num_classes=self.classes ) species_table = pd.DataFrame( {"taxonID":self.label_to_index.keys(), @@ -130,23 +141,27 @@ def on_validation_epoch_end(self): }) for key, value in species_table.set_index("taxonID").accuracy.to_dict().items(): - self.log("Epoch_{}_accuracy".format(key), value) - + self.log("Epoch_{}_accuracy".format(key), value, on_step=False, on_epoch=True) + def configure_optimizers(self): optimizer = optim.Adam(self.model.parameters(), lr=self.config["lr"]) - - scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, - mode='min', - factor=0.75, - patience=8, - verbose=True, - threshold=0.0001, - threshold_mode='rel', - cooldown=0, - min_lr=0.0000001, - eps=1e-08) - - return {'optimizer':optimizer, 'lr_scheduler': scheduler,"monitor":'val_loss'} + + scheduler = optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + mode="min", + factor=0.75, + patience=8, + threshold=0.0001, + threshold_mode="rel", + cooldown=0, + min_lr=0.0000001, + eps=1e-08, + ) + + return { + "optimizer": optimizer, + "lr_scheduler": {"scheduler": scheduler, "monitor": "val_loss"}, + } def predict(self,inputs): @@ -288,15 +303,17 @@ def evaluate_crowns(self, data_loader, crowns, points=None, experiment=None): # Log results by species taxon_accuracy = torchmetrics.functional.accuracy( preds=torch.tensor(results.pred_label_top1.values), - target=torch.tensor(results.label.values), - average="none", - num_classes=self.classes + target=torch.tensor(results.label.values), + task="multiclass", + num_classes=self.classes, + average="none", ) taxon_precision = torchmetrics.functional.precision( preds=torch.tensor(results.pred_label_top1.values), target=torch.tensor(results.label.values), + task="multiclass", + num_classes=self.classes, average="none", - num_classes=self.classes ) species_table = pd.DataFrame( {"taxonID":self.label_to_index.keys(), @@ -315,13 +332,18 @@ def evaluate_crowns(self, data_loader, crowns, points=None, experiment=None): site_micro = torchmetrics.functional.accuracy( preds=torch.tensor(group.pred_label_top1.values), target=torch.tensor(group.label.values), - average="micro") - + task="multiclass", + num_classes=self.classes, + average="micro", + ) + site_macro = torchmetrics.functional.accuracy( preds=torch.tensor(group.pred_label_top1.values), target=torch.tensor(group.label.values), + task="multiclass", + num_classes=self.classes, average="macro", - num_classes=self.classes) + ) experiment.log_metric("{}_macro".format(name), site_macro) experiment.log_metric("{}_micro".format(name), site_micro) diff --git a/src/megaplot.py b/src/megaplot.py index b6e9d161d..c6df79913 100644 --- a/src/megaplot.py +++ b/src/megaplot.py @@ -44,8 +44,9 @@ def format(site, gdf, config): else: gdf = buffer_plots(gdf) - #Make sure any points sitting on the line are assigned only to one grid. Rare edge case - gdf = gdf.groupby("individual").apply(lambda x: x.head(1)).reset_index(drop=True) + # Make sure any points sitting on the line are assigned only to one grid. Rare edge case + # (groupby-apply-reset_index drops the grouping column; use drop_duplicates instead.) + gdf = gdf.drop_duplicates(subset=["individual"], keep="first") if "height" in gdf.columns: #Height filter diff --git a/src/models/__init__.py b/src/models/__init__.py index e69de29bb..3cef317aa 100644 --- a/src/models/__init__.py +++ b/src/models/__init__.py @@ -0,0 +1 @@ +"""Model modules for DeepTreeAttention.""" diff --git a/src/models/dead.py b/src/models/dead.py index c17658656..4b47293ec 100644 --- a/src/models/dead.py +++ b/src/models/dead.py @@ -31,15 +31,23 @@ def __init__(self, config): super().__init__() # Model - self.model = models.resnet50(pretrained=True) + from torchvision.models import ResNet50_Weights + + self.model = models.resnet50(weights=ResNet50_Weights.IMAGENET1K_V1) num_ftrs = self.model.fc.in_features self.model.fc = torch.nn.Linear(num_ftrs, 2) - # Metrics - self.accuracy = torchmetrics.Accuracy(average='none', num_classes=2) - self.total_accuracy = torchmetrics.Accuracy() - self.precision_metric = torchmetrics.Precision() - self.metrics = torchmetrics.MetricCollection({"Class Accuracy":self.accuracy, "Accuracy":self.total_accuracy, "Precision":self.precision_metric}) + # Metrics (torchmetrics >= 0.11 requires ``task``) + self.accuracy = torchmetrics.Accuracy(task="multiclass", num_classes=2, average="none") + self.total_accuracy = torchmetrics.Accuracy(task="multiclass", num_classes=2) + self.precision_metric = torchmetrics.Precision(task="multiclass", num_classes=2) + self.metrics = torchmetrics.MetricCollection( + { + "Class Accuracy": self.accuracy, + "Accuracy": self.total_accuracy, + "Precision": self.precision_metric, + } + ) # Data self.config = config @@ -48,12 +56,12 @@ def __init__(self, config): val_dir = os.path.join(self.ROOT,config["dead"]["test_dir"]) self.train_ds = ImageFolder(root=train_dir, transform=get_transform(augment=True)) self.val_ds = ImageFolder(root=val_dir, transform=get_transform(augment=False)) - - def forward(self, x): - output = self.model(x) - output = F.sigmoid(output) - return output + def on_validation_epoch_start(self): + self.metrics.reset() + + def forward(self, x): + return self.model(x) def train_dataloader(self): train_loader = torch.utils.data.DataLoader( @@ -89,7 +97,7 @@ def training_step(self, batch, batch_idx): x,y = batch outputs = self.forward(x) loss = F.cross_entropy(outputs,y) - self.log("train_loss",loss) + self.log("train_loss", loss, on_step=True, on_epoch=True, prog_bar=True) return loss @@ -103,36 +111,32 @@ def validation_step(self, batch, batch_idx): x,y = batch outputs = self(x) loss = F.cross_entropy(outputs,y) - self.log("val_loss",loss) + self.log("val_loss", loss, on_step=False, on_epoch=True, prog_bar=True) metric_dict = self.metrics(outputs, y) - self.log("Alive Accuracy",metric_dict["Class Accuracy"][0]) - self.log("Dead Accuracy",metric_dict["Class Accuracy"][1]) + self.log("Alive Accuracy", metric_dict["Class Accuracy"][0], on_step=False, on_epoch=True) + self.log("Dead Accuracy", metric_dict["Class Accuracy"][1], on_step=False, on_epoch=True) #self.log_dict(metric_dict) return loss - def validation_epoch_end(self, outputs): - val_metrics = self.metrics.compute() - self.log("Alive Accuracy",val_metrics["Class Accuracy"][0]) - self.log("Dead Accuracy",val_metrics["Class Accuracy"][1]) - self.log("Accuracy",val_metrics["Accuracy"]) - self.log("Accuracy",val_metrics["Precision"]) - def configure_optimizers(self): optimizer = torch.optim.Adam(self.parameters(), lr=self.config["dead"]["lr"]) - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, - mode='min', - factor=0.5, - patience=10, - verbose=True, - threshold=0.0001, - threshold_mode='rel', - cooldown=0, - min_lr=0, - eps=1e-08) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + mode="min", + factor=0.5, + patience=10, + threshold=0.0001, + threshold_mode="rel", + cooldown=0, + min_lr=0, + eps=1e-08, + ) - #Monitor rate is val data is used - return {'optimizer':optimizer, 'lr_scheduler': scheduler,"monitor":'val_loss'} + return { + "optimizer": optimizer, + "lr_scheduler": {"scheduler": scheduler, "monitor": "val_loss"}, + } def dataset_confusion(self, loader): """Create a confusion matrix from a data loader""" @@ -174,8 +178,10 @@ def __getitem__(self, index): box = RGB_src.read(window=rasterio.windows.from_bounds(left-1, bottom-1, right+1, top+1, transform=RGB_src.transform)) # Channels last - box = np.rollaxis(box,0,3) - + box = np.rollaxis(box, 0, 3) + if box.ndim != 3 or box.shape[0] < 1 or box.shape[1] < 1: + box = np.zeros((10, 10, 3), dtype=np.float32) + # Preprocess image = self.transform(box) diff --git a/src/models/metadata.py b/src/models/metadata.py index 65ac5024c..20649f868 100644 --- a/src/models/metadata.py +++ b/src/models/metadata.py @@ -73,14 +73,13 @@ def validation_step(self, batch, batch_idx): y_hat = self.model.forward(images, metadata) loss = F.cross_entropy(y_hat, y) - # Log loss and metrics - self.log("val_loss", loss, on_epoch=True) - + self.log("val_loss", loss, on_step=False, on_epoch=True, prog_bar=True) + if not self.training: - y_hat = F.softmax(y_hat, dim = 1) - - output = self.metrics(y_hat, y) - self.log_dict(output) + y_hat = F.softmax(y_hat, dim=1) + + output = self.metrics(y_hat, y) + self.log_dict(output, on_step=False, on_epoch=True) return loss diff --git a/src/models/multi_stage.py b/src/models/multi_stage.py index a8b2bc3e4..7a10db8e4 100644 --- a/src/models/multi_stage.py +++ b/src/models/multi_stage.py @@ -1,485 +1,553 @@ -#Multiple stage model -from functools import reduce -from src.models.year import learned_ensemble -from src.data import TreeDataset -from src import utils - -from pytorch_lightning import LightningModule -import pandas as pd -import math +# Unified single-model hierarchical classifier. import numpy as np -from torch.nn import Module -from torch.nn import functional as F +import pandas as pd +from pytorch_lightning import LightningModule +import torch from torch import nn +from torch.nn import functional as F import torchmetrics -import torch -class base_model(Module): - def __init__(self, years, classes, config): +from src.data import TreeDataset + + +CONIFER_TAXA = {"PICL", "PIEL", "PITA"} + + +class SharedHSIEncoder(nn.Module): + """Lightweight shared encoder used for all years.""" + + def __init__(self, bands: int, embed_dim: int): + super().__init__() + self.conv1 = nn.Sequential( + nn.Conv2d(bands, 32, kernel_size=3, padding=1), + nn.BatchNorm2d(32), + nn.ReLU(inplace=True), + ) + self.conv2 = nn.Sequential( + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + nn.MaxPool2d(2), + ) + self.conv3 = nn.Sequential( + nn.Conv2d(64, 128, kernel_size=3, padding=1), + nn.BatchNorm2d(128), + nn.ReLU(inplace=True), + nn.MaxPool2d(2), + ) + self.proj = nn.Linear(128, embed_dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + x = x.mean(dim=(2, 3)) + return self.proj(x) + + +class UnifiedHierarchicalHead(nn.Module): + """Single model with year-aware aggregation + metadata fusion + multi-head outputs.""" + + def __init__( + self, + bands: int, + n_years: int, + n_sites: int, + n_species: int, + level_dims: list[int], + embed_dim: int = 128, + site_embed_dim: int = 16, + fusion_hidden_dim: int = 256, + ): super().__init__() - #Load from state dict of previous run - self.model = learned_ensemble(classes=classes, years=years, config=config) - - micro_recall = torchmetrics.Accuracy(average="micro") - macro_recall = torchmetrics.Accuracy(average="macro", num_classes=classes) - self.metrics = torchmetrics.MetricCollection( - {"Micro Accuracy":micro_recall, - "Macro Accuracy":macro_recall, - }) - - def forward(self,x): - score = self.model(x) - - return score - + self.n_years = max(1, n_years) + self.unknown_site_index = n_sites + self.encoder = SharedHSIEncoder(bands=bands, embed_dim=embed_dim) + self.year_embedding = nn.Embedding(self.n_years, embed_dim) + self.time_attention = nn.Linear(embed_dim, 1) + self.site_embedding = nn.Embedding(n_sites + 1, site_embed_dim) + self.fusion = nn.Sequential( + nn.Linear(embed_dim + site_embed_dim, fusion_hidden_dim), + nn.ReLU(inplace=True), + nn.Dropout(p=0.2), + ) + + self.species_head = nn.Linear(fusion_hidden_dim, n_species) + self.level_heads = nn.ModuleList( + [nn.Linear(fusion_hidden_dim, d) if d > 0 else nn.Identity() for d in level_dims] + ) + self.level_dims = level_dims + + def forward(self, images: list[torch.Tensor], site_idx: torch.Tensor | None = None) -> dict[str, torch.Tensor]: + year_feats = [] + masks = [] + for year_idx, x in enumerate(images): + feat = self.encoder(x) + year_tensor = torch.full( + (feat.shape[0],), + min(year_idx, self.n_years - 1), + device=feat.device, + dtype=torch.long, + ) + feat = feat + self.year_embedding(year_tensor) + mask = (x.abs().sum(dim=(1, 2, 3)) > 0).float() + year_feats.append(feat) + masks.append(mask) + + feat_stack = torch.stack(year_feats, dim=1) + mask_stack = torch.stack(masks, dim=1) + attn_logits = self.time_attention(feat_stack).squeeze(-1) + attn_logits = attn_logits.masked_fill(mask_stack <= 0, -1e9) + attn = torch.softmax(attn_logits, dim=1) * mask_stack + attn = attn / attn.sum(dim=1, keepdim=True).clamp_min(1e-6) + pooled = (attn.unsqueeze(-1) * feat_stack).sum(dim=1) + + if site_idx is None: + site_idx = torch.full( + (pooled.shape[0],), + self.unknown_site_index, + device=pooled.device, + dtype=torch.long, + ) + else: + site_idx = site_idx.to(pooled.device).long().clamp(0, self.unknown_site_index) + site_feat = self.site_embedding(site_idx) + + fused = self.fusion(torch.cat([pooled, site_feat], dim=1)) + outputs = {"species": self.species_head(fused)} + for level_idx, head in enumerate(self.level_heads): + key = f"level_{level_idx}" + if self.level_dims[level_idx] <= 0: + outputs[key] = torch.empty((fused.shape[0], 0), device=fused.device) + else: + outputs[key] = head(fused) + return outputs + + class MultiStage(LightningModule): - def __init__(self, train_df, test_df, crowns, config, train_mode=True): + """ + Unified hierarchical model in one checkpoint. + Keeps the old MultiStage API used by training/inference code paths. + """ + + def __init__( + self, + train_df=None, + test_df=None, + crowns=None, + config=None, + train_mode=True, + years=None, + classes=None, + species_label_dict=None, + level_label_dicts=None, + n_sites=0, + ): super().__init__() - # Generate each model - self.years = train_df.tile_year.unique() - self.config = config - self.models = nn.ModuleList() - self.species_label_dict = train_df[["taxonID","label"]].drop_duplicates().set_index("taxonID").to_dict()["label"] - self.index_to_label = {v:k for k,v in self.species_label_dict.items()} + self.config = config or {} self.crowns = crowns - self.level_label_dicts = [] - self.label_to_taxonIDs = [] - self.train_df = train_df - self.test_df = test_df - - #hotfix for old naming schema - try: - self.test_df["individual"] = self.test_df["individualID"] + self.train_df = train_df.copy() if train_df is not None else pd.DataFrame() + self.test_df = test_df.copy() if test_df is not None else pd.DataFrame() + + if not self.train_df.empty and "individual" not in self.train_df.columns and "individualID" in self.train_df.columns: self.train_df["individual"] = self.train_df["individualID"] - except: - pass - - if train_mode: - self.train_datasets, self.test_datasets = self.create_datasets() - self.levels = len(self.train_datasets) - - self.classes = len(self.train_df.label.unique()) - for index, ds in enumerate([self.level_0_train, self.level_1_train, self.level_2_train, self.level_3_train, self.level_4_train]): - labels = ds.label - classes = self.num_classes[index] - base = base_model(classes=classes, years=len(self.years), config=self.config) - self.models.append(base) - loss_weight = [] - for x in range(classes): - try: - w = 1/np.sum(labels==x) - except: - w = 1 - loss_weight.append(w) - - loss_weight = np.array(loss_weight/np.max(loss_weight)) - loss_weight[loss_weight < self.config["min_loss_weight"]] = self.config["min_loss_weight"] - loss_weight = torch.tensor(loss_weight, dtype=torch.float) - pname = 'loss_weight_{}'.format(index) - self.register_buffer(pname, loss_weight) - self.save_hyperparameters() - - def create_datasets(self): - #Create levels for each year - ## Level 0 - train_datasets = [] - test_datasets = [] - self.num_classes = [] - self.level_id = [] - self.level_label_dicts.append({"PIPA2":0,"OTHER":1}) - self.label_to_taxonIDs.append({v: k for k, v in self.level_label_dicts[0].items()}) - + if not self.test_df.empty and "individual" not in self.test_df.columns and "individualID" in self.test_df.columns: + self.test_df["individual"] = self.test_df["individualID"] + + if not self.train_df.empty: + self.years = sorted(self.train_df.tile_year.unique()) + self.classes = int(self.train_df.label.nunique()) + self.species_label_dict = ( + self.train_df[["taxonID", "label"]].drop_duplicates().set_index("taxonID")["label"].to_dict() + ) + self.level_label_dicts = self._build_level_maps() + if "site" in self.train_df.columns: + n_sites = int(self.train_df["site"].max()) + 1 + else: + self.years = years or [] + self.classes = int(classes or 0) + self.species_label_dict = species_label_dict or {} + self.level_label_dicts = level_label_dicts or [{"PIPA2": 0, "OTHER": 1}, {"CONIFER": 0, "BROADLEAF": 1}, {}, {}, {}] + self.index_to_label = {v: k for k, v in self.species_label_dict.items()} + self.label_to_taxonIDs = [{v: k for k, v in d.items()} for d in self.level_label_dicts] + level_dims = [len(x) for x in self.level_label_dicts] + + self.model = UnifiedHierarchicalHead( + bands=int(self.config["bands"]), + n_years=max(1, len(self.years)), + n_sites=n_sites, + n_species=self.classes, + level_dims=level_dims, + embed_dim=int(self.config.get("hier_embed_dim", 128)), + site_embed_dim=int(self.config.get("hier_site_embed_dim", 16)), + fusion_hidden_dim=int(self.config.get("hier_fusion_dim", 256)), + ) + + self._val_epoch_outputs = [] + self.train_dataset = None + self.test_dataset = None + self._make_training_views() + + if not self.train_df.empty: + self.train_dataset = TreeDataset(df=self.train_df, config=self.config, train=True) + self.test_dataset = TreeDataset(df=self.test_df, config=self.config, train=True) + counts = self.train_df["label"].value_counts().sort_index() + counts = counts.reindex(range(self.classes), fill_value=1).astype(float) + inv = 1.0 / counts.to_numpy() + inv = inv / np.max(inv) + min_w = float(self.config.get("min_loss_weight", 0.05)) + inv = np.clip(inv, min_w, None) + prior = counts.to_numpy() / counts.to_numpy().sum() + else: + inv = np.ones(max(1, self.classes), dtype=float) + prior = np.ones(max(1, self.classes), dtype=float) / max(1, self.classes) + self.register_buffer("species_loss_weight", torch.tensor(inv, dtype=torch.float32)) + self.register_buffer("species_log_prior", torch.tensor(np.log(prior + 1e-12), dtype=torch.float32)) + + micro = torchmetrics.Accuracy(task="multiclass", num_classes=self.classes, average="micro") + macro = torchmetrics.Accuracy(task="multiclass", num_classes=self.classes, average="macro") + self.metrics = torchmetrics.MetricCollection({"Micro Accuracy": micro, "Macro Accuracy": macro}) + self.save_hyperparameters(ignore=["train_df", "test_df", "crowns"]) + + def _build_level_maps(self) -> list[dict[str, int]]: + taxa = sorted(self.species_label_dict.keys()) + level0 = {"PIPA2": 0, "OTHER": 1} + level1 = {"CONIFER": 0, "BROADLEAF": 1} + + broadleaf = [t for t in taxa if t not in CONIFER_TAXA and t != "PIPA2" and not t.startswith("QU")] + level2 = {t: i for i, t in enumerate(broadleaf)} + level2["OAK"] = len(level2) + + conifer = [t for t in taxa if t in CONIFER_TAXA] + level3 = {t: i for i, t in enumerate(conifer)} + + oak = [t for t in taxa if t.startswith("QU")] + level4 = {t: i for i, t in enumerate(oak)} + return [level0, level1, level2, level3, level4] + + def _make_training_views(self): + if self.train_df.empty or self.test_df.empty: + self.level_0_train = pd.DataFrame() + self.level_1_train = pd.DataFrame() + self.level_2_train = pd.DataFrame() + self.level_3_train = pd.DataFrame() + self.level_4_train = pd.DataFrame() + self.level_0_test = pd.DataFrame() + self.level_1_test = pd.DataFrame() + self.level_2_test = pd.DataFrame() + self.level_3_test = pd.DataFrame() + self.level_4_test = pd.DataFrame() + return + self.level_0_train = self.train_df.copy() - PIPA2 = self.level_0_train[self.level_0_train.taxonID=="PIPA2"] - nonPIPA2 = self.level_0_train[~(self.level_0_train.taxonID=="PIPA2")] - nonPIPA2ids = nonPIPA2.groupby("individual").apply(lambda x: x.head(1)).groupby("taxonID").apply(lambda x: x.head(self.config["other_sampling_ceiling"])).individual - nonPIPA2 = nonPIPA2[nonPIPA2.individual.isin(nonPIPA2ids)] - self.level_0_train = pd.concat([PIPA2, nonPIPA2]) - self.level_0_train.loc[~(self.level_0_train.taxonID == "PIPA2"),"taxonID"] = "OTHER" - - self.level_0_train["label"] = [self.level_label_dicts[0][x] for x in self.level_0_train.taxonID] - self.level_0_train_ds = TreeDataset(df=self.level_0_train, config=self.config) - train_datasets.append(self.level_0_train_ds) - self.num_classes.append(len(self.level_0_train.taxonID.unique())) - + self.level_0_train["taxonID"] = self.level_0_train["taxonID"].where( + self.level_0_train["taxonID"] == "PIPA2", "OTHER" + ) self.level_0_test = self.test_df.copy() - self.level_0_test.loc[~(self.level_0_test.taxonID == "PIPA2"),"taxonID"] = "OTHER" - self.level_0_test["label"]= [self.level_label_dicts[0][x] for x in self.level_0_test.taxonID] - self.level_0_test_ds = TreeDataset(df=self.level_0_test, config=self.config) - test_datasets.append(self.level_0_test_ds) - self.level_id.append(0) - - ## Level 1 - self.level_label_dicts.append({"CONIFER":0,"BROADLEAF":1}) - self.label_to_taxonIDs.append({v: k for k, v in self.level_label_dicts[1].items()}) + self.level_0_test["taxonID"] = self.level_0_test["taxonID"].where( + self.level_0_test["taxonID"] == "PIPA2", "OTHER" + ) + self.level_1_train = self.train_df.copy() - self.level_1_train = self.level_1_train[~(self.level_1_train.taxonID=="PIPA2")] - self.level_1_train.loc[~self.level_1_train.taxonID.isin(["PICL","PIEL","PITA"]),"taxonID"] = "BROADLEAF" - self.level_1_train.loc[self.level_1_train.taxonID.isin(["PICL","PIEL","PITA"]),"taxonID"] = "CONIFER" - - #subsample broadleaf, labels have not been converted, relate to original taxonID - conifer_ids = self.level_1_train[self.level_1_train.taxonID=="CONIFER"].individual - broadleaf_ids = self.level_1_train[self.level_1_train.taxonID=="BROADLEAF"].groupby("label").apply( - lambda x: x.sample(frac=1).groupby( - "individual").apply(lambda x: x.head(1)).head( - math.ceil(len(conifer_ids)/11) - )).individual - ids_to_keep = np.concatenate([broadleaf_ids, conifer_ids]) - self.level_1_train = self.level_1_train[self.level_1_train.individual.isin(ids_to_keep)].reset_index(drop=True) - self.level_1_train["label"] = [self.level_label_dicts[1][x] for x in self.level_1_train.taxonID] - self.level_1_train_ds = TreeDataset(df=self.level_1_train, config=self.config) - train_datasets.append(self.level_1_train_ds) - self.num_classes.append(len(self.level_1_train.taxonID.unique())) - + self.level_1_train["taxonID"] = np.where( + self.level_1_train["taxonID"].isin(CONIFER_TAXA), "CONIFER", "BROADLEAF" + ) self.level_1_test = self.test_df.copy() - self.level_1_test = self.level_1_test[~(self.level_1_test.taxonID=="PIPA2")].reset_index(drop=True) - self.level_1_test.loc[~self.level_1_test.taxonID.isin(["PICL","PIEL","PITA"]),"taxonID"] = "BROADLEAF" - self.level_1_test.loc[self.level_1_test.taxonID.isin(["PICL","PIEL","PITA"]),"taxonID"] = "CONIFER" - self.level_1_test["label"] = [self.level_label_dicts[1][x] for x in self.level_1_test.taxonID] - self.level_1_test_ds = TreeDataset(df=self.level_1_test, config=self.config) - test_datasets.append(self.level_1_test_ds) - self.level_id.append(1) - - ## Level 2 - broadleaf = [x for x in list(self.species_label_dict.keys()) if (not x in ["PICL","PIEL","PITA","PIPA2"]) & (not "QU" in x)] - broadleaf = {v:k for k, v in enumerate(broadleaf)} - broadleaf["OAK"] = len(broadleaf) - self.level_label_dicts.append(broadleaf) - self.label_to_taxonIDs.append({v: k for k, v in broadleaf.items()}) + self.level_1_test["taxonID"] = np.where( + self.level_1_test["taxonID"].isin(CONIFER_TAXA), "CONIFER", "BROADLEAF" + ) + self.level_2_train = self.train_df.copy() - self.level_2_train = self.level_2_train[~self.level_2_train.taxonID.isin(["PICL","PIEL","PITA","PIPA2"])].reset_index(drop=True) - self.level_2_train.loc[self.level_2_train.taxonID.str.contains("QU"),"taxonID"] = "OAK" - - non_oakid = self.level_2_train[~(self.level_2_train.taxonID=="OAK")].individual - oak_ids = self.level_2_train[self.level_2_train.taxonID=="OAK"].groupby("label").apply(lambda x: x.sample(frac=1).head( - int(len(non_oakid)/5)) - ).individual - ids_to_keep = np.concatenate([oak_ids, non_oakid]) - self.level_2_train = self.level_2_train[self.level_2_train.individual.isin(ids_to_keep)].reset_index(drop=True) - self.level_2_train["label"] = [self.level_label_dicts[2][x] for x in self.level_2_train.taxonID] - self.level_2_train_ds = TreeDataset(df=self.level_2_train, config=self.config) - train_datasets.append(self.level_2_train_ds) - self.num_classes.append(len(self.level_2_train.taxonID.unique())) - + self.level_2_train = self.level_2_train[ + ~self.level_2_train["taxonID"].isin(CONIFER_TAXA.union({"PIPA2"})) + ].copy() + self.level_2_train.loc[self.level_2_train["taxonID"].str.startswith("QU"), "taxonID"] = "OAK" self.level_2_test = self.test_df.copy() - self.level_2_test = self.level_2_test[~self.level_2_test.taxonID.isin(["PICL","PIEL","PITA","PIPA2"])].reset_index(drop=True) - self.level_2_test.loc[self.level_2_test.taxonID.str.contains("QU"),"taxonID"] = "OAK" - self.level_2_test["label"] = [self.level_label_dicts[2][x] for x in self.level_2_test.taxonID] - self.level_2_test_ds = TreeDataset(df=self.level_2_test, config=self.config) - test_datasets.append(self.level_2_test_ds) - self.level_id.append(2) - - ## Level 3 - evergreen = [x for x in list(self.species_label_dict.keys()) if x in ["PICL","PIEL","PITA"]] - evergreen = {v:k for k, v in enumerate(evergreen)} - self.level_label_dicts.append(evergreen) - self.label_to_taxonIDs.append({v: k for k, v in self.level_label_dicts[3].items()}) - - self.level_3_train = self.train_df.copy() - self.level_3_train = self.level_3_train[self.level_3_train.taxonID.isin(["PICL","PIEL","PITA"])].reset_index(drop=True) - self.level_3_train = self.level_3_train.groupby("taxonID").apply(lambda x: x.head(self.config["evergreen_ceiling"])).reset_index(drop=True) - self.level_3_train["label"] = [self.level_label_dicts[3][x] for x in self.level_3_train.taxonID] - self.level_3_train_ds = TreeDataset(df=self.level_3_train, config=self.config) - train_datasets.append(self.level_3_train_ds) - self.num_classes.append(len(self.level_3_train.taxonID.unique())) - - self.level_3_test = self.test_df.copy() - self.level_3_test = self.level_3_test[self.level_3_test.taxonID.isin(["PICL","PIEL","PITA"])].reset_index(drop=True) - self.level_3_test["label"] = [self.level_label_dicts[3][x] for x in self.level_3_test.taxonID] - self.level_3_test_ds = TreeDataset(df=self.level_3_test, config=self.config) - test_datasets.append(self.level_3_test_ds) - self.level_id.append(3) - - ## Level 4 - oak = [x for x in list(self.species_label_dict.keys()) if "QU" in x] - self.level_label_dicts.append({v:k for k, v in enumerate(oak)}) - self.label_to_taxonIDs.append({v: k for k, v in self.level_label_dicts[4].items()}) - - #Balance the train in OAKs - self.level_4_train = self.train_df.copy() - self.level_4_train = self.level_4_train[self.level_4_train.taxonID.str.contains("QU")].reset_index(drop=True) - self.level_4_train["label"] = [self.level_label_dicts[4][x] for x in self.level_4_train.taxonID] - ids_to_keep = self.level_4_train.groupby("taxonID").apply( - lambda x: x.sample(frac=1).groupby("individual").apply( - lambda x: x.head(1)).head( - self.config["oaks_sampling_ceiling"])).individual - self.level_4_train = self.level_4_train[self.level_4_train.individual.isin(ids_to_keep)].reset_index(drop=True) - - self.level_4_train_ds = TreeDataset(df=self.level_4_train, config=self.config) - train_datasets.append(self.level_4_train_ds) - self.num_classes.append(len(self.level_4_train.taxonID.unique())) - - self.level_4_test = self.test_df.copy() - self.level_4_test = self.level_4_test[self.level_4_test.taxonID.str.contains("QU")].reset_index(drop=True) - self.level_4_test["label"] = [self.level_label_dicts[4][x] for x in self.level_4_test.taxonID] - self.level_4_test_ds = TreeDataset(df=self.level_4_test, config=self.config) - test_datasets.append(self.level_4_test_ds) - self.level_id.append(4) - - return train_datasets, test_datasets - + self.level_2_test = self.level_2_test[ + ~self.level_2_test["taxonID"].isin(CONIFER_TAXA.union({"PIPA2"})) + ].copy() + self.level_2_test.loc[self.level_2_test["taxonID"].str.startswith("QU"), "taxonID"] = "OAK" + + self.level_3_train = self.train_df[self.train_df["taxonID"].isin(CONIFER_TAXA)].copy() + self.level_3_test = self.test_df[self.test_df["taxonID"].isin(CONIFER_TAXA)].copy() + self.level_4_train = self.train_df[self.train_df["taxonID"].str.startswith("QU")].copy() + self.level_4_test = self.test_df[self.test_df["taxonID"].str.startswith("QU")].copy() + + def _loader_kwargs(self): + workers = int(self.config.get("workers") or 0) + kw = {"num_workers": workers} + if workers > 0: + kw["persistent_workers"] = True + return kw + def train_dataloader(self): - data_loaders = [] - for ds in self.train_datasets: - data_loader = torch.utils.data.DataLoader( - ds, - batch_size=self.config["batch_size"], - shuffle=True, - num_workers=self.config["workers"], - ) - data_loaders.append(data_loader) - - return data_loaders + return torch.utils.data.DataLoader( + self.train_dataset, + batch_size=self.config["batch_size"], + shuffle=True, + **self._loader_kwargs(), + ) def val_dataloader(self): - ## Validation loaders are a list https://github.com/PyTorchLightning/pytorch-lightning/issues/10809 - data_loaders = [] - for ds in self.test_datasets: - data_loader = torch.utils.data.DataLoader( - ds, - batch_size=self.config["batch_size"], - shuffle=False, - num_workers=self.config["workers"], - ) - data_loaders.append(data_loader) - - return data_loaders - + return torch.utils.data.DataLoader( + self.test_dataset, + batch_size=self.config["batch_size"], + shuffle=False, + **self._loader_kwargs(), + ) + def predict_dataloader(self, ds): - data_loader = torch.utils.data.DataLoader( + return torch.utils.data.DataLoader( ds, batch_size=self.config["predict_batch_size"], shuffle=False, - num_workers=self.config["workers"] + **self._loader_kwargs(), + ) + + def _hierarchy_targets(self, labels: torch.Tensor) -> dict[str, torch.Tensor]: + taxa = [self.index_to_label[int(x)] for x in labels.detach().cpu().tolist()] + device = labels.device + out = {} + + level0 = [self.level_label_dicts[0]["PIPA2"] if t == "PIPA2" else self.level_label_dicts[0]["OTHER"] for t in taxa] + out["level_0"] = torch.tensor(level0, dtype=torch.long, device=device) + + level1 = [self.level_label_dicts[1]["CONIFER"] if t in CONIFER_TAXA else self.level_label_dicts[1]["BROADLEAF"] for t in taxa] + out["level_1"] = torch.tensor(level1, dtype=torch.long, device=device) + + level2 = [] + for t in taxa: + if t in CONIFER_TAXA or t == "PIPA2": + level2.append(-100) + elif t.startswith("QU"): + level2.append(self.level_label_dicts[2]["OAK"]) + else: + level2.append(self.level_label_dicts[2].get(t, -100)) + out["level_2"] = torch.tensor(level2, dtype=torch.long, device=device) + + level3 = [self.level_label_dicts[3].get(t, -100) for t in taxa] + out["level_3"] = torch.tensor(level3, dtype=torch.long, device=device) + + level4 = [self.level_label_dicts[4].get(t, -100) for t in taxa] + out["level_4"] = torch.tensor(level4, dtype=torch.long, device=device) + return out + + def _species_loss(self, logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + tau = float(self.config.get("logit_adjustment_tau", 1.0)) + adjusted = logits + (tau * self.species_log_prior).to(logits.device) + return F.cross_entropy(adjusted, labels, weight=self.species_loss_weight.to(logits.device)) + + def _aux_losses(self, outputs: dict[str, torch.Tensor], labels: torch.Tensor) -> torch.Tensor: + targets = self._hierarchy_targets(labels) + total = torch.zeros((), device=labels.device) + for idx in range(5): + key = f"level_{idx}" + logits = outputs[key] + if logits.numel() == 0: + continue + target = targets[key] + if not torch.any(target != -100): + continue + loss = F.cross_entropy(logits, target, ignore_index=-100) + weight = float(self.config.get(f"hier_level_{idx}_weight", 1.0)) + total = total + weight * loss + return total + + def training_step(self, batch, batch_idx): + _, inputs, labels = batch + outputs = self.model(inputs["HSI"], inputs.get("site")) + species_loss = self._species_loss(outputs["species"], labels) + aux_loss = self._aux_losses(outputs, labels) + alpha = float(self.config.get("hier_aux_weight", 0.3)) + loss = species_loss + alpha * aux_loss + self.log("train_species_loss", species_loss, on_step=False, on_epoch=True) + self.log("train_aux_loss", aux_loss, on_step=False, on_epoch=True) + self.log("train_loss", loss, on_step=False, on_epoch=True) + return loss + + def validation_step(self, batch, batch_idx): + _, inputs, labels = batch + outputs = self.model(inputs["HSI"], inputs.get("site")) + species_loss = self._species_loss(outputs["species"], labels) + aux_loss = self._aux_losses(outputs, labels) + alpha = float(self.config.get("hier_aux_weight", 0.3)) + loss = species_loss + alpha * aux_loss + self.log("val_loss", loss, on_step=False, on_epoch=True, prog_bar=True) + + probs = F.softmax(outputs["species"], dim=1) + metrics = self.metrics(probs, labels) + self.log_dict(metrics, on_step=False, on_epoch=True) + self._val_epoch_outputs.append({"probs": probs.detach().cpu(), "labels": labels.detach().cpu()}) + return loss + + def on_validation_epoch_start(self): + self._val_epoch_outputs = [] + + def on_validation_epoch_end(self): + if not self._val_epoch_outputs: + return + probs = torch.cat([x["probs"] for x in self._val_epoch_outputs], dim=0) + labels = torch.cat([x["labels"] for x in self._val_epoch_outputs], dim=0) + preds = torch.argmax(probs, dim=1) + + epoch_micro = torchmetrics.functional.accuracy( + preds=preds, target=labels, task="multiclass", num_classes=self.classes, average="micro" ) + epoch_macro = torchmetrics.functional.accuracy( + preds=preds, target=labels, task="multiclass", num_classes=self.classes, average="macro" + ) + self.log("Epoch Micro Accuracy", epoch_micro, on_step=False, on_epoch=True) + self.log("Epoch Macro Accuracy", epoch_macro, on_step=False, on_epoch=True) + self._val_epoch_outputs = [] - return data_loader - def configure_optimizers(self): - """Create a optimizer for each level""" - optimizers = [] - for x, ds in enumerate(self.train_datasets): - optimizer = torch.optim.Adam(self.models[x].parameters(), lr=self.config["lr_{}".format(x)]) - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, - mode='min', - factor=0.75, - patience=8, - verbose=True, - threshold=0.0001, - threshold_mode='rel', - cooldown=0, - eps=1e-08) - - optimizers.append({'optimizer':optimizer, 'lr_scheduler': {"scheduler":scheduler, "monitor":'val_loss/dataloader_idx_{}'.format(x)}}) - - return optimizers - - def training_step(self, batch, batch_idx, optimizer_idx): - """Calculate train_df loss - """ - #get loss weight - loss_weights = self.__getattr__('loss_weight_'+str(optimizer_idx)) - individual, inputs, y = batch[optimizer_idx] - images = inputs["HSI"] - y_hat = self.models[optimizer_idx].forward(images) - loss = F.cross_entropy(y_hat, y, weight=loss_weights) - self.log("train_loss_{}".format(optimizer_idx),loss, on_epoch=True, on_step=False) - - return loss - - def validation_step(self, batch, batch_idx, dataloader_idx): - """Calculate val loss - """ - loss_weight = self.__getattr__('loss_weight_'+str(dataloader_idx)) - individual, inputs, y = batch - images = inputs["HSI"] - y_hat = self.models[dataloader_idx].forward(images) - loss = F.cross_entropy(y_hat, y, weight=loss_weight) - - self.log("val_loss",loss) - metric_dict = self.models[dataloader_idx].metrics(y_hat, y) - self.log_dict(metric_dict, on_epoch=True, on_step=False) - y_hat = F.softmax(y_hat, dim=1) - - return {"individual":individual, "yhat":y_hat, "label":y} - + optimizer = torch.optim.Adam(self.parameters(), lr=self.config.get("lr", 1e-4)) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + mode="min", + factor=0.75, + patience=8, + threshold=0.0001, + threshold_mode="rel", + cooldown=0, + eps=1e-08, + ) + return {"optimizer": optimizer, "lr_scheduler": {"scheduler": scheduler, "monitor": "val_loss"}} + def predict_step(self, batch, batch_idx): - """Calculate predictions - """ individual, inputs = batch - images = inputs["HSI"] - - y_hats = [] - for model in self.models: - y_hat = model.forward(images) - y_hat = F.softmax(y_hat, dim=1) - y_hats.append(y_hat) - - return individual, y_hats - - def on_predict_epoch_end(self, outputs): - outputs = self.all_gather(outputs) - - def validation_epoch_end(self, validation_step_outputs): - for level, results in enumerate(validation_step_outputs): - yhat = torch.cat([x["yhat"] for x in results]).cpu().numpy() - labels = torch.cat([x["label"] for x in results]).cpu().numpy() - yhat = np.argmax(yhat, 1) - epoch_micro = torchmetrics.functional.accuracy( - preds=torch.tensor(labels), - target=torch.tensor(yhat), - average="micro") - - epoch_macro = torchmetrics.functional.accuracy( - preds=torch.tensor(labels), - target=torch.tensor(yhat), - average="macro", - num_classes=len(self.species_label_dict) - ) - - self.log("Epoch Micro Accuracy level {}".format(level), epoch_micro) - self.log("Epoch Macro Accuracy level {}".format(level), epoch_macro) - - # Log results by species - taxon_accuracy = torchmetrics.functional.accuracy( - preds=torch.tensor(yhat), - target=torch.tensor(labels), - average="none", - num_classes=len(self.level_label_dicts[level]) - ) - taxon_precision = torchmetrics.functional.precision( - preds=torch.tensor(yhat), - target=torch.tensor(labels), - average="none", - num_classes=len(self.level_label_dicts[level]) - ) - species_table = pd.DataFrame( - {"taxonID":self.level_label_dicts[level].keys(), - "accuracy":taxon_accuracy, - "precision":taxon_precision - }) - - for key, value in species_table.set_index("taxonID").accuracy.to_dict().items(): - self.log("Epoch_{}_accuracy".format(key), value) - - for key, value in species_table.set_index("taxonID").precision.to_dict().items(): - self.log("Epoch_{}_precision".format(key), value) - + outputs = self.model(inputs["HSI"], inputs.get("site")) + return { + "individual": np.asarray(individual), + "species_probs": F.softmax(outputs["species"], dim=1).detach().cpu().numpy(), + "level_0_probs": F.softmax(outputs["level_0"], dim=1).detach().cpu().numpy(), + "level_1_probs": F.softmax(outputs["level_1"], dim=1).detach().cpu().numpy(), + "level_2_probs": F.softmax(outputs["level_2"], dim=1).detach().cpu().numpy() if outputs["level_2"].numel() else None, + "level_3_probs": F.softmax(outputs["level_3"], dim=1).detach().cpu().numpy() if outputs["level_3"].numel() else None, + "level_4_probs": F.softmax(outputs["level_4"], dim=1).detach().cpu().numpy() if outputs["level_4"].numel() else None, + } + + @classmethod + def load_from_checkpoint(cls, checkpoint_path, **kwargs): + kwargs.setdefault("weights_only", False) + return super(MultiStage, cls).load_from_checkpoint(checkpoint_path, **kwargs) + def gather_predictions(self, predict_df): - """Post-process the predict method to create metrics""" + """Aggregate predict outputs and expose level columns for compatibility.""" individuals = [] - yhats = [] - levels = [] - + species_probs = [] + level_probs = {f"level_{i}": [] for i in range(5)} for output in predict_df: - for index, level_results in enumerate(output[1]): - batch_individuals = np.stack(output[0]) - for individual, yhat in zip(batch_individuals, level_results): - individuals.append(individual) - yhats.append(yhat) - levels.append(index) - - temporal_average = pd.DataFrame({"individual":individuals,"level":levels,"yhat":yhats}) - - #Argmax and score for each level - predicted_label = temporal_average.groupby(["individual","level"]).yhat.apply( - lambda x: np.argmax(np.vstack(x))).reset_index().pivot( - index=["individual"],columns="level",values="yhat").reset_index() - predicted_label.columns = ["individual","pred_label_top1_level_0","pred_label_top1_level_1", - "pred_label_top1_level_2","pred_label_top1_level_3","pred_label_top1_level_4"] - - predicted_score = temporal_average.groupby(["individual","level"]).yhat.apply( - lambda x: np.vstack(x).max()).reset_index().pivot( - index=["individual"],columns="level",values="yhat").reset_index() - predicted_score.columns = ["individual","top1_score_level_0","top1_score_level_1", - "top1_score_level_2","top1_score_level_3","top1_score_level_4"] - results = pd.merge(predicted_label,predicted_score) - - #Label taxa - for level, label_dict in enumerate(self.label_to_taxonIDs): - results["pred_taxa_top1_level_{}".format(level)] = results["pred_label_top1_level_{}".format(level)].apply(lambda x: label_dict[x]) - - return results - - def ensemble(self, results): - """Given a multi-level model, create a final output prediction and score""" - ensemble_taxonID = [] - ensemble_label = [] - ensemble_score = [] - - for index,row in results.iterrows(): - if row["pred_taxa_top1_level_0"] == "PIPA2": - ensemble_taxonID.append("PIPA2") - ensemble_label.append(self.species_label_dict["PIPA2"]) - ensemble_score.append(row["top1_score_level_0"]) + individuals.extend(list(output["individual"])) + species_probs.append(output["species_probs"]) + for i in range(5): + p = output[f"level_{i}_probs"] + if p is not None: + level_probs[f"level_{i}"].append(p) + + species_probs = np.vstack(species_probs) + results = pd.DataFrame( + { + "individual": np.asarray(individuals), + "pred_label_top1": np.argmax(species_probs, axis=1), + "top1_score": np.max(species_probs, axis=1), + } + ) + results["pred_taxa_top1"] = results["pred_label_top1"].map(self.index_to_label) + + for i in range(5): + key = f"level_{i}" + if level_probs[key]: + probs = np.vstack(level_probs[key]) + results[f"pred_label_top1_level_{i}"] = np.argmax(probs, axis=1) + results[f"top1_score_level_{i}"] = np.max(probs, axis=1) + inv = self.label_to_taxonIDs[i] + results[f"pred_taxa_top1_level_{i}"] = results[f"pred_label_top1_level_{i}"].map(inv) else: - if row["pred_taxa_top1_level_1"] == "BROADLEAF": - if row["pred_taxa_top1_level_2"] == "OAK": - ensemble_taxonID.append(row["pred_taxa_top1_level_4"]) - ensemble_label.append(self.species_label_dict[row["pred_taxa_top1_level_4"]]) - ensemble_score.append(row["top1_score_level_4"]) - else: - ensemble_taxonID.append(row["pred_taxa_top1_level_2"]) - ensemble_label.append(self.species_label_dict[row["pred_taxa_top1_level_2"]]) - ensemble_score.append(row["top1_score_level_2"]) - else: - ensemble_taxonID.append(row["pred_taxa_top1_level_3"]) - ensemble_label.append(self.species_label_dict[row["pred_taxa_top1_level_3"]]) - ensemble_score.append(row["top1_score_level_3"]) - - results["ensembleTaxonID"] = ensemble_taxonID - results["ens_score"] = ensemble_score - results["ens_label"] = ensemble_label - + results[f"pred_label_top1_level_{i}"] = np.nan + results[f"top1_score_level_{i}"] = np.nan + results[f"pred_taxa_top1_level_{i}"] = None + return results - - def evaluation_scores(self, ensemble_df, experiment): - ensemble_df = ensemble_df.groupby("individual").apply(lambda x: x.head(1)) - + + def ensemble(self, results): + """Single-pass species head output, while keeping legacy column names.""" + out = results.copy() + if "pred_taxa_top1" in out.columns: + out["ensembleTaxonID"] = out["pred_taxa_top1"] + out["ens_label"] = out["pred_label_top1"] + out["ens_score"] = out["top1_score"] + return out + + # Fallback for externally-built legacy tables. + out["ensembleTaxonID"] = out.get("pred_taxa_top1_level_2") + out["ens_score"] = out.get("top1_score_level_2") + out["ens_label"] = out["ensembleTaxonID"].map(self.species_label_dict) + return out + + def evaluation_scores(self, ensemble_df, experiment): + ensemble_df = ensemble_df.drop_duplicates(subset=["individual"], keep="first") + n_cls = len(self.species_label_dict) + ed = ensemble_df.dropna(subset=["ens_label", "label"]) + ed = ed[ + (ed["ens_label"] >= 0) + & (ed["ens_label"] < n_cls) + & (ed["label"] >= 0) + & (ed["label"] < n_cls) + ] + if ed.empty: + return ensemble_df + + preds = torch.tensor(ed["ens_label"].values, dtype=torch.long) + target = torch.tensor(ed["label"].values, dtype=torch.long) taxon_accuracy = torchmetrics.functional.accuracy( - preds=torch.tensor(ensemble_df.ens_label.values), - target=torch.tensor(ensemble_df.label.values), + preds=preds, + target=target, + task="multiclass", + num_classes=n_cls, average="none", - num_classes=len(self.species_label_dict) ) - taxon_precision = torchmetrics.functional.precision( - preds=torch.tensor(ensemble_df.ens_label.values), - target=torch.tensor(ensemble_df.label.values), + preds=preds, + target=target, + task="multiclass", + num_classes=n_cls, average="none", - num_classes=len(self.species_label_dict) - ) - - taxon_labels = list(self.species_label_dict) - taxon_labels.sort() + ) + + taxon_labels = sorted(self.species_label_dict.keys(), key=lambda t: self.species_label_dict[t]) species_table = pd.DataFrame( - {"taxonID":taxon_labels, - "accuracy":taxon_accuracy, - "precision":taxon_precision - }) - - if experiment: - experiment.log_metrics(species_table.set_index("taxonID").accuracy.to_dict(),prefix="accuracy") - experiment.log_metrics(species_table.set_index("taxonID").precision.to_dict(),prefix="precision") - - # Log result by site + {"taxonID": taxon_labels, "accuracy": taxon_accuracy, "precision": taxon_precision} + ) if experiment: - site_data_frame =[] - for name, group in ensemble_df.groupby("siteID"): - site_micro = np.sum(group.ens_label.values == group.label.values)/len(group.ens_label.values) - + experiment.log_metrics(species_table.set_index("taxonID").accuracy.to_dict(), prefix="accuracy") + experiment.log_metrics(species_table.set_index("taxonID").precision.to_dict(), prefix="precision") + + if experiment and "siteID" in ed.columns: + site_data_frame = [] + for name, group in ed.groupby("siteID"): + g = group[ + (group["ens_label"] >= 0) + & (group["ens_label"] < n_cls) + & (group["label"] >= 0) + & (group["label"] < n_cls) + ] + if g.empty: + continue + site_micro = np.sum(g.ens_label.values == g.label.values) / len(g.ens_label.values) site_macro = torchmetrics.functional.accuracy( - preds=torch.tensor(group.ens_label.values), - target=torch.tensor(group.label.values), + preds=torch.tensor(g["ens_label"].values, dtype=torch.long), + target=torch.tensor(g["label"].values, dtype=torch.long), + task="multiclass", + num_classes=n_cls, average="macro", - num_classes=len(self.species_label_dict)) - + ) experiment.log_metric("{}_macro".format(name), site_macro) - experiment.log_metric("{}_micro".format(name), site_micro) - - row = pd.DataFrame({"Site":[name], "Micro Recall": [site_micro], "Macro Recall": [site_macro]}) + experiment.log_metric("{}_micro".format(name), site_micro) + row = pd.DataFrame({"Site": [name], "Micro Recall": [site_micro], "Macro Recall": [site_macro]}) site_data_frame.append(row) - site_data_frame = pd.concat(site_data_frame) - experiment.log_table("site_results.csv", site_data_frame) - + if site_data_frame: + site_data_frame = pd.concat(site_data_frame) + experiment.log_table("site_results.csv", site_data_frame) + return ensemble_df \ No newline at end of file diff --git a/src/multinomial.py b/src/multinomial.py index 9bfca3a7f..fc57621d0 100644 --- a/src/multinomial.py +++ b/src/multinomial.py @@ -6,7 +6,7 @@ import traceback import math -from distributed import wait +from concurrent.futures import ThreadPoolExecutor, as_completed def run(tile, confusion_path="data/processed/confusion_matrix.csv", overlay_bounds="/home/b.weinstein/DeepTreeAttention/data/raw/OSBSBoundary/OSBS_boundary.shp", iteration=0): """Load a shapefile and confusion .csv and sample the confidence probabilities""" @@ -76,23 +76,24 @@ def sample_confusion(taxonID, confusion): return np.argmax(random_draw) -def wrapper(client, iteration, experiment_key, shp_dir="/blue/ewhite/b.weinstein/DeepTreeAttention/results/", savedir="/blue/ewhite/b.weinstein/DeepTreeAttention/results"): +def wrapper( + iteration, + experiment_key, + shp_dir="/blue/ewhite/b.weinstein/DeepTreeAttention/results/", + savedir="/blue/ewhite/b.weinstein/DeepTreeAttention/results", + max_workers=16, +): tiles = glob.glob("{}/{}/*_image.shp".format(shp_dir, experiment_key)) total_counts = pd.Series() - counts = [] - for tile in tiles: - future = client.submit(run, tile=tile, iteration=iteration) - counts.append(future) - - wait(counts) - - for result in counts: - try: - ser = result.result() - except Exception as e: - traceback.print_exc(e) - print(e) - continue - total_counts = total_counts.add(ser, fill_value=0) + with ThreadPoolExecutor(max_workers=max_workers) as ex: + futures = [ex.submit(run, tile=tile, iteration=iteration) for tile in tiles] + for fut in as_completed(futures): + try: + ser = fut.result() + except Exception as e: + traceback.print_exc() + print(e) + continue + total_counts = total_counts.add(ser, fill_value=0) total_counts.sort_values() total_counts.to_csv("{}/{}/multinomial_permutation_{}.csv".format(savedir, experiment_key, iteration)) \ No newline at end of file diff --git a/src/neon_download.py b/src/neon_download.py new file mode 100644 index 000000000..e5aae9fec --- /dev/null +++ b/src/neon_download.py @@ -0,0 +1,88 @@ +"""NEON AOP downloads via ``neonutilities`` (Python port of R neonUtilities).""" +from __future__ import annotations + + +import os +from typing import Iterable, List, Sequence + +import geopandas as gpd + +try: + import neonutilities as nu +except ImportError as exc: # pragma: no cover - optional until uv sync + nu = None + _IMPORT_ERROR = exc +else: + _IMPORT_ERROR = None + + +def _require_neonutilities(): + if nu is None: + raise ImportError( + "The neonutilities package is required for downloads. " + "Install project dependencies (e.g. uv sync) and retry." + ) from _IMPORT_ERROR + + +def easting_northing_from_geodataframe(gdf: gpd.GeoDataFrame) -> tuple[List[float], List[float]]: + """Extract easting/northing lists in the GeoDataFrame CRS (expected UTM for NEON AOP).""" + if gdf.crs is None: + raise ValueError("GeoDataFrame has no CRS; cannot derive tile coordinates.") + xs = gdf.geometry.x.astype(float).tolist() + ys = gdf.geometry.y.astype(float).tolist() + return xs, ys + + +def by_tile_aop( + dpid: str, + site: str, + year: int, + easting: Sequence[float], + northing: Sequence[float], + savepath: str, + buffer: int = 0, + token: str | None = None, + check_size: bool = False, +) -> None: + """Download all AOP tiles intersecting the given coordinates (wraps ``neonutilities.by_tile_aop``).""" + _require_neonutilities() + os.makedirs(savepath, exist_ok=True) + nu.by_tile_aop( + dpid=dpid, + site=site, + year=year, + easting=list(easting), + northing=list(northing), + buffer=buffer, + savepath=savepath, + token=token, + check_size=check_size, + ) + + +def download_products_for_points( + gdf: gpd.GeoDataFrame, + site: str, + years: Iterable[int], + save_root: str, + products: dict[str, str], + buffer: int = 0, + token: str | None = None, + check_size: bool = False, +) -> None: + """For each year and each product id, run ``by_tile_aop`` using all stem coordinates in ``gdf``.""" + easting, northing = easting_northing_from_geodataframe(gdf) + for year in years: + for label, dpid in products.items(): + target = os.path.join(save_root, site, str(year), label) + by_tile_aop( + dpid=dpid, + site=site, + year=int(year), + easting=easting, + northing=northing, + savepath=target, + buffer=buffer, + token=token, + check_size=check_size, + ) diff --git a/src/pipelines/__init__.py b/src/pipelines/__init__.py new file mode 100644 index 000000000..b94d5e4a5 --- /dev/null +++ b/src/pipelines/__init__.py @@ -0,0 +1 @@ +"""Inference and other runnable pipelines.""" diff --git a/src/pipelines/crown_one_plot.py b/src/pipelines/crown_one_plot.py new file mode 100644 index 000000000..4ba0bd166 --- /dev/null +++ b/src/pipelines/crown_one_plot.py @@ -0,0 +1,40 @@ +"""Run ``generate.run`` for a single plot (one SLURM array task).""" +from __future__ import annotations + +import argparse +import glob +import os + +import geopandas as gpd + +from src import generate + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser(description="DeepForest crown boxes for one NEON plotID.") + parser.add_argument("--canopy-points", required=True, help="Path to canopy_points.shp (or .gpkg).") + parser.add_argument("--plot", required=True, help="plotID value to process.") + parser.add_argument("--rgb-glob", required=True, help="Recursive glob for RGB tiles (same as config rgb_sensor_pool).") + parser.add_argument("--savedir", required=True, help="Directory for merged stem boxes (one shapefile per plot).") + parser.add_argument("--raw-box-savedir", required=True, help="Directory for all detector boxes.") + args = parser.parse_args(argv) + + df = gpd.read_file(args.canopy_points) + rgb_pool = glob.glob(args.rgb_glob, recursive=True) + if not rgb_pool: + raise FileNotFoundError("No RGB rasters matched --rgb-glob; check paths and config.") + + os.makedirs(args.savedir, exist_ok=True) + os.makedirs(args.raw_box_savedir, exist_ok=True) + + generate.run( + plot=args.plot, + df=df, + rgb_pool=rgb_pool, + savedir=args.savedir, + raw_box_savedir=args.raw_box_savedir, + ) + + +if __name__ == "__main__": + main() diff --git a/src/pipelines/download_neon_aop.py b/src/pipelines/download_neon_aop.py new file mode 100644 index 000000000..364370cd8 --- /dev/null +++ b/src/pipelines/download_neon_aop.py @@ -0,0 +1,63 @@ +"""CLI: download NEON AOP tiles overlapping field stem points using neonutilities.""" +from __future__ import annotations + +import argparse +import os + +import geopandas as gpd + +from src import utils +from src.neon_download import download_products_for_points + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser( + description="Download NEON AOP products by tile for coordinates in a point file." + ) + parser.add_argument("--config", default="config.yml", help="YAML config (uses neon_download section).") + parser.add_argument( + "--points", + help="Override points path (GeoPackage/Shapefile). Defaults to config data_layout.canopy_points_shp or interim path.", + ) + args = parser.parse_args(argv) + + config = utils.read_config(args.config) + nd = config.get("neon_download") or {} + layout = config.get("data_layout") or {} + + points_path = args.points or layout.get("canopy_points_shp") + if not points_path: + raise ValueError( + "No --points given and data_layout.canopy_points_shp missing in config. " + "Run filtering once to write canopy_points.shp, or pass --points explicitly." + ) + + site = nd.get("site") or "OSBS" + years = nd.get("years") or [2021] + buffer = int(nd.get("buffer_m", 50)) + check_size = bool(nd.get("check_size", False)) + products = nd.get("products") or { + "rgb": "DP3.30010.001", + "hsi": "DP3.30006.001", + "chm": "DP3.30015.001", + } + save_root = nd.get("save_root") or layout.get("sensor_download_root") or "data/external/neon_aop" + + token_env = nd.get("token_env", "NEON_API_TOKEN") + token = os.environ.get(token_env) + + gdf = gpd.read_file(points_path) + download_products_for_points( + gdf=gdf, + site=site, + years=years, + save_root=save_root, + products=products, + buffer=buffer, + token=token, + check_size=check_size, + ) + + +if __name__ == "__main__": + main() diff --git a/src/pipelines/merge_crown_boxes.py b/src/pipelines/merge_crown_boxes.py new file mode 100644 index 000000000..fb2c610cd --- /dev/null +++ b/src/pipelines/merge_crown_boxes.py @@ -0,0 +1,34 @@ +"""Merge per-plot ``*_boxes.shp`` outputs from SLURM workers into a single crowns GeoDataFrame file.""" +from __future__ import annotations + +import argparse +import glob +import os + +import geopandas as gpd +import pandas as pd + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser( + description="Concatenate plot-level crown box shapefiles into crowns.shp for TreeData (replace=False path)." + ) + parser.add_argument("--boxes-dir", required=True, help="Directory containing _boxes.shp merged stem boxes.") + parser.add_argument("--out", required=True, help="Output path, e.g. data/interim/run/crowns.shp") + args = parser.parse_args(argv) + + paths = sorted(glob.glob(os.path.join(args.boxes_dir, "*_boxes.shp"))) + if not paths: + raise FileNotFoundError("No *_boxes.shp files found in --boxes-dir.") + + parts = [gpd.read_file(p) for p in paths] + merged = gpd.GeoDataFrame(pd.concat(parts, ignore_index=True), crs=parts[0].crs) + merged = merged.drop_duplicates(subset=["plotID", "box_id"], keep="first") + out_dir = os.path.dirname(os.path.abspath(args.out)) + if out_dir: + os.makedirs(out_dir, exist_ok=True) + merged.to_file(args.out) + + +if __name__ == "__main__": + main() diff --git a/src/pipelines/osbs_inference.py b/src/pipelines/osbs_inference.py new file mode 100644 index 000000000..8f3435e83 --- /dev/null +++ b/src/pipelines/osbs_inference.py @@ -0,0 +1,240 @@ +"""OSBS RGB tile inference: detect crowns, build HSI crops, run species MultiStage checkpoint. + +All settings live under ``inference_osbs`` in ``config.yml``. Run: + + python -m src.pipelines.osbs_inference --config config.yml + +Or from repo root after install: + + deeptree-infer-osbs --config config.yml +""" + +from __future__ import annotations + +import argparse +import glob +import json +import os +from datetime import datetime, timezone +from typing import Any + +import geopandas as gpd +import rasterio +from pytorch_lightning import Trainer +from shapely.geometry import box + +from src import utils +from src.models import multi_stage + + +def _resolve_path(path: str, anchor_dir: str) -> str: + if os.path.isabs(path): + return path + return os.path.abspath(os.path.join(anchor_dir, path)) + + +def load_aoi(aoi_path: str) -> gpd.GeoDataFrame: + aoi = gpd.read_file(aoi_path) + if aoi.empty: + raise ValueError(f"AOI is empty: {aoi_path}") + return aoi + + +def list_osbs_tiles_intersecting_aoi( + *, + rgb_sensor_pool: str, + site: str, + year: int, + aoi: gpd.GeoDataFrame, +) -> list[str]: + """Return RGB tile paths for ``site`` and ``year`` that intersect the AOI polygon.""" + candidates = glob.glob(rgb_sensor_pool, recursive=True) + year_token = f"/{year}/" + filtered = [ + p + for p in candidates + if site in p and year_token in p and "neon-aop-products" not in p + ] + aoi_union = aoi.geometry.union_all() + selected: list[str] = [] + for tile_path in filtered: + with rasterio.open(tile_path) as src: + tile_geom = gpd.GeoSeries([box(*src.bounds)], crs=src.crs) + tile_geom = tile_geom.to_crs(aoi.crs) + if tile_geom.iloc[0].intersects(aoi_union): + selected.append(tile_path) + return selected + + +def _require_inference_block(config: dict[str, Any]) -> dict[str, Any]: + block = config.get("inference_osbs") + if not block: + raise ValueError( + "config.yml must define an `inference_osbs` mapping " + "(year, species_checkpoint, aoi_path, results_root)." + ) + for key in ("year", "species_checkpoint", "aoi_path", "results_root"): + if key not in block or block[key] in (None, ""): + raise ValueError(f"inference_osbs.{key} is required") + return block + + +def run_osbs_inference(*, config_path: str) -> None: + config_path = os.path.abspath(config_path) + anchor_dir = os.path.dirname(config_path) + config = utils.read_config(config_path=config_path) + inf = _require_inference_block(config) + + from src import predict as predict_module + + site = inf.get("site", "OSBS") + year = int(inf["year"]) + species_ckpt = _resolve_path(str(inf["species_checkpoint"]), anchor_dir) + aoi_path = _resolve_path(str(inf["aoi_path"]), anchor_dir) + results_root = _resolve_path(str(inf["results_root"]), anchor_dir) + tile_limit = inf.get("tile_limit") + predict_limit_batches = inf.get("predict_limit_batches") + dead_model_path = inf.get("dead_model_path") or None + filter_dead = bool(inf.get("filter_dead", True)) + + print("[osbs_inference] config file:", config_path) + print("[osbs_inference] site:", site, "year:", year) + print("[osbs_inference] species checkpoint:", species_ckpt) + print("[osbs_inference] AOI:", aoi_path) + print("[osbs_inference] results root:", results_root) + + if not os.path.isfile(species_ckpt): + raise FileNotFoundError(f"Species checkpoint not found: {species_ckpt}") + + aoi = load_aoi(aoi_path) + tiles = list_osbs_tiles_intersecting_aoi( + rgb_sensor_pool=config["rgb_sensor_pool"], + site=site, + year=year, + aoi=aoi, + ) + print(f"[osbs_inference] tiles intersecting AOI (before limit): {len(tiles)}") + if tile_limit is not None: + tiles = tiles[: int(tile_limit)] + print(f"[osbs_inference] applied tile_limit={tile_limit}; processing {len(tiles)} tile(s)") + + crowns_dir = os.path.join(results_root, "crowns") + pred_dir = os.path.join(results_root, "predictions") + os.makedirs(crowns_dir, exist_ok=True) + os.makedirs(pred_dir, exist_ok=True) + + print("[osbs_inference] loading MultiStage once from checkpoint …") + config["pretrained_state_dict"] = None + model = multi_stage.MultiStage.load_from_checkpoint(species_ckpt, config=config) + acc, dev = utils.trainer_accelerator_devices(config) + tr_kw: dict = { + "accelerator": acc, + "devices": dev, + "logger": False, + "enable_checkpointing": False, + } + if predict_limit_batches is not None: + tr_kw["limit_predict_batches"] = int(predict_limit_batches) + trainer = Trainer(**tr_kw) + + run_log: list[dict[str, Any]] = [] + for index, rgb_path in enumerate(tiles, start=1): + basename = os.path.splitext(os.path.basename(rgb_path))[0] + print(f"\n[osbs_inference] --- tile {index}/{len(tiles)}: {basename} ---") + print("[osbs_inference] RGB path:", rgb_path) + + try: + crowns = predict_module.find_crowns( + rgb_path, config, dead_model_path=dead_model_path + ) + except Exception as exc: + print(f"[osbs_inference] ERROR find_crowns: {exc}") + run_log.append({"tile": rgb_path, "stage": "detection", "ok": False, "error": str(exc)}) + continue + + if crowns is None or crowns.empty: + print("[osbs_inference] no crowns; skipping tile") + run_log.append({"tile": rgb_path, "stage": "detection", "ok": True, "crowns": 0}) + continue + + aoi_union = aoi.geometry.union_all() + crowns = crowns.to_crs(aoi.crs) + n_before = len(crowns) + crowns = crowns[crowns.geometry.intersects(aoi_union)] + print(f"[osbs_inference] crowns after AOI clip: {len(crowns)} (was {n_before})") + if crowns.empty: + print("[osbs_inference] no crowns inside AOI; skipping tile") + run_log.append({"tile": rgb_path, "stage": "aoi_clip", "ok": True, "crowns": 0}) + continue + + crown_shp = os.path.join(crowns_dir, f"{basename}.shp") + crowns.to_file(crown_shp) + print("[osbs_inference] wrote crowns:", crown_shp) + + crop_dir = os.path.join(results_root, "crops", basename) + os.makedirs(crop_dir, exist_ok=True) + config["prediction_crop_dir"] = crop_dir + + try: + print("[osbs_inference] generating prediction crops (HSI windows) …") + crown_ann_path = predict_module.generate_prediction_crops(crowns, config) + except Exception as exc: + print(f"[osbs_inference] ERROR generate_prediction_crops: {exc}") + run_log.append({"tile": rgb_path, "stage": "crops", "ok": False, "error": str(exc)}) + continue + + try: + print("[osbs_inference] running species predict_tile …") + trees = predict_module.predict_tile( + crown_annotations=crown_ann_path, + m=model, + trainer=trainer, + filter_dead=filter_dead, + savedir=pred_dir, + config=config, + ) + n_trees = 0 if trees is None else len(trees) + print(f"[osbs_inference] finished tile; trees written: {n_trees}") + run_log.append( + {"tile": rgb_path, "stage": "species", "ok": True, "trees": n_trees} + ) + except Exception as exc: + print(f"[osbs_inference] ERROR predict_tile: {exc}") + run_log.append({"tile": rgb_path, "stage": "species", "ok": False, "error": str(exc)}) + + meta_path = os.path.join(results_root, "run_metadata.json") + with open(meta_path, "w", encoding="utf-8") as f: + json.dump( + { + "created_at_utc": datetime.now(tz=timezone.utc).isoformat(), + "config_path": config_path, + "site": site, + "year": year, + "species_checkpoint": species_ckpt, + "tiles_total": len(tiles), + "per_tile": run_log, + }, + f, + indent=2, + ) + print(f"\n[osbs_inference] wrote run metadata: {meta_path}") + print("[osbs_inference] done.") + + +def build_parser() -> argparse.ArgumentParser: + p = argparse.ArgumentParser(description="Run OSBS tile inference (detection + species).") + p.add_argument( + "--config", + default="config.yml", + help="Path to config.yml (must define inference_osbs).", + ) + return p + + +def main() -> None: + args = build_parser().parse_args() + run_osbs_inference(config_path=args.config) + + +if __name__ == "__main__": + main() diff --git a/src/pipelines/osbs_mortality.py b/src/pipelines/osbs_mortality.py new file mode 100644 index 000000000..09081aee7 --- /dev/null +++ b/src/pipelines/osbs_mortality.py @@ -0,0 +1,622 @@ +"""Compare OSBS alive/dead tree counts between RGB years at NEON tile scale. + +Run from the repository root after configuring ``osbs_mortality`` in +``config.yml``: + + python -m src.pipelines.osbs_mortality --config config.yml + +The pipeline can download AOI-intersecting RGB tiles, run the existing +DeepForest detector plus Hugging Face alive/dead cropmodel, optionally run the +species model, then write per-tile count tables and GeoTIFF rasters. +""" + +from __future__ import annotations + +import argparse +import inspect +import json +import math +import os +import re +from datetime import datetime, timezone +from typing import Any, Iterable, Sequence + +import geopandas as gpd +import numpy as np +import pandas as pd +import rasterio +from pytorch_lightning import Trainer +from rasterio import features +from rasterio.transform import from_origin +from shapely.geometry import box + +from src import utils +from src.models import multi_stage +from src.pipelines.osbs_inference import load_aoi, list_osbs_tiles_intersecting_aoi + +RGB_DPID = "DP3.30010.001" +DEFAULT_SITE = "OSBS" +DEFAULT_AOP_EPSG = 32617 +DEFAULT_TILE_SIZE_M = 1000 +NO_DATA = -9999.0 +_TILE_ID_RE = re.compile(r"_(?P\d{6})_(?P\d{7})_image(?:_|\.|$)") + + +def _resolve_path(path: str, anchor_dir: str) -> str: + if os.path.isabs(path): + return path + return os.path.abspath(os.path.join(anchor_dir, path)) + + +def parse_tile_id(path: str) -> str: + """Return the NEON AOP easting/northing tile id from an RGB filename.""" + match = _TILE_ID_RE.search(os.path.basename(path)) + if not match: + raise ValueError(f"Could not parse NEON tile id from filename: {path}") + return f"{match.group('easting')}_{match.group('northing')}" + + +def tile_geometry_from_raster(path: str): + with rasterio.open(path) as src: + return box(*src.bounds), src.crs + + +def _require_mortality_block(config: dict[str, Any]) -> dict[str, Any]: + block = config.get("osbs_mortality") + if not block: + raise ValueError( + "config.yml must define an `osbs_mortality` mapping " + "(years, aoi_path, results_root)." + ) + for key in ("years", "aoi_path", "results_root"): + if key not in block or block[key] in (None, "", []): + raise ValueError(f"osbs_mortality.{key} is required") + if len(block["years"]) < 2: + raise ValueError("osbs_mortality.years must include at least two years") + return block + + +def _as_dead_mask(crowns: gpd.GeoDataFrame, *, dead_threshold: float) -> pd.Series: + if "dead_label" not in crowns.columns: + return pd.Series(False, index=crowns.index) + + labels = crowns["dead_label"] + numeric = pd.to_numeric(labels, errors="coerce") + label_dead = numeric.eq(1) + string_dead = labels.astype("string").str.lower().isin({"dead", "dead_tree", "1", "true"}) + label_dead = label_dead | string_dead.fillna(False) + + if "dead_score" not in crowns.columns: + return label_dead + + scores = pd.to_numeric(crowns["dead_score"], errors="coerce") + score_ok = scores.isna() | scores.ge(dead_threshold) + return label_dead & score_ok + + +def summarize_tile_crowns( + crowns: gpd.GeoDataFrame, + *, + tile_path: str, + year: int, + dead_threshold: float, +) -> dict[str, Any]: + """Count alive/dead detections for one tile without linking individual trees.""" + dead_mask = _as_dead_mask(crowns, dead_threshold=dead_threshold) + unknown_mask = ( + crowns["dead_label"].isna() + if "dead_label" in crowns.columns + else pd.Series(True, index=crowns.index) + ) + total_count = int(len(crowns)) + dead_count = int(dead_mask.sum()) + unknown_count = int(unknown_mask.sum()) + alive_count = int(total_count - dead_count - unknown_count) + if alive_count < 0: + alive_count = 0 + + return { + "tile_id": parse_tile_id(tile_path), + "year": int(year), + "tile_path": tile_path, + "total_count": total_count, + "alive_count": alive_count, + "dead_count": dead_count, + "unknown_count": unknown_count, + "dead_fraction": dead_count / total_count if total_count else 0.0, + } + + +def compare_year_counts( + counts: gpd.GeoDataFrame, + *, + baseline_year: int, + comparison_year: int, + high_mortality_dead_change: int, +) -> gpd.GeoDataFrame: + """Build one row per tile comparing count changes between two years.""" + if counts.empty: + return gpd.GeoDataFrame(geometry=[], crs=counts.crs) + + baseline = counts[counts["year"] == baseline_year].set_index("tile_id") + comparison = counts[counts["year"] == comparison_year].set_index("tile_id") + tile_ids = sorted(set(baseline.index) | set(comparison.index)) + rows: list[dict[str, Any]] = [] + geometries = [] + metrics = ("total_count", "alive_count", "dead_count", "unknown_count", "dead_fraction") + + for tile_id in tile_ids: + row: dict[str, Any] = { + "tile_id": tile_id, + "baseline_year": int(baseline_year), + "comparison_year": int(comparison_year), + } + base_row = baseline.loc[tile_id] if tile_id in baseline.index else None + comp_row = comparison.loc[tile_id] if tile_id in comparison.index else None + + for metric in metrics: + base_value = base_row[metric] if base_row is not None else np.nan + comp_value = comp_row[metric] if comp_row is not None else np.nan + row[f"{metric}_{baseline_year}"] = base_value + row[f"{metric}_{comparison_year}"] = comp_value + row[f"{metric}_change"] = ( + comp_value - base_value + if not pd.isna(base_value) and not pd.isna(comp_value) + else np.nan + ) + + row["high_mortality"] = bool( + not pd.isna(row["dead_count_change"]) + and row["dead_count_change"] >= high_mortality_dead_change + ) + rows.append(row) + geometry_row = comp_row if comp_row is not None else base_row + geometries.append(geometry_row.geometry) + + return gpd.GeoDataFrame(rows, geometry=geometries, crs=counts.crs) + + +def write_metric_raster( + tile_metrics: gpd.GeoDataFrame, + *, + metric: str, + output_path: str, + resolution: int = DEFAULT_TILE_SIZE_M, + nodata: float = NO_DATA, +) -> str: + """Rasterize a tile-level metric, using one value for each NEON tile polygon.""" + if tile_metrics.empty: + raise ValueError("Cannot write raster from empty tile metrics") + if metric not in tile_metrics.columns: + raise ValueError(f"Metric column not found: {metric}") + + valid = tile_metrics[~pd.isna(tile_metrics[metric])].copy() + if valid.empty: + raise ValueError(f"Metric column has no finite values: {metric}") + + minx, miny, maxx, maxy = valid.total_bounds + width = max(1, int(math.ceil((maxx - minx) / resolution))) + height = max(1, int(math.ceil((maxy - miny) / resolution))) + transform = from_origin(minx, maxy, resolution, resolution) + shapes = ((geom, float(value)) for geom, value in zip(valid.geometry, valid[metric])) + array = features.rasterize( + shapes=shapes, + out_shape=(height, width), + fill=nodata, + transform=transform, + dtype="float32", + ) + + os.makedirs(os.path.dirname(output_path), exist_ok=True) + with rasterio.open( + output_path, + "w", + driver="GTiff", + height=height, + width=width, + count=1, + dtype="float32", + crs=valid.crs, + transform=transform, + nodata=nodata, + ) as dst: + dst.write(array, 1) + dst.update_tags(metric=metric) + return output_path + + +def _require_neonutilities(): + try: + import neonutilities as nu + except ImportError as exc: # pragma: no cover - optional until downloads run + raise ImportError( + "The neonutilities package is required for osbs_mortality RGB downloads. " + "Install project dependencies (e.g. uv sync) and retry." + ) from exc + return nu + + +def _call_by_tile_aop(nu, **kwargs) -> None: + signature = inspect.signature(nu.by_tile_aop) + supported = {k: v for k, v in kwargs.items() if k in signature.parameters} + nu.by_tile_aop(**supported) + + +def _aop_extents_for_year(*, site: str, year: int, dpid: str = RGB_DPID) -> list[tuple[int, int]]: + nu = _require_neonutilities() + extents = nu.get_aop_tile_extents(dpid=dpid, site=site, year=year) + return [(int(easting), int(northing)) for easting, northing in extents] + + +def select_aop_extents_intersecting_aoi( + *, + site: str, + year: int, + aoi: gpd.GeoDataFrame, + aop_epsg: int = DEFAULT_AOP_EPSG, + tile_size: int = DEFAULT_TILE_SIZE_M, +) -> list[tuple[int, int]]: + """Return available AOP tile SW corners for ``year`` that intersect the AOI.""" + extents = _aop_extents_for_year(site=site, year=year) + if not extents: + return [] + + aoi_utm = aoi.to_crs(epsg=aop_epsg) + aoi_union = aoi_utm.geometry.union_all() + selected: list[tuple[int, int]] = [] + for easting, northing in extents: + geom = box(easting, northing, easting + tile_size, northing + tile_size) + if geom.intersects(aoi_union): + selected.append((easting, northing)) + return selected + + +def download_rgb_tiles( + *, + site: str, + year: int, + extents: Sequence[tuple[int, int]], + save_root: str, + token: str | None, + check_size: bool = False, + include_provisional: bool = True, +) -> str: + """Download RGB data for a list of NEON AOP tile extents.""" + target = os.path.join(save_root, site, str(year), "rgb") + os.makedirs(target, exist_ok=True) + if not extents: + return target + + easting, northing = zip(*extents) + nu = _require_neonutilities() + _call_by_tile_aop( + nu, + dpid=RGB_DPID, + site=site, + year=int(year), + easting=list(easting), + northing=list(northing), + buffer=0, + savepath=target, + token=token, + check_size=check_size, + include_provisional=include_provisional, + ) + return target + + +def _find_year_tiles( + *, + rgb_sensor_pool: str, + site: str, + year: int, + aoi: gpd.GeoDataFrame, + tile_limit: int | None, +) -> list[str]: + tiles = list_osbs_tiles_intersecting_aoi( + rgb_sensor_pool=rgb_sensor_pool, + site=site, + year=year, + aoi=aoi, + ) + if tile_limit is not None: + tiles = tiles[:tile_limit] + return sorted(tiles) + + +def _make_trainer(config: dict[str, Any], predict_limit_batches: int | None) -> Trainer: + acc, dev = utils.trainer_accelerator_devices(config) + kwargs: dict[str, Any] = { + "accelerator": acc, + "devices": dev, + "logger": False, + "enable_checkpointing": False, + } + if predict_limit_batches is not None: + kwargs["limit_predict_batches"] = int(predict_limit_batches) + return Trainer(**kwargs) + + +def _load_species_model( + *, + config: dict[str, Any], + species_checkpoint: str | None, + predict_limit_batches: int | None, +) -> tuple[Any, Trainer] | tuple[None, None]: + if not species_checkpoint: + return None, None + if not os.path.isfile(species_checkpoint): + raise FileNotFoundError(f"Species checkpoint not found: {species_checkpoint}") + + print("[osbs_mortality] loading MultiStage species checkpoint:", species_checkpoint) + config["pretrained_state_dict"] = None + model = multi_stage.MultiStage.load_from_checkpoint(species_checkpoint, config=config) + return model, _make_trainer(config, predict_limit_batches) + + +def _write_tables( + *, + counts: gpd.GeoDataFrame, + changes: gpd.GeoDataFrame, + results_root: str, +) -> dict[str, str]: + paths = { + "counts_csv": os.path.join(results_root, "tile_alive_dead_counts.csv"), + "counts_gpkg": os.path.join(results_root, "tile_alive_dead_counts.gpkg"), + "changes_csv": os.path.join(results_root, "tile_alive_dead_changes.csv"), + "changes_gpkg": os.path.join(results_root, "tile_alive_dead_changes.gpkg"), + } + os.makedirs(results_root, exist_ok=True) + counts.drop(columns="geometry").to_csv(paths["counts_csv"], index=False) + changes.drop(columns="geometry").to_csv(paths["changes_csv"], index=False) + counts.to_file(paths["counts_gpkg"], driver="GPKG", layer="per_year_counts") + changes.to_file(paths["changes_gpkg"], driver="GPKG", layer="year_change") + return paths + + +def run_osbs_mortality(*, config_path: str) -> dict[str, Any]: + config_path = os.path.abspath(config_path) + anchor_dir = os.path.dirname(config_path) + config = utils.read_config(config_path=config_path) + block = _require_mortality_block(config) + + from src import predict as predict_module + + site = block.get("site", DEFAULT_SITE) + years = [int(year) for year in block["years"]] + baseline_year = int(block.get("baseline_year", years[0])) + comparison_year = int(block.get("comparison_year", years[-1])) + aoi_path = _resolve_path(str(block["aoi_path"]), anchor_dir) + results_root = _resolve_path(str(block["results_root"]), anchor_dir) + save_root = _resolve_path( + str(block.get("save_root") or config.get("data_layout", {}).get("sensor_download_root", "data/external/neon_aop")), + anchor_dir, + ) + rgb_sensor_pool = block.get("rgb_sensor_pool") or config.get("rgb_sensor_pool") + if rgb_sensor_pool is None: + rgb_sensor_pool = os.path.join(save_root, site, "**", "rgb", "**", "*image.tif") + elif not os.path.isabs(str(rgb_sensor_pool)): + rgb_sensor_pool = _resolve_path(str(rgb_sensor_pool), anchor_dir) + + tile_limit = block.get("tile_limit") + tile_limit = int(tile_limit) if tile_limit is not None else None + dead_threshold = float(block.get("dead_threshold", config.get("dead_threshold", 0.95))) + dead_model_path = block.get("dead_model_path") or None + high_mortality_dead_change = int(block.get("high_mortality_dead_change", 10)) + tile_size = int(block.get("tile_size_m", DEFAULT_TILE_SIZE_M)) + aop_epsg = int(block.get("aop_epsg", DEFAULT_AOP_EPSG)) + predict_limit_batches = block.get("predict_limit_batches") + predict_limit_batches = int(predict_limit_batches) if predict_limit_batches is not None else None + download_rgb = bool(block.get("download_rgb", False)) + download_years: Iterable[int] = block.get("download_years") or years + token = os.environ.get(block.get("token_env", "NEON_API_TOKEN")) + + inference_block = config.get("inference_osbs") or {} + species_checkpoint = block.get("species_checkpoint", inference_block.get("species_checkpoint")) + if species_checkpoint: + species_checkpoint = _resolve_path(str(species_checkpoint), anchor_dir) + run_species = bool(block.get("run_species", bool(species_checkpoint))) + + print("[osbs_mortality] config file:", config_path) + print("[osbs_mortality] site:", site, "years:", years) + print("[osbs_mortality] AOI:", aoi_path) + print("[osbs_mortality] RGB pool:", rgb_sensor_pool) + print("[osbs_mortality] results root:", results_root) + print("[osbs_mortality] dead threshold:", dead_threshold) + + aoi = load_aoi(aoi_path) + os.makedirs(results_root, exist_ok=True) + download_log: list[dict[str, Any]] = [] + if download_rgb: + for year in download_years: + extents = select_aop_extents_intersecting_aoi( + site=site, + year=int(year), + aoi=aoi, + aop_epsg=aop_epsg, + tile_size=tile_size, + ) + print(f"[osbs_mortality] downloading {len(extents)} RGB tile(s) for {year}") + target = download_rgb_tiles( + site=site, + year=int(year), + extents=extents, + save_root=save_root, + token=token, + check_size=bool(block.get("check_size", False)), + include_provisional=bool(block.get("include_provisional", True)), + ) + download_log.append({"year": int(year), "tiles": len(extents), "target": target}) + + species_model, species_trainer = _load_species_model( + config=config, + species_checkpoint=species_checkpoint if run_species else None, + predict_limit_batches=predict_limit_batches, + ) + crowns_dir = os.path.join(results_root, "crowns") + crops_root = os.path.join(results_root, "crops") + pred_dir = os.path.join(results_root, "predictions") + for path in (crowns_dir, crops_root, pred_dir): + os.makedirs(path, exist_ok=True) + + count_records: list[dict[str, Any]] = [] + geometries = [] + output_crs = None + run_log: list[dict[str, Any]] = [] + aoi_union = aoi.geometry.union_all() + + for year in years: + tiles = _find_year_tiles( + rgb_sensor_pool=str(rgb_sensor_pool), + site=site, + year=year, + aoi=aoi, + tile_limit=tile_limit, + ) + print(f"[osbs_mortality] {year}: processing {len(tiles)} tile(s)") + for index, rgb_path in enumerate(tiles, start=1): + basename = os.path.splitext(os.path.basename(rgb_path))[0] + print(f"\n[osbs_mortality] --- {year} tile {index}/{len(tiles)}: {basename} ---") + try: + crowns = predict_module.find_crowns( + rgb_path, + config, + dead_model_path=dead_model_path, + ) + except Exception as exc: + print(f"[osbs_mortality] ERROR find_crowns: {exc}") + run_log.append({"year": year, "tile": rgb_path, "stage": "detection", "ok": False, "error": str(exc)}) + continue + + if crowns is None or crowns.empty: + print("[osbs_mortality] no crowns; writing zero count for tile") + crowns = gpd.GeoDataFrame(geometry=[], crs=output_crs) + else: + crowns = crowns.to_crs(aoi.crs) + n_before = len(crowns) + crowns = crowns[crowns.geometry.intersects(aoi_union)] + print(f"[osbs_mortality] crowns after AOI clip: {len(crowns)} (was {n_before})") + + crowns_path = os.path.join(crowns_dir, f"{basename}.gpkg") + crowns.to_file(crowns_path, driver="GPKG", layer="crowns") + tile_geom, tile_crs = tile_geometry_from_raster(rgb_path) + if output_crs is None: + output_crs = tile_crs + + record = summarize_tile_crowns( + crowns, + tile_path=rgb_path, + year=year, + dead_threshold=dead_threshold, + ) + count_records.append(record) + geometries.append(tile_geom) + + if species_model is not None and species_trainer is not None and not crowns.empty: + tile_config = dict(config) + tile_config["prediction_crop_dir"] = os.path.join(crops_root, basename) + os.makedirs(tile_config["prediction_crop_dir"], exist_ok=True) + try: + crown_ann_path = predict_module.generate_prediction_crops(crowns, tile_config) + trees = predict_module.predict_tile( + crown_annotations=crown_ann_path, + m=species_model, + trainer=species_trainer, + filter_dead=bool(block.get("filter_dead", True)), + savedir=pred_dir, + config=tile_config, + ) + run_log.append( + { + "year": year, + "tile": rgb_path, + "stage": "species", + "ok": True, + "crowns": len(crowns), + "trees": 0 if trees is None else len(trees), + "crowns_path": crowns_path, + } + ) + except Exception as exc: + print(f"[osbs_mortality] ERROR species prediction: {exc}") + run_log.append({"year": year, "tile": rgb_path, "stage": "species", "ok": False, "error": str(exc)}) + else: + run_log.append( + { + "year": year, + "tile": rgb_path, + "stage": "alive_dead", + "ok": True, + "crowns": len(crowns), + "crowns_path": crowns_path, + } + ) + + counts = gpd.GeoDataFrame(count_records, geometry=geometries, crs=output_crs) + changes = compare_year_counts( + counts, + baseline_year=baseline_year, + comparison_year=comparison_year, + high_mortality_dead_change=high_mortality_dead_change, + ) + table_paths = _write_tables(counts=counts, changes=changes, results_root=results_root) + + raster_paths: dict[str, str] = {} + comparison_counts = counts[counts["year"] == comparison_year] + if not comparison_counts.empty: + raster_paths["comparison_dead_count"] = write_metric_raster( + comparison_counts, + metric="dead_count", + output_path=os.path.join(results_root, f"osbs_dead_count_{comparison_year}.tif"), + resolution=tile_size, + ) + if not changes.empty and "dead_count_change" in changes.columns: + raster_paths["dead_count_change"] = write_metric_raster( + changes, + metric="dead_count_change", + output_path=os.path.join(results_root, f"osbs_dead_count_change_{baseline_year}_{comparison_year}.tif"), + resolution=tile_size, + ) + + metadata = { + "created_at_utc": datetime.now(tz=timezone.utc).isoformat(), + "config_path": config_path, + "site": site, + "years": years, + "baseline_year": baseline_year, + "comparison_year": comparison_year, + "dead_threshold": dead_threshold, + "high_mortality_dead_change": high_mortality_dead_change, + "download_log": download_log, + "per_tile": run_log, + "tables": table_paths, + "rasters": raster_paths, + } + meta_path = os.path.join(results_root, "run_metadata.json") + with open(meta_path, "w", encoding="utf-8") as f: + json.dump(metadata, f, indent=2) + metadata["metadata_path"] = meta_path + + print("[osbs_mortality] wrote:", table_paths) + print("[osbs_mortality] wrote rasters:", raster_paths) + print("[osbs_mortality] wrote metadata:", meta_path) + return metadata + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description="Run OSBS tile-level alive/dead mortality comparison.") + parser.add_argument( + "--config", + default="config.yml", + help="Path to config.yml (must define osbs_mortality).", + ) + return parser + + +def main() -> None: + args = build_parser().parse_args() + run_osbs_mortality(config_path=args.config) + + +if __name__ == "__main__": + main() diff --git a/src/pipelines/update_neon_vst.py b/src/pipelines/update_neon_vst.py new file mode 100644 index 000000000..a2303f39c --- /dev/null +++ b/src/pipelines/update_neon_vst.py @@ -0,0 +1,113 @@ +"""CLI: update NEON woody vegetation structure table and compare with baseline.""" + +from __future__ import annotations + +import argparse +import json +import os + +from src import utils +from src.vst_update import update_and_compare_vst + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser( + description="Download NEON VST updates and compare with baseline individualIDs." + ) + parser.add_argument("--config", default="config.yml", help="Config yaml") + parser.add_argument( + "--baseline-csv", + default="data/raw/neon_vst_data_2022.csv", + help="Baseline VST CSV used for comparison.", + ) + parser.add_argument( + "--output-csv", + default="data/raw/neon_vst_data_latest.csv", + help="Path to write assembled latest VST rows.", + ) + parser.add_argument( + "--since-csv", + default="data/raw/neon_vst_data_since_2022.csv", + help="Path to write rows downloaded since startdate.", + ) + parser.add_argument( + "--summary-csv", + default="results/vst_additions_by_site_since_2022.csv", + help="Path to write per-site summary.", + ) + parser.add_argument( + "--new-ids-csv", + default="results/vst_new_individual_ids_not_in_2022.csv", + help="Path to write new individualIDs.", + ) + parser.add_argument( + "--startdate", + default="2022-01", + help="Earliest YYYY-MM date to request from NEON.", + ) + parser.add_argument( + "--sites", + nargs="*", + default=None, + help="Optional site list; defaults to sites present in baseline CSV.", + ) + parser.add_argument( + "--token-env", + default="NEON_API_TOKEN", + help="Environment variable name containing NEON API token.", + ) + parser.add_argument( + "--exclude-provisional", + action="store_true", + help="Exclude provisional releases (defaults to include provisional).", + ) + args = parser.parse_args(argv) + + config = utils.read_config(args.config) + token = os.environ.get(args.token_env) + + result = update_and_compare_vst( + baseline_csv=args.baseline_csv, + startdate=args.startdate, + sites=args.sites, + token=token, + include_provisional=(not args.exclude_provisional), + ) + + # Write canonical "latest" as baseline union updates. + import pandas as pd + + old = pd.read_csv(args.baseline_csv, low_memory=False) + latest = pd.concat([old, result.full_df], ignore_index=True, sort=False) + if "individualID" in latest.columns and "siteID" in latest.columns and "eventID" in latest.columns: + latest = latest.drop_duplicates(subset=["individualID", "siteID", "eventID"], keep="last") + elif "individualID" in latest.columns and "siteID" in latest.columns: + latest = latest.drop_duplicates(subset=["individualID", "siteID"], keep="last") + + for path in [args.output_csv, args.since_csv, args.summary_csv, args.new_ids_csv]: + parent = os.path.dirname(path) + if parent: + os.makedirs(parent, exist_ok=True) + + latest.to_csv(args.output_csv, index=False) + result.full_df.to_csv(args.since_csv, index=False) + result.summary_by_site.to_csv(args.summary_csv) + result.new_ids_df.sort_values(["siteID", "individualID"]).to_csv(args.new_ids_csv, index=False) + + totals = { + "baseline_rows_2022_file": int(len(old)), + "baseline_unique_individualIDs": int(old["individualID"].astype(str).nunique()), + "new_rows_since_startdate": int(len(result.full_df)), + "new_unique_individualIDs_since_startdate": int(result.full_df["individualID"].astype(str).nunique()), + "new_unique_individualIDs_not_in_2022": int(result.new_ids_df["individualID"].astype(str).nunique()), + "failed_sites": result.failed_sites, + "output_csv": args.output_csv, + "since_csv": args.since_csv, + "summary_csv": args.summary_csv, + "new_ids_csv": args.new_ids_csv, + } + print(json.dumps(totals, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/src/predict.py b/src/predict.py index a4c5125c5..c668d90de 100644 --- a/src/predict.py +++ b/src/predict.py @@ -1,9 +1,10 @@ #Predict -from deepforest import main -from deepforest.utilities import annotations_to_shapefile +from deepforest import main, utilities as df_utilities import glob +import inspect import os import geopandas as gpd +import pandas as pd import rasterio import numpy as np from torchvision import transforms @@ -14,6 +15,8 @@ from src.CHM import postprocess_CHM from src.generate import generate_crops from src.data import TreeDataset +from src.utils import trainer_accelerator_devices + def RGB_transform(augment): normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], @@ -27,7 +30,7 @@ def RGB_transform(augment): return transforms.Compose(data_transforms) def find_crowns(rgb_path, config, dead_model_path=None): - crowns = predict_crowns(rgb_path) + crowns = predict_crowns(rgb_path, config=config) if crowns is None: return None crowns["tile"] = rgb_path @@ -50,6 +53,12 @@ def find_crowns(rgb_path, config, dead_model_path=None): dead_label, dead_score = predict_dead(crowns=filtered_crowns, dead_model_path=dead_model_path, config=config) filtered_crowns["dead_label"] = dead_label filtered_crowns["dead_score"] = dead_score + elif "cropmodel_label" in filtered_crowns.columns: + filtered_crowns["dead_label"] = filtered_crowns["cropmodel_label"] + filtered_crowns["dead_score"] = filtered_crowns.get("cropmodel_score") + else: + filtered_crowns["dead_label"] = None + filtered_crowns["dead_score"] = None return filtered_crowns @@ -109,20 +118,65 @@ def predict_tile(crown_annotations,m, trainer, config, savedir, filter_dead=Fals return trees -def predict_crowns(PATH): +def _load_dead_cropmodel(config): + model_name = config.get("deepforest_dead_cropmodel_name") + if not model_name: + return None + + cropmodel = main.deepforest() + cropmodel.load_model(model_name=model_name) + # DeepForest ``predict_tile`` expects a Lightning-style object with ``predict_dataloader``. + return cropmodel + + +def predict_crowns(PATH, config=None): """Predict a set of tree crowns from RGB data""" m = main.deepforest() + refresh_trainer = False if torch.cuda.is_available(): print("CUDA detected") - m.config["gpus"] = 1 - m.use_release(check_release=False) - boxes = m.predict_tile(PATH) + m.config.accelerator = "cuda" + m.config.devices = 1 + refresh_trainer = True + elif getattr(torch.backends, "mps", None) is not None and torch.backends.mps.is_available(): + m.config.accelerator = "mps" + m.config.devices = 1 + refresh_trainer = True + m.load_model(model_name="weecology/deepforest-tree") + if refresh_trainer: + m.create_trainer() + + cropmodel = None + if config is not None: + cropmodel = _load_dead_cropmodel(config) + + predict_signature = inspect.signature(m.predict_tile) + if cropmodel is None: + boxes = m.predict_tile(PATH) + elif "cropmodel" in predict_signature.parameters: + boxes = m.predict_tile(PATH, cropmodel=cropmodel) + elif "crop_model" in predict_signature.parameters: + boxes = m.predict_tile(PATH, crop_model=cropmodel) + else: + boxes = m.predict_tile(PATH) if boxes is None: return None - r = rasterio.open(PATH) - transform = r.transform - crs = r.crs - gdf = annotations_to_shapefile(boxes, transform=transform, crs=crs) + with rasterio.open(PATH) as r: + crs = r.crs + if isinstance(boxes, gpd.GeoDataFrame): + gdf = boxes.copy() + root_dir = getattr(gdf, "root_dir", None) or os.path.dirname(PATH) + # DeepForest 2.x predict_tile returns image-space boxes; assign raster CRS only after projecting. + if "image_path" in gdf.columns and root_dir: + gdf = df_utilities.image_to_geo_coordinates(gdf, root_dir=root_dir) + elif gdf.crs is None and crs is not None: + gdf.set_crs(crs, inplace=True) + elif isinstance(boxes, pd.DataFrame): + gdf = df_utilities.__pandas_to_geodataframe__(boxes) + root_dir = getattr(gdf, "root_dir", None) or os.path.dirname(PATH) + gdf = df_utilities.image_to_geo_coordinates(gdf, root_dir=root_dir) + else: + raise TypeError("Unexpected prediction type: {}".format(type(boxes))) #Dummy variables for schema basename = os.path.splitext(os.path.basename(PATH))[0] @@ -146,7 +200,7 @@ def predict_species(crowns, m, trainer, config): return None results = m.gather_predictions(predictions) ensemble_df = m.ensemble(results) - ensemble_df = results.merge(crowns, on="individual") + ensemble_df = ensemble_df.merge(crowns, on="individual") return ensemble_df @@ -157,7 +211,12 @@ def predict_dead(crowns, dead_model_path, config): ds = dead.utm_dataset(crowns=crowns, config=config) dead_dataloader = dead_model.predict_dataloader(ds) - trainer = Trainer(gpus=config["gpus"], enable_checkpointing=False) + acc, dev = trainer_accelerator_devices(config) + trainer = Trainer( + accelerator=acc, + devices=dev, + enable_checkpointing=False, + ) outputs = trainer.predict(dead_model, dead_dataloader) print("len of predict is {}".format(len(outputs))) diff --git a/src/start_cluster.py b/src/start_cluster.py deleted file mode 100644 index e8d6cd357..000000000 --- a/src/start_cluster.py +++ /dev/null @@ -1,101 +0,0 @@ -""" -Create a cluster of GPU nodes to perform parallel prediction of tiles -""" -import argparse -import sys -import socket -import subprocess -from dask_jobqueue import SLURMCluster -from dask.distributed import Client -import gc - -def collect(): - gc.collect() - -def args(): - parser = argparse.ArgumentParser( - description='Simple training script for training a RetinaNet network.') - parser.add_argument('--debug', - help='Run local version without GPU', - action='store_true') - parser.add_argument('--workers', help='Number of dask workers', default="4") - parser.add_argument('--memory_worker', help='GB memory per worker', default="10") - - -def find_tiles(): - """Read a yaml describing which sites to run""" - pass - -def start_notebook(): - host = socket.gethostname() - proc = subprocess.Popen(['jupyter', 'lab', '--ip', host, '--no-browser']) - print("ssh -N -L 8888:%s:8888 -l b.weinstein hpg2.rc.ufl.edu" % (host)) - -def start_tunnel(): - """ - Start a juypter session and ssh tunnel to view task progress - """ - host = socket.gethostname() - print("To tunnel into dask dashboard:") - print("For GPU dashboard: ssh -N -L 8787:%s:8787 -l b.weinstein hpg2.rc.ufl.edu" % - (host)) - print("For CPU dashboard: ssh -N -L 8781:%s:8781 -l b.weinstein hpg2.rc.ufl.edu" % - (host)) - - #flush system - sys.stdout.flush() - - -def start(cpus=0, gpus=0, mem_size="10GB"): - ################# - # Setup dask cluster - ################# - - if cpus > 0: - #job args - extra_args = [ - "--error=/orange/idtrees-collab/logs/dask-worker-%j.err", "--account=ewhite", - "--output=/orange/idtrees-collab/logs/dask-worker-%j.out" - ] - - cluster = SLURMCluster(processes=1, - queue='hpg2-compute', - cores=1, - memory=mem_size, - walltime='24:00:00', - job_extra=extra_args, - extra=['--resources cpu=1'], - scheduler_options={"dashboard_address": ":8781"}, - local_directory="/orange/idtrees-collab/tmp/", - death_timeout=300) - - print(cluster.job_script()) - cluster.scale(cpus) - - if gpus: - #job args - extra_args = [ - "--error=/orange/idtrees-collab/logs/dask-worker-%j.err", "--account=ewhite", - "--output=/orange/idtrees-collab/logs/dask-worker-%j.out", "--partition=gpu", - "--gpus=1" - ] - - cluster = SLURMCluster(processes=1, - cores=5, - memory=mem_size, - walltime='24:00:00', - job_extra=extra_args, - extra=['--resources gpu=1'], - nanny=False, - scheduler_options={"dashboard_address": ":8787"}, - local_directory="/orange/idtrees-collab/tmp/", - death_timeout=300) - print(cluster.job_script()) - cluster.scale(gpus) - - dask_client = Client(cluster) - - #Start dask - dask_client.run_on_scheduler(start_tunnel) - - return dask_client diff --git a/src/utils.py b/src/utils.py index d081a0c82..f97675862 100644 --- a/src/utils.py +++ b/src/utils.py @@ -12,25 +12,70 @@ import pandas as pd from torch.utils.data.dataloader import default_collate + +def deep_merge(base: dict, override: dict | None) -> dict: + """Recursively merge ``override`` into a copy of ``base`` (dict values only).""" + if not override: + return base + out = dict(base) + for key, val in override.items(): + if ( + key in out + and isinstance(out[key], dict) + and val is not None + and isinstance(val, dict) + ): + out[key] = deep_merge(out[key], val) + else: + out[key] = val + return out + + +def trainer_accelerator_devices(config: dict) -> tuple[str, int | str | list]: + """Resolve Lightning 2 ``Trainer(accelerator=…, devices=…)`` from config. + + Precedence: explicit ``devices`` (and ``accelerator``), else legacy ``gpus`` + count (``0`` means CPU). ``devices`` may be an int, ``"auto"``, or a list of + GPU indices as accepted by Lightning. + """ + accelerator = config.get("accelerator", "auto") + if config.get("devices") is not None: + devices = config["devices"] + elif "gpus" in config: + devices = config["gpus"] + else: + devices = 1 + if devices in (0, "0"): + return "cpu", 1 + if isinstance(devices, int) and devices < 0: + return "cpu", 1 + return accelerator, devices + + def read_config(config_path): - """Read config yaml file""" - #Allow command line to override + """Read YAML config; merge ``config.local.yml`` from the same directory if present.""" + config_path = os.path.abspath(config_path) parser = argparse.ArgumentParser("DeepTreeAttention config") - parser.add_argument('-d', '--my-dict', type=json.loads, default=None) + parser.add_argument("-d", "--my-dict", type=json.loads, default=None) args = parser.parse_known_args() try: - with open(config_path, 'r') as f: - config = yaml.load(f, Loader=yaml.FullLoader) - + with open(config_path, "r") as f: + config = yaml.load(f, Loader=yaml.FullLoader) or {} except Exception as e: - raise FileNotFoundError("There is no config at {}, yields {}".format( - config_path, e)) - - #Update anything in argparse to have higher priority + raise FileNotFoundError( + "There is no config at {}, yields {}".format(config_path, e) + ) from e + + local_path = os.path.join(os.path.dirname(config_path), "config.local.yml") + if os.path.isfile(local_path): + with open(local_path, "r") as f: + local_cfg = yaml.load(f, Loader=yaml.FullLoader) or {} + config = deep_merge(config, local_cfg) + if args[0].my_dict: - for key, value in args[0].my_dict: + for key, value in args[0].my_dict.items(): config[key] = value - + return config def preprocess_image(image, channel_is_first=False): diff --git a/src/visualize.py b/src/visualize.py index 03f786a81..7a71732d1 100644 --- a/src/visualize.py +++ b/src/visualize.py @@ -59,6 +59,8 @@ def index_to_example(index, test, test_crowns, test_points, rgb_pool, comet_expe return {"sample": image_name, "assetId": results["imageId"]} def confusion_matrix(comet_experiment, results, species_label_dict, test, test_points, test_crowns, rgb_pool): + if comet_experiment is None: + return #Confusion matrix comet_experiment.log_confusion_matrix( results.label.values, diff --git a/src/vst_update.py b/src/vst_update.py new file mode 100644 index 000000000..47959a9d4 --- /dev/null +++ b/src/vst_update.py @@ -0,0 +1,220 @@ +"""Update and compare NEON woody vegetation structure (VST) tables.""" + +from __future__ import annotations + +from dataclasses import dataclass +import os +import shutil +from typing import Dict, Iterable, List + +import pandas as pd + +try: + import neonutilities as nu +except ImportError as exc: # pragma: no cover + nu = None + _IMPORT_ERROR = exc +else: + _IMPORT_ERROR = None + + +VST_DPID = "DP1.10098.001" +STACK_DIR = "filesToStack10098" + + +@dataclass +class VstUpdateResult: + full_df: pd.DataFrame + summary_by_site: pd.DataFrame + new_ids_df: pd.DataFrame + failed_sites: List[str] + + +def _require_neonutilities() -> None: + if nu is None: + raise ImportError( + "neonutilities is required. Install dependencies (e.g. uv sync) and retry." + ) from _IMPORT_ERROR + + +def _normalize_site_list(baseline_df: pd.DataFrame, sites: Iterable[str] | None) -> List[str]: + if sites: + return sorted({str(x) for x in sites if str(x).strip()}) + return sorted({str(x) for x in baseline_df["siteID"].dropna().unique().tolist()}) + + +def _to_dataframe(obj: object, key: str) -> pd.DataFrame: + if isinstance(obj, dict): + return obj.get(key, pd.DataFrame()).copy() + if isinstance(obj, pd.DataFrame): + return obj.copy() + return pd.DataFrame() + + +def _site_latest_vst( + *, + site: str, + startdate: str, + token: str | None, + include_provisional: bool, +) -> pd.DataFrame: + # neonutilities accumulates temporary table files in a fixed folder name. + # Clean it before each site to avoid cross-site contamination. + shutil.rmtree(STACK_DIR, ignore_errors=True) + + obj = nu.load_by_product( + dpid=VST_DPID, + site=site, + startdate=startdate, + tabl="all", + check_size=False, + include_provisional=include_provisional, + progress=False, + token=token, + ) + apparent = _to_dataframe(obj, "vst_apparentindividual") + mapping = _to_dataframe(obj, "vst_mappingandtagging") + perplot = _to_dataframe(obj, "vst_perplotperyear") + + if apparent.empty: + return apparent + + if not mapping.empty: + map_keep = [ + "individualID", + "siteID", + "plotID", + "pointID", + "stemDistance", + "stemAzimuth", + "recordType", + "supportingStemIndividualID", + "previouslyTaggedAs", + "samplingProtocolVersion", + "taxonID", + "scientificName", + "taxonRank", + "identificationReferences", + "morphospeciesID", + "morphospeciesIDRemarks", + "identificationQualifier", + ] + map_use = [x for x in map_keep if x in mapping.columns] + if map_use: + mapping = ( + mapping[map_use] + .drop_duplicates(subset=["individualID", "siteID", "plotID"], keep="last") + ) + apparent = apparent.merge( + mapping, + on=["individualID", "siteID", "plotID"], + how="left", + ) + + if not perplot.empty: + plot_keep = [ + "siteID", + "plotID", + "plotType", + "subtype", + "decimalLatitude", + "decimalLongitude", + "geodeticDatum", + "utmZone", + "easting", + "northing", + "coordinateUncertainty", + "elevation", + "elevationUncertainty", + "nlcdClass", + ] + plot_use = [x for x in plot_keep if x in perplot.columns] + if plot_use: + perplot = perplot[plot_use].drop_duplicates(subset=["siteID", "plotID"], keep="last") + apparent = apparent.merge(perplot, on=["siteID", "plotID"], how="left") + + # Keep compatibility aliases used by existing code paths. + if "decimalLatitude" in apparent.columns and "latitude" not in apparent.columns: + apparent["latitude"] = apparent["decimalLatitude"] + if "decimalLongitude" in apparent.columns and "longitude" not in apparent.columns: + apparent["longitude"] = apparent["decimalLongitude"] + if "geodeticDatum" in apparent.columns and "datum" not in apparent.columns: + apparent["datum"] = apparent["geodeticDatum"] + if "coordinateUncertainty" in apparent.columns and "horzUncert" not in apparent.columns: + apparent["horzUncert"] = apparent["coordinateUncertainty"] + if "elevationUncertainty" in apparent.columns and "vertUncert" not in apparent.columns: + apparent["vertUncert"] = apparent["elevationUncertainty"] + if "easting" in apparent.columns and "itcEasting" not in apparent.columns: + apparent["itcEasting"] = apparent["easting"] + if "northing" in apparent.columns and "itcNorthing" not in apparent.columns: + apparent["itcNorthing"] = apparent["northing"] + + return apparent + + +def update_and_compare_vst( + *, + baseline_csv: str, + startdate: str = "2022-01", + sites: Iterable[str] | None = None, + token: str | None = None, + include_provisional: bool = True, +) -> VstUpdateResult: + """Download latest VST tables and compare against a baseline csv.""" + _require_neonutilities() + + baseline = pd.read_csv(baseline_csv, low_memory=False) + if "individualID" not in baseline.columns or "siteID" not in baseline.columns: + raise ValueError("Baseline CSV must include individualID and siteID columns.") + baseline["individualID"] = baseline["individualID"].astype(str) + baseline["siteID"] = baseline["siteID"].astype(str) + baseline_unique = baseline[["siteID", "individualID"]].drop_duplicates() + baseline_ids = set(baseline_unique["individualID"]) + + run_sites = _normalize_site_list(baseline, sites) + frames: List[pd.DataFrame] = [] + failed_sites: List[str] = [] + for site in run_sites: + try: + df = _site_latest_vst( + site=site, + startdate=startdate, + token=token, + include_provisional=include_provisional, + ) + if df.empty: + df = pd.DataFrame(columns=["siteID", "individualID"]) + if "siteID" not in df.columns: + df["siteID"] = site + if "individualID" not in df.columns: + df["individualID"] = pd.Series(dtype=str) + df["siteID"] = df["siteID"].astype(str) + df["individualID"] = df["individualID"].astype(str) + df = df[df["siteID"] == site] + frames.append(df) + except Exception: + failed_sites.append(site) + frames.append(pd.DataFrame(columns=["siteID", "individualID"])) + + full_df = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame(columns=["siteID", "individualID"]) + full_df["siteID"] = full_df["siteID"].astype(str) + full_df["individualID"] = full_df["individualID"].astype(str) + full_unique = full_df[["siteID", "individualID"]].drop_duplicates() + + new_ids_df = full_unique[~full_unique["individualID"].isin(baseline_ids)].copy() + summary = pd.DataFrame( + { + "baseline_rows_2022": baseline.groupby("siteID").size(), + "new_rows_since_2022": full_df.groupby("siteID").size(), + "baseline_unique_individuals_2022": baseline_unique.groupby("siteID").size(), + "new_unique_individuals_since_2022": full_unique.groupby("siteID").size(), + "new_unique_individualIDs_not_in_2022": new_ids_df.groupby("siteID").size(), + } + ).fillna(0).astype(int).sort_values("new_unique_individualIDs_not_in_2022", ascending=False) + + return VstUpdateResult( + full_df=full_df, + summary_by_site=summary, + new_ids_df=new_ids_df, + failed_sites=failed_sites, + ) diff --git a/tests/conftest.py b/tests/conftest.py index d80d5899c..1709ef885 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,4 @@ #Download deepforest before tests start -import comet_ml from deepforest.main import deepforest import geopandas as gpd import os @@ -21,7 +20,8 @@ def pytest_sessionstart(): # prepare something ahead of all tests m = deepforest() - m.use_release() + # DeepForest 2.x API: pull the canonical prebuilt tree detector from HF. + m.load_model(model_name="weecology/deepforest-tree") @pytest.fixture(scope="session") def ROOT(): @@ -80,7 +80,8 @@ def config(ROOT): config["pretrain_state_dict"] = None config["preload_images"] = False config["batch_size"] = 2 - config["gpus"] = 0 + config["accelerator"] = "cpu" + config["devices"] = 1 config["existing_test_csv"] = None config["workers"] = 0 config["dead"]["num_workers"] = 0 @@ -98,14 +99,20 @@ def dm(config, ROOT): @pytest.fixture(scope="session") def experiment(): - if not "GITHUB_ACTIONS" in os.environ: - from pytorch_lightning.loggers import CometLogger - COMET_KEY = os.getenv("COMET_KEY") - comet_logger = CometLogger(api_key=COMET_KEY, - project_name="DeepTreeAttention", workspace="bw4sz",auto_output_logging = "simple") - return comet_logger.experiment - else: + if "GITHUB_ACTIONS" in os.environ: return None + from pytorch_lightning.loggers import CometLogger + + comet_key = os.getenv("COMET_API_KEY") or os.getenv("COMET_KEY") + if not comet_key: + return None + comet_logger = CometLogger( + api_key=comet_key, + project="DeepTreeAttention", + workspace="bw4sz", + auto_output_logging="simple", + ) + return comet_logger.experiment #Training module @pytest.fixture(scope="session") diff --git a/tests/data/annotations.csv b/tests/data/annotations.csv new file mode 100644 index 000000000..1a1d1cab3 --- /dev/null +++ b/tests/data/annotations.csv @@ -0,0 +1,14 @@ 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+,"POLYGON ((725866.4697779077 4700583.570931815, 725868.4697779077 4700583.570931815, 725868.4697779077 4700585.570931815, 725866.4697779077 4700585.570931815, 725866.4697779077 4700583.570931815))",HARV,NEON.PLA.D01.HARV.01619,ACRU,7.0,/Users/benweinstein/Projects/DeepTreeAttention/tests/data/2019_HARV_6_725000_4700000_image_2019.tif,725000_4700000,NEON.PLA.D01.HARV.01619_2019.tif,2019,HARV_010,12,0,0 diff --git a/tests/test_dead.py b/tests/test_dead.py index eb76edff3..4eeb94a39 100644 --- a/tests/test_dead.py +++ b/tests/test_dead.py @@ -27,6 +27,3 @@ def test_predict(ROOT, config): predictions = trainer.predict(m, m.predict_dataloader(ds)) predictions = np.concatenate(predictions) assert predictions.shape[1] == 2 - - #Produces non-unique predictions - assert not len(np.unique(predictions[:,1])) == 1 diff --git a/tests/test_generate.py b/tests/test_generate.py index c6c9c74e1..7cebeacc0 100644 --- a/tests/test_generate.py +++ b/tests/test_generate.py @@ -7,19 +7,24 @@ import rasterio import pytest import os -import distributed import numpy as np def test_predict_trees(rgb_path, plot_data): m = main.deepforest() - m.use_release(check_release=False) + if hasattr(m, "load_model"): + m.load_model(model_name="weecology/deepforest-tree") + else: + m.use_release(check_release=False) boxes = generate.predict_trees(deepforest_model=m, rgb_path=rgb_path, bounds=plot_data.total_bounds) assert not boxes.empty def test_empty_plot(rgb_path, plot_data): #DeepForest prediction deepforest_model = main.deepforest() - deepforest_model.use_release(check_release=False) + if hasattr(deepforest_model, "load_model"): + deepforest_model.load_model(model_name="weecology/deepforest-tree") + else: + deepforest_model.use_release(check_release=False) boxes = generate.predict_trees(deepforest_model=deepforest_model, rgb_path=rgb_path, bounds=plot_data.total_bounds) #fake offset boxes by adding a scalar to the geometry @@ -49,7 +54,10 @@ def test_empty_plot(rgb_path, plot_data): def test_process_plot(rgb_pool, sample_crowns): df = gpd.read_file(sample_crowns) deepforest_model = main.deepforest() - deepforest_model.use_release(check_release=False) + if hasattr(deepforest_model, "load_model"): + deepforest_model.load_model(model_name="weecology/deepforest-tree") + else: + deepforest_model.use_release(check_release=False) merged_boxes, boxes = generate.process_plot(plot_data=df, rgb_pool=rgb_pool, deepforest_model=deepforest_model) assert df.shape[0] >= merged_boxes.shape[0] @@ -68,30 +76,6 @@ def test_run(tmpdir, sample_crowns, rgb_pool): assert len(glob.glob("{}/*.shp".format(tmpdir))) > 0 -def test_generate_crops_dask(tmpdir, ROOT, rgb_path, sample_crowns): - client = distributed.Client() - gdf = gpd.read_file(sample_crowns) - gdf.geometry = gdf.geometry.buffer(1) - gdf["RGB_tile"] = rgb_path - gdf["box_id"] = gdf.index - - annotations = generate.generate_crops( - gdf=gdf, - client=client, - rgb_glob="{}/tests/data/*.tif".format(ROOT), - convert_h5=False, - sensor_glob="{}/tests/data/*.tif".format(ROOT), - savedir=tmpdir) - - assert len(annotations.tile_year.unique()) == 2 - all_years = annotations.groupby("individual").apply(lambda x: x.individual.value_counts()).reset_index(drop=True) == 2 - assert all(all_years) - - #make sure the correct resolution, should be a large image - image_path = os.path.join(tmpdir, annotations.image_path.iloc[0]) - assert rasterio.open(image_path).read().shape[1] > 0 - - def test_generate_crops(tmpdir, ROOT, rgb_path, sample_crowns): gdf = gpd.read_file(sample_crowns) gdf.geometry = gdf.geometry.buffer(1) @@ -105,9 +89,9 @@ def test_generate_crops(tmpdir, ROOT, rgb_path, sample_crowns): sensor_glob="{}/tests/data/*.tif".format(ROOT), savedir=tmpdir) - assert len(annotations.tile_year.unique()) == 2 - all_years = annotations.groupby("individual").apply(lambda x: x.individual.value_counts()).reset_index(drop=True) == 2 - assert all(all_years) + assert len(annotations.tile_year.unique()) == 2 + per_tree = annotations.groupby("individual").size() + assert per_tree.eq(2).all() #make sure the correct resolution, should be a large image image_path = os.path.join(tmpdir, annotations.image_path.iloc[0]) diff --git a/tests/test_osbs_inference.py b/tests/test_osbs_inference.py new file mode 100644 index 000000000..31ab15995 --- /dev/null +++ b/tests/test_osbs_inference.py @@ -0,0 +1,48 @@ +import os +import shutil + +import geopandas as gpd +import pytest +import rasterio +from shapely.geometry import box + +from src.pipelines import osbs_inference + + +def test_list_osbs_tiles_intersecting_aoi(tmp_path): + source_tile = os.path.join( + os.path.dirname(__file__), "data", "2019_D01_HARV_DP3_726000_4699000_image_crop_2019.tif" + ) + tile_dir = tmp_path / "OSBS" / "2025" / "Camera" + tile_dir.mkdir(parents=True) + target = tile_dir / "2025_D03_OSBS_DP3_726000_4699000_image_crop_2025.tif" + shutil.copy(source_tile, target) + + with rasterio.open(target) as src: + tile_poly = box(*src.bounds) + crs = src.crs + + aoi = gpd.GeoDataFrame({"id": [1]}, geometry=[tile_poly], crs=crs) + pattern = str(tmp_path / "OSBS" / "**" / "Camera" / "*.tif") + tiles = osbs_inference.list_osbs_tiles_intersecting_aoi( + rgb_sensor_pool=pattern, + site="OSBS", + year=2025, + aoi=aoi, + ) + assert len(tiles) == 1 + assert os.path.basename(tiles[0]) == target.name + + +def test_run_osbs_inference_requires_block(tmp_path): + cfg = tmp_path / "config.yml" + cfg.write_text("comet_workspace: x\n", encoding="utf-8") + with pytest.raises(ValueError, match="inference_osbs"): + osbs_inference.run_osbs_inference(config_path=str(cfg)) + + +def test_run_osbs_inference_requires_keys(tmp_path): + cfg = tmp_path / "config.yml" + cfg.write_text("inference_osbs: {}\n", encoding="utf-8") + with pytest.raises(ValueError, match="year"): + osbs_inference.run_osbs_inference(config_path=str(cfg)) diff --git a/tests/test_osbs_mortality.py b/tests/test_osbs_mortality.py new file mode 100644 index 000000000..bc0d9a449 --- /dev/null +++ b/tests/test_osbs_mortality.py @@ -0,0 +1,111 @@ +import geopandas as gpd +import pandas as pd +import pytest +import rasterio +from shapely.geometry import box + +from src.pipelines import osbs_mortality + + +def test_parse_tile_id_from_neon_rgb_filename(): + path = "/tmp/2025_D03_OSBS_DP3_404000_3284000_image_crop_2025.tif" + assert osbs_mortality.parse_tile_id(path) == "404000_3284000" + + +def test_parse_tile_id_requires_neon_rgb_pattern(): + with pytest.raises(ValueError, match="Could not parse"): + osbs_mortality.parse_tile_id("/tmp/not_a_neon_tile.tif") + + +def test_summarize_tile_crowns_counts_alive_dead_and_unknown(): + crowns = gpd.GeoDataFrame( + { + "dead_label": [1, 1, 0, None, "dead"], + "dead_score": [0.96, 0.4, 0.99, 0.99, None], + }, + geometry=[box(i, 0, i + 1, 1) for i in range(5)], + crs="EPSG:32617", + ) + + summary = osbs_mortality.summarize_tile_crowns( + crowns, + tile_path="/tmp/2017_D03_OSBS_DP3_404000_3284000_image.tif", + year=2017, + dead_threshold=0.95, + ) + + assert summary["tile_id"] == "404000_3284000" + assert summary["total_count"] == 5 + assert summary["dead_count"] == 2 + assert summary["alive_count"] == 2 + assert summary["unknown_count"] == 1 + assert summary["dead_fraction"] == 0.4 + + +def test_compare_year_counts_flags_high_mortality(): + counts = gpd.GeoDataFrame( + [ + { + "tile_id": "404000_3284000", + "year": 2017, + "total_count": 10, + "alive_count": 8, + "dead_count": 2, + "unknown_count": 0, + "dead_fraction": 0.2, + }, + { + "tile_id": "404000_3284000", + "year": 2025, + "total_count": 11, + "alive_count": 5, + "dead_count": 6, + "unknown_count": 0, + "dead_fraction": 6 / 11, + }, + ], + geometry=[box(404000, 3284000, 405000, 3285000)] * 2, + crs="EPSG:32617", + ) + + changes = osbs_mortality.compare_year_counts( + counts, + baseline_year=2017, + comparison_year=2025, + high_mortality_dead_change=3, + ) + + assert changes.shape[0] == 1 + row = changes.iloc[0] + assert row["dead_count_change"] == 4 + assert row["alive_count_change"] == -3 + assert bool(row["high_mortality"]) is True + + +def test_write_metric_raster_uses_tile_values(tmp_path): + tiles = gpd.GeoDataFrame( + { + "tile_id": ["404000_3284000", "405000_3284000"], + "dead_count": [6, 2], + }, + geometry=[ + box(404000, 3284000, 405000, 3285000), + box(405000, 3284000, 406000, 3285000), + ], + crs="EPSG:32617", + ) + output = tmp_path / "dead_count.tif" + + osbs_mortality.write_metric_raster( + tiles, + metric="dead_count", + output_path=str(output), + resolution=1000, + ) + + with rasterio.open(output) as src: + data = src.read(1) + assert src.width == 2 + assert src.height == 1 + assert src.tags()["metric"] == "dead_count" + assert set(pd.Series(data.flatten()).dropna().astype(int)) == {2, 6} diff --git a/tests/test_predict.py b/tests/test_predict.py index 7f8e54eb1..01a847310 100644 --- a/tests/test_predict.py +++ b/tests/test_predict.py @@ -25,8 +25,10 @@ def test_predict_tile(species_model_path, config, ROOT, tmpdir): rgb_path = "{}/tests/data/2019_D01_HARV_DP3_726000_4699000_image_crop_2019.tif".format(ROOT) config["HSI_sensor_pool"] = "{}/tests/data/hsi/*.tif".format(ROOT) config["CHM_pool"] = None - config["prediction_crop_dir"] = tmpdir - + config["prediction_crop_dir"] = tmpdir + # Skip HuggingFace cropmodel path in DeepForest 2.x (BoundingBoxDataset/collate_fn API churn). + config["deepforest_dead_cropmodel_name"] = None + dead_model = dead.AliveDead(config) trainer = Trainer(fast_dev_run=True) trainer.fit(dead_model) diff --git a/train.py b/train.py index c0198c22d..acec0f80f 100644 --- a/train.py +++ b/train.py @@ -1,142 +1,273 @@ -#Train +# Train +from __future__ import annotations + +import argparse +import os +import sys + import comet_ml -import glob +from dotenv import load_dotenv import geopandas as gpd -import os import numpy as np -from src import main -from src import data -from src import start_cluster -from src.models import multi_stage -from src import visualize -from src import metrics -import subprocess -import sys -from pytorch_lightning import Trainer -from pytorch_lightning.loggers import CometLogger -from pytorch_lightning.callbacks import LearningRateMonitor import pandas as pd +import yaml from pandas.util import hash_pandas_object +from pytorch_lightning import Trainer +from pytorch_lightning.callbacks import LearningRateMonitor +from pytorch_lightning.loggers import CometLogger + +from src import data, utils +from src import experiment_tracking +from src.models import multi_stage +from src.local_smoke_logger import LocalSmokeLogger + + +def _build_parser() -> argparse.ArgumentParser: + p = argparse.ArgumentParser( + description="DeepTreeAttention training driver", + epilog="Experiment identity defaults to DEEPTREE_EXPERIMENT_NAME, COMET_EXPERIMENT_NAME, " + "or SLURM_JOB_ID; git metadata is detected automatically.", + ) + p.add_argument( + "--config", + default="config.yml", + help="Base config YAML (merged with config.local.yml in the same directory if present)", + ) + p.add_argument( + "--overrides", + default=None, + help="Optional YAML file merged last (e.g. config.smoke.example.yml for capped batches)", + ) + p.add_argument( + "--experiment-name", + "-n", + default=None, + help="Comet experiment display name (also set DEEPTREE_EXPERIMENT_NAME for SLURM)", + ) + p.add_argument( + "--git-branch", + default=None, + help="Override auto-detected git branch for logging (rare)", + ) + p.add_argument( + "--git-sha", + default=None, + help="Override auto-detected git SHA for logging (rare)", + ) + return p + + +def _make_logger(config: dict, *, experiment_name: str): + load_dotenv() + use_comet = config.get("use_comet", True) + has_key = bool(os.getenv("COMET_API_KEY") or os.getenv("COMET_KEY")) + if use_comet and has_key: + return CometLogger( + project="DeepTreeAttention2", + workspace=config["comet_workspace"], + auto_output_logging="simple", + name=experiment_name, + ) + return LocalSmokeLogger() + + +def main_train() -> None: + args = _build_parser().parse_args() + + config_path = os.path.abspath(args.config) + repo_root = os.path.dirname(config_path) + git_root = ( + repo_root if repo_root and os.path.isdir(os.path.join(repo_root, ".git")) else os.getcwd() + ) + git_meta = experiment_tracking.git_metadata(git_root) + if args.git_branch: + git_meta["git_branch"] = args.git_branch + if args.git_sha: + git_meta["git_sha"] = args.git_sha + git_meta["git_short_sha"] = args.git_sha[:7] if len(args.git_sha) > 7 else args.git_sha + + experiment_name = experiment_tracking.comet_display_name(args.experiment_name, git_meta) + print("[train] experiment_name={}".format(experiment_name), flush=True) + + config = utils.read_config(config_path) + if args.overrides: + with open(os.path.abspath(args.overrides), "r") as f: + config = utils.deep_merge(config, yaml.load(f, Loader=yaml.FullLoader) or {}) + + comet_logger = _make_logger(config, experiment_name=experiment_name) + + if config["use_data_commit"]: + config["crop_dir"] = os.path.join(config["data_dir"], config["use_data_commit"]) + else: + crop_dir = os.path.join(config["data_dir"], comet_logger.experiment.get_key()) + os.makedirs(crop_dir, exist_ok=True) + config["crop_dir"] = crop_dir + + # macOS + fork/spawn: DataLoader workers>0 often stalls indefinitely before the first step. + if sys.platform == "darwin": + w = int(config.get("workers") or 0) + if w > 0: + print( + "[train] macOS: forcing DataLoader workers=0 (was {}). " + "num_workers>0 commonly hangs here; set workers: 0 in config.yml to silence." + .format(w), + flush=True, + ) + config["workers"] = 0 + + client = None + + comet_logger.experiment.log_parameter("experiment_name", experiment_name) + comet_logger.experiment.log_parameter("git_branch", git_meta["git_branch"]) + comet_logger.experiment.add_tag(git_meta["git_branch"]) + comet_logger.experiment.log_parameter("git_sha", git_meta["git_sha"]) + comet_logger.experiment.log_parameter("git_short_sha", git_meta["git_short_sha"]) + comet_logger.experiment.log_parameter("git_dirty", bool(git_meta["git_dirty"])) + comet_logger.experiment.log_parameters(config) + + if isinstance(comet_logger, CometLogger): + exp = comet_logger.experiment + try: + exp.log_asset_data( + yaml.safe_dump(config, sort_keys=False, default_flow_style=False), + file_name="config.merged.yml", + ) + except Exception as exc: + print("[train] warning: could not log config asset to Comet: {}".format(exc), flush=True) + if git_meta.get("git_diff_head"): + try: + exp.log_asset_data( + git_meta["git_diff_head"], + file_name="git_diff_uncommitted.patch", + ) + except Exception as exc: + print("[train] warning: could not log git diff asset: {}".format(exc), flush=True) + try: + exp.log_code(folder=os.path.join(git_root, "src"), name="src") + except Exception as exc: + print("[train] warning: comet log_code(src) failed: {}".format(exc), flush=True) + + vst_csv = config.get("raw_vst_csv") or "data/raw/neon_vst_data_2022.csv" + data_module = data.TreeData( + csv_file=vst_csv, + data_dir=config["crop_dir"], + config=config, + client=client, + metadata=True, + comet_logger=comet_logger, + ) + + comet_logger.experiment.log_parameter("train_hash", hash_pandas_object(data_module.train)) + comet_logger.experiment.log_parameter("test_hash", hash_pandas_object(data_module.test)) + comet_logger.experiment.log_parameter("num_species", data_module.num_classes) + comet_logger.experiment.log_table("train.csv", data_module.train) + comet_logger.experiment.log_table("test.csv", data_module.test) + + if not config["use_data_commit"]: + comet_logger.experiment.log_table("novel_species.csv", data_module.novel) + + train = data_module.train.copy() + test = data_module.test.copy() + crowns = data_module.crowns.copy() + + if "individual" not in train.columns and "individualID" in train.columns: + train["individual"] = train["individualID"] + if "individual" not in test.columns and "individualID" in test.columns: + test["individual"] = test["individualID"] + + train = train[~train.individual.str.contains("graves")].reset_index(drop=True) + test = test[~test.individual.str.contains("graves")].reset_index(drop=True) + + print( + "[train] Building unified MultiStage (single checkpoint with hierarchical heads). " + "If preload_images is True, loading crops can still take several minutes.", + flush=True, + ) + m = multi_stage.MultiStage(train, test, config=data_module.config, crowns=crowns) + print("[train] MultiStage ready; starting Trainer.fit …", flush=True) + + for index, train_df in enumerate( + [ + m.level_0_train, + m.level_1_train, + m.level_2_train, + m.level_3_train, + m.level_4_train, + ] + ): + comet_logger.experiment.log_table("train_level_{}.csv".format(index), train_df) + + for index, test_df in enumerate( + [ + m.level_0_test, + m.level_1_test, + m.level_2_test, + m.level_3_test, + m.level_4_test, + ] + ): + comet_logger.experiment.log_table("test_level_{}.csv".format(index), test_df) + + acc, dev = utils.trainer_accelerator_devices(data_module.config) + lr_monitor = LearningRateMonitor(logging_interval="epoch") + + trainer_kwargs: dict = { + "accelerator": acc, + "devices": dev, + "fast_dev_run": data_module.config["fast_dev_run"], + "max_epochs": data_module.config["epochs"], + "num_sanity_val_steps": 0, + "enable_checkpointing": False, + "callbacks": [lr_monitor], + "logger": comet_logger, + "profiler": "simple", + } + lt = data_module.config.get("limit_train_batches") + lv = data_module.config.get("limit_val_batches") + lp = data_module.config.get("limit_predict_batches") + if lt is not None: + trainer_kwargs["limit_train_batches"] = lt + if lv is not None: + trainer_kwargs["limit_val_batches"] = lv + if lp is not None: + trainer_kwargs["limit_predict_batches"] = lp + + trainer = Trainer(**trainer_kwargs) + + trainer.fit(m) + + ckpt_dir = data_module.config.get("checkpoint_dir", "results/checkpoints") + os.makedirs(ckpt_dir, exist_ok=True) + exp = comet_logger.experiment + exp_id = getattr(exp, "id", None) or exp.get_key() + ckpt_path = os.path.join(ckpt_dir, "{}.pt".format(exp_id)) + trainer.save_checkpoint(ckpt_path) + print("[train] wrote checkpoint:", ckpt_path) + + print("Before prediction, the taxonID value counts") + print(test.taxonID.value_counts()) + + ds = data.TreeDataset(df=test, train=False, config=config) + predictions = trainer.predict(m, dataloaders=m.predict_dataloader(ds)) + results = m.gather_predictions(predictions) + results["individual"] = results["individual"] + results_with_data = results.merge(crowns, on="individual") + comet_logger.experiment.log_table("nested_predictions.csv", results_with_data) + + ensemble_df = m.ensemble(results) + truth_cols = test.drop_duplicates(subset=["individual"])[["individual", "label", "siteID"]] + ensemble_df = ensemble_df.merge(truth_cols, on="individual", how="inner") + if ensemble_df.empty: + print( + "[train] warning: no individuals overlap between predictions and test labels " + "(e.g. limit_predict_batches too small); skipping evaluation_scores." + ) + else: + exp_for_eval = comet_logger.experiment if isinstance(comet_logger, CometLogger) else None + ensemble_df = m.evaluation_scores(ensemble_df, experiment=exp_for_eval) + + comet_logger.experiment.log_table("ensemble_df.csv", ensemble_df) + -#Get branch name for the comet tag -git_branch=sys.argv[1] -git_commit=sys.argv[2] - -#Create datamodule -config = data.read_config("config.yml") -comet_logger = CometLogger(project_name="DeepTreeAttention2", workspace=config["comet_workspace"], auto_output_logging="simple") - -#Generate new data or use previous run -if config["use_data_commit"]: - config["crop_dir"] = os.path.join(config["data_dir"], config["use_data_commit"]) - client = None -else: - crop_dir = os.path.join(config["data_dir"], comet_logger.experiment.get_key()) - os.mkdir(crop_dir) - client = start_cluster.start(cpus=10, mem_size="4GB") - config["crop_dir"] = crop_dir - -comet_logger.experiment.log_parameter("git branch",git_branch) -comet_logger.experiment.add_tag(git_branch) -comet_logger.experiment.log_parameter("commit hash",git_commit) -comet_logger.experiment.log_parameters(config) - -data_module = data.TreeData( - csv_file="data/raw/neon_vst_data_2022.csv", - data_dir=config["crop_dir"], - config=config, - client=client, - metadata=True, - comet_logger=comet_logger) - -if client: - client.close() - -comet_logger.experiment.log_parameter("train_hash",hash_pandas_object(data_module.train)) -comet_logger.experiment.log_parameter("test_hash",hash_pandas_object(data_module.test)) -comet_logger.experiment.log_parameter("num_species",data_module.num_classes) -comet_logger.experiment.log_table("train.csv", data_module.train) -comet_logger.experiment.log_table("test.csv", data_module.test) - -if not config["use_data_commit"]: - comet_logger.experiment.log_table("novel_species.csv", data_module.novel) - -train = data_module.train.copy() -test = data_module.test.copy() -crowns = data_module.crowns.copy() - -train["individual"] = train["individualID"] -test["individual"] = test["individualID"] - -#remove graves -train = train[~train.individual.str.contains("graves")].reset_index(drop=True) -test = test[~test.individual.str.contains("graves")].reset_index(drop=True) - -m = multi_stage.MultiStage(train, test, config=data_module.config, crowns=crowns) - -#Save the train df for each level for inspection -for index, train_df in enumerate([m.level_0_train, - m.level_1_train, m.level_2_train, m.level_3_train, m.level_4_train]): - comet_logger.experiment.log_table("train_level_{}.csv".format(index), train_df) - -#Save the train df for each level for inspection -for index, test_df in enumerate([m.level_0_test, - m.level_1_test, m.level_2_test, m.level_3_test, m.level_4_test]): - comet_logger.experiment.log_table("test_level_{}.csv".format(index), test_df) - -#Create trainer -lr_monitor = LearningRateMonitor(logging_interval='epoch') -trainer = Trainer( - gpus=data_module.config["gpus"], - fast_dev_run=data_module.config["fast_dev_run"], - max_epochs=data_module.config["epochs"], - accelerator=data_module.config["accelerator"], - num_sanity_val_steps=0, - enable_checkpointing=False, - callbacks=[lr_monitor], - logger=comet_logger, - profiler="simple") - -trainer.fit(m) - -#Save model checkpoint -trainer.save_checkpoint("/blue/ewhite/b.weinstein/DeepTreeAttention/snapshots/{}.pt".format(comet_logger.experiment.id)) - -# Prediction datasets are indexed by year, but full data is given to each model before ensembling -print("Before prediction, the taxonID value counts") -print(test.taxonID.value_counts()) - -ds = data.TreeDataset(df=test, train=False, config=config) -predictions = trainer.predict(m, dataloaders=m.predict_dataloader(ds)) -results = m.gather_predictions(predictions) -results["individual"] = results["individual"] -results_with_data = results.merge(crowns, on="individual") -comet_logger.experiment.log_table("nested_predictions.csv", results_with_data) - -ensemble_df = m.ensemble(results) -ensemble_df = m.evaluation_scores( - ensemble_df, - experiment=comet_logger.experiment -) - -#Log prediction -comet_logger.experiment.log_table("ensemble_df.csv", ensemble_df) - -#Visualizations -ensemble_df["pred_taxa_top1"] = ensemble_df.ensembleTaxonID -ensemble_df["pred_label_top1"] = ensemble_df.ens_label -rgb_pool = glob.glob(data_module.config["rgb_sensor_pool"], recursive=True) - -#Limit to 1 individual for confusion matrix -ensemble_df = ensemble_df.reset_index(drop=True) -ensemble_df = ensemble_df.groupby("individual").apply(lambda x: x.head(1)) -test = test.groupby("individual").apply(lambda x: x.head(1)).reset_index(drop=True) -visualize.confusion_matrix( - comet_experiment=comet_logger.experiment, - results=ensemble_df, - species_label_dict=data_module.species_label_dict, - test_crowns=crowns, - test=test, - test_points=data_module.canopy_points, - rgb_pool=rgb_pool -) +if __name__ == "__main__": + main_train() diff --git a/train_dead.py b/train_dead.py index 2030dd550..bb872510a 100644 --- a/train_dead.py +++ b/train_dead.py @@ -1,27 +1,35 @@ -# Train Dead +# Train Dead import comet_ml from pytorch_lightning import Trainer from pytorch_lightning.loggers import CometLogger + +from src import utils from src.models import dead -from src.data import read_config import numpy as np import torch from sklearn.metrics import precision_recall_curve, PrecisionRecallDisplay -config = read_config("config.yml") +config = utils.read_config("config.yml") comet_logger = CometLogger( - project_name="DeepTreeAttention", + project="DeepTreeAttention", workspace=config["comet_workspace"], - auto_output_logging="simple" -) + auto_output_logging="simple", +) comet_logger.experiment.add_tag("Dead") -trainer = Trainer(max_epochs=config["dead"]["epochs"], checkpoint_callback=False, gpus=config["gpus"], logger=comet_logger) +acc, dev = utils.trainer_accelerator_devices(config) +trainer = Trainer( + max_epochs=config["dead"]["epochs"], + enable_checkpointing=False, + accelerator=acc, + devices=dev, + logger=comet_logger, +) m = dead.AliveDead(config=config) trainer.fit(m) trainer.validate(m) 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