diff --git a/experiments/temporal-models/pyro-detector-baseline/.dvc/.gitignore b/experiments/temporal-models/pyro-detector-baseline/.dvc/.gitignore new file mode 100644 index 0000000..528f30c --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/experiments/temporal-models/pyro-detector-baseline/.dvc/config b/experiments/temporal-models/pyro-detector-baseline/.dvc/config new file mode 100644 index 0000000..02d62d9 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/.dvc/config @@ -0,0 +1,5 @@ +[core] + remote = s3remote + analytics = false +['remote "s3remote"'] + url = s3://pyro-vision-rd/dvc/experiments/pyro-detector-baseline/ diff --git a/experiments/temporal-models/pyro-detector-baseline/.dvcignore b/experiments/temporal-models/pyro-detector-baseline/.dvcignore new file mode 100644 index 0000000..5197305 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/experiments/temporal-models/pyro-detector-baseline/.python-version b/experiments/temporal-models/pyro-detector-baseline/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/experiments/temporal-models/pyro-detector-baseline/Makefile b/experiments/temporal-models/pyro-detector-baseline/Makefile new file mode 100644 index 0000000..ce01aa3 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/Makefile @@ -0,0 +1,17 @@ +.PHONY: install lint format test help + +help: ## Show this help + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-15s\033[0m %s\n", $$1, $$2}' + +install: ## Install dependencies and set up notebook output stripping + uv sync + uv run nbstripout --install + +lint: ## Run ruff linter on code and notebooks + uv run ruff check . + +format: ## Format code and notebooks with ruff + uv run ruff format . + +test: ## Run tests with pytest + uv run pytest tests/ -v diff --git a/experiments/temporal-models/pyro-detector-baseline/README.md b/experiments/temporal-models/pyro-detector-baseline/README.md new file mode 100644 index 0000000..45c0655 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/README.md @@ -0,0 +1,102 @@ +# Pyro-Detector Baseline + +## Objective + +Evaluate the production pyro-engine detection pipeline (`pyro-predictor`) as a +`TemporalModel` baseline. Provides a reference point for comparing new temporal +models against the actual production system. + +## Approach + +1. Wrap `pyro-predictor`'s `Predictor` class (YOLO ONNX detection + per-camera + sliding-window temporal smoothing) as a pyrocore `TemporalModel` subclass. +2. Run YOLO inference once per frame and cache detections (`infer` stage). +3. Replay cached detections through the Predictor's temporal logic (`predict` + stage) — classifies a sequence as positive when the aggregated confidence + exceeds `conf_thresh`. +4. Grid-search `conf_thresh` x `nb_consecutive_frames` on cached detections + (`sweep` stage) to find optimal parameters. +5. Compute sequence-level metrics (precision, recall, F1, FPR, TTD). + +The Predictor mirrors production behavior: +- YOLO ONNX model for single-frame detection (weights from + `pyronear/yolo11s_nimble-narwhal_v6.0.0`) +- Per-camera sliding window for temporal smoothing +- Aggregated confidence output in [0, 1] + +## Data + +Imported from [pyro-dataset](https://github.com/pyronear/pyro-dataset) v2.2.0 +via `dvc import` (sequential_train_val split): + +- **Train**: 1,467 wildfire + 1,467 FP sequences +- **Val**: 151 wildfire + 151 FP sequences +- Layout: `data/01_raw/datasets/{train,val}/{wildfire,fp}/sequence_name/{images,labels}/` +- Ground truth: inferred from parent directory name (`wildfire/` = positive, `fp/` = negative) + +## Results + +### Evaluate (optimized params: conf=0.2, nb_frames=4) + +| Split | Method | Precision | Recall | F1 | FPR | Median TTD | +|---|---|---|---|---|---|---| +| val/all | Single-frame | 0.853 | 0.960 | 0.903 | 0.166 | -- | +| val/all | Predictor | 0.892 | 0.934 | **0.913** | 0.113 | 9s | +| train/all | Single-frame | 0.820 | 0.981 | 0.893 | 0.215 | -- | +| train/all | Predictor | 0.849 | 0.971 | **0.906** | 0.172 | 20s | + +### Sweep top configs (val/all, ranked by F1) + +| conf | nb_frames | Precision | Recall | F1 | FPR | Mean TTD | +|---|---|---|---|---|---|---| +| 0.20 | 4 | 0.892 | 0.934 | **0.913** | 0.113 | 90s | +| 0.25 | 3 | 0.897 | 0.927 | 0.912 | 0.106 | 80s | +| 0.20 | 5 | 0.892 | 0.927 | 0.909 | 0.113 | 100s | +| 0.30 | 2 | 0.892 | 0.927 | 0.909 | 0.113 | 72s | + +Production defaults (conf=0.35, nb_frames=7) scored F1=0.825 on val/all. +The sweep-optimized params improve F1 by ~9pp. + +## How to Reproduce + +```bash +cd experiments/temporal-models/pyro-detector-baseline +make install + +# Dataset is imported via DVC from pyro-dataset v2.2.0: +# uv run dvc import https://github.com/pyronear/pyro-dataset \ +# data/processed/sequential_train_val/train \ +# -o data/01_raw/datasets/train --rev v2.2.0 +# uv run dvc import https://github.com/pyronear/pyro-dataset \ +# data/processed/sequential_train_val/val \ +# -o data/01_raw/datasets/val --rev v2.2.0 +# The .dvc files are committed — just pull: +uv run dvc pull + +uv run dvc repro +uv run dvc metrics show +``` + +## Pipeline + +``` +prepare (download ONNX model from HuggingFace) + -> infer (01_raw -> 03_primary) # YOLO-only, cached per-frame detections + -> predict (03_primary -> 07_model_output) # replay temporal logic (fixed params) + -> evaluate (-> 08_reporting) + -> sweep (03_primary -> 08_reporting/sweep) # grid-search temporal params + -> package (01_raw -> 06_models) +``` + +## Parameters + +Current values are sweep-optimized. Production defaults from +[pyro-engine](https://github.com/pyronear/pyro-engine) are shown for +reference. + +| Parameter | Value | Production | Description | +|---|---|---|---| +| `predict.conf_thresh` | **0.2** | 0.35 | **Alert threshold.** The Predictor accumulates detections across the sliding window and computes an aggregated confidence score. An alert fires when this score exceeds `conf_thresh`. Also used to filter individual detections before aggregation. | +| `predict.model_conf_thresh` | 0.05 | 0.05 | **YOLO detection threshold.** Minimum confidence for the YOLO model to report a bounding box. Kept low so the temporal layer sees all candidate detections. | +| `predict.nb_consecutive_frames` | **4** | 7 | **Sliding window size.** Number of recent frames the Predictor keeps in its per-camera state for temporal smoothing. A detection must persist across enough of these frames to trigger an alert. | +| `predict.max_bbox_size` | 0.4 | 0.4 | **Max detection width.** Detections wider than this fraction of the image are discarded (filters out large false positives like clouds). | diff --git a/experiments/temporal-models/pyro-detector-baseline/data/01_raw/.gitignore b/experiments/temporal-models/pyro-detector-baseline/data/01_raw/.gitignore new file mode 100644 index 0000000..f633594 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/01_raw/.gitignore @@ -0,0 +1 @@ +/models diff --git a/experiments/temporal-models/pyro-detector-baseline/data/01_raw/datasets/.gitignore b/experiments/temporal-models/pyro-detector-baseline/data/01_raw/datasets/.gitignore new file mode 100644 index 0000000..a5d9d98 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/01_raw/datasets/.gitignore @@ -0,0 +1,2 @@ +/train +/val diff --git a/experiments/temporal-models/pyro-detector-baseline/data/01_raw/datasets/train.dvc b/experiments/temporal-models/pyro-detector-baseline/data/01_raw/datasets/train.dvc new file mode 100644 index 0000000..9f3d6c5 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/01_raw/datasets/train.dvc @@ -0,0 +1,14 @@ +md5: ceb8e6b2a605cfe4d97815aeee233176 +frozen: true +deps: +- path: data/processed/sequential_train_val/train + repo: + url: https://github.com/pyronear/pyro-dataset + rev: v2.2.0 + rev_lock: ca3b7bbfc9f072d101849e99a5ec91791a3e7c7a +outs: +- md5: 22ee5d57943a0ff900121ca28b18f2a8.dir + size: 7149007108 + nfiles: 135742 + hash: md5 + path: train diff --git a/experiments/temporal-models/pyro-detector-baseline/data/01_raw/datasets/val.dvc b/experiments/temporal-models/pyro-detector-baseline/data/01_raw/datasets/val.dvc new file mode 100644 index 0000000..1281799 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/01_raw/datasets/val.dvc @@ -0,0 +1,14 @@ +md5: 180164308103dcd9b3ebdbb2395deac6 +frozen: true +deps: +- path: data/processed/sequential_train_val/val + repo: + url: https://github.com/pyronear/pyro-dataset + rev: v2.2.0 + rev_lock: ca3b7bbfc9f072d101849e99a5ec91791a3e7c7a +outs: +- md5: 2ca47638a4b0cc9ca7d4011376c54e45.dir + size: 631259421 + nfiles: 12448 + hash: md5 + path: val diff --git a/experiments/temporal-models/pyro-detector-baseline/data/02_intermediate/.gitkeep b/experiments/temporal-models/pyro-detector-baseline/data/02_intermediate/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/experiments/temporal-models/pyro-detector-baseline/data/03_primary/.gitignore b/experiments/temporal-models/pyro-detector-baseline/data/03_primary/.gitignore new file mode 100644 index 0000000..ef615c5 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/03_primary/.gitignore @@ -0,0 +1,2 @@ +/val +/train diff --git a/experiments/temporal-models/pyro-detector-baseline/data/04_feature/.gitkeep b/experiments/temporal-models/pyro-detector-baseline/data/04_feature/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/experiments/temporal-models/pyro-detector-baseline/data/05_model_input/.gitkeep b/experiments/temporal-models/pyro-detector-baseline/data/05_model_input/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/experiments/temporal-models/pyro-detector-baseline/data/06_models/.gitignore b/experiments/temporal-models/pyro-detector-baseline/data/06_models/.gitignore new file mode 100644 index 0000000..79f9cd4 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/06_models/.gitignore @@ -0,0 +1 @@ +/model.zip diff --git a/experiments/temporal-models/pyro-detector-baseline/data/07_model_output/.gitignore b/experiments/temporal-models/pyro-detector-baseline/data/07_model_output/.gitignore new file mode 100644 index 0000000..ef615c5 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/07_model_output/.gitignore @@ -0,0 +1,2 @@ +/val +/train diff --git a/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/sweep/train/.gitignore b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/sweep/train/.gitignore new file mode 100644 index 0000000..22258fc --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/sweep/train/.gitignore @@ -0,0 +1,2 @@ +/all +/pyronear diff --git a/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/sweep/val/.gitignore b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/sweep/val/.gitignore new file mode 100644 index 0000000..22258fc --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/sweep/val/.gitignore @@ -0,0 +1,2 @@ +/all +/pyronear diff --git a/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/train/all/.gitignore b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/train/all/.gitignore new file mode 100644 index 0000000..516467e --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/train/all/.gitignore @@ -0,0 +1 @@ +/plots diff --git a/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/train/all/metrics.json b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/train/all/metrics.json new file mode 100644 index 0000000..b0320d1 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/train/all/metrics.json @@ -0,0 +1,40 @@ +{ + "single_frame": { + "num_sequences": 2933, + "num_positive_gt": 1466, + "num_negative_gt": 1467, + "tp": 1438, + "fp": 315, + "fn": 28, + "tn": 1152, + "tp_pct": 98.09, + "fp_pct": 21.47, + "fn_pct": 1.91, + "tn_pct": 78.53, + "precision": 0.8203, + "recall": 0.9809, + "f1": 0.8934, + "fpr": 0.2147, + "mean_ttd_seconds": 0.0, + "median_ttd_seconds": 0.0 + }, + "predictor": { + "num_sequences": 2933, + "num_positive_gt": 1466, + "num_negative_gt": 1467, + "tp": 1424, + "fp": 253, + "fn": 42, + "tn": 1214, + "tp_pct": 97.14, + "fp_pct": 17.25, + "fn_pct": 2.86, + "tn_pct": 82.75, + "precision": 0.8491, + "recall": 0.9714, + "f1": 0.9061, + "fpr": 0.1725, + "mean_ttd_seconds": 80.6, + "median_ttd_seconds": 20.0 + } +} \ No newline at end of file diff --git a/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/train/pyronear/.gitignore b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/train/pyronear/.gitignore new file mode 100644 index 0000000..516467e --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/train/pyronear/.gitignore @@ -0,0 +1 @@ +/plots diff --git a/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/train/pyronear/metrics.json b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/train/pyronear/metrics.json new file mode 100644 index 0000000..9aeab9c --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/train/pyronear/metrics.json @@ -0,0 +1,40 @@ +{ + "single_frame": { + "num_sequences": 2536, + "num_positive_gt": 1069, + "num_negative_gt": 1467, + "tp": 1058, + "fp": 315, + "fn": 11, + "tn": 1152, + "tp_pct": 98.97, + "fp_pct": 21.47, + "fn_pct": 1.03, + "tn_pct": 78.53, + "precision": 0.7706, + "recall": 0.9897, + "f1": 0.8665, + "fpr": 0.2147, + "mean_ttd_seconds": 0.0, + "median_ttd_seconds": 0.0 + }, + "predictor": { + "num_sequences": 2536, + "num_positive_gt": 1069, + "num_negative_gt": 1467, + "tp": 1047, + "fp": 253, + "fn": 22, + "tn": 1214, + "tp_pct": 97.94, + "fp_pct": 17.25, + "fn_pct": 2.06, + "tn_pct": 82.75, + "precision": 0.8054, + "recall": 0.9794, + "f1": 0.8839, + "fpr": 0.1725, + "mean_ttd_seconds": 41.7, + "median_ttd_seconds": 7.0 + } +} \ No newline at end of file diff --git a/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/val/all/.gitignore b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/val/all/.gitignore new file mode 100644 index 0000000..516467e --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/val/all/.gitignore @@ -0,0 +1 @@ +/plots diff --git a/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/val/all/metrics.json b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/val/all/metrics.json new file mode 100644 index 0000000..2bb3651 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/val/all/metrics.json @@ -0,0 +1,40 @@ +{ + "single_frame": { + "num_sequences": 302, + "num_positive_gt": 151, + "num_negative_gt": 151, + "tp": 145, + "fp": 25, + "fn": 6, + "tn": 126, + "tp_pct": 96.03, + "fp_pct": 16.56, + "fn_pct": 3.97, + "tn_pct": 83.44, + "precision": 0.8529, + "recall": 0.9603, + "f1": 0.9034, + "fpr": 0.1656, + "mean_ttd_seconds": 0.0, + "median_ttd_seconds": 0.0 + }, + "predictor": { + "num_sequences": 302, + "num_positive_gt": 151, + "num_negative_gt": 151, + "tp": 141, + "fp": 17, + "fn": 10, + "tn": 134, + "tp_pct": 93.38, + "fp_pct": 11.26, + "fn_pct": 6.62, + "tn_pct": 88.74, + "precision": 0.8924, + "recall": 0.9338, + "f1": 0.9126, + "fpr": 0.1126, + "mean_ttd_seconds": 90.4, + "median_ttd_seconds": 9.0 + } +} \ No newline at end of file diff --git a/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/val/pyronear/.gitignore b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/val/pyronear/.gitignore new file mode 100644 index 0000000..516467e --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/val/pyronear/.gitignore @@ -0,0 +1 @@ +/plots diff --git a/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/val/pyronear/metrics.json b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/val/pyronear/metrics.json new file mode 100644 index 0000000..851eba5 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/data/08_reporting/val/pyronear/metrics.json @@ -0,0 +1,40 @@ +{ + "single_frame": { + "num_sequences": 267, + "num_positive_gt": 116, + "num_negative_gt": 151, + "tp": 113, + "fp": 25, + "fn": 3, + "tn": 126, + "tp_pct": 97.41, + "fp_pct": 16.56, + "fn_pct": 2.59, + "tn_pct": 83.44, + "precision": 0.8188, + "recall": 0.9741, + "f1": 0.8898, + "fpr": 0.1656, + "mean_ttd_seconds": 0.0, + "median_ttd_seconds": 0.0 + }, + "predictor": { + "num_sequences": 267, + "num_positive_gt": 116, + "num_negative_gt": 151, + "tp": 111, + "fp": 17, + "fn": 5, + "tn": 134, + "tp_pct": 95.69, + "fp_pct": 11.26, + "fn_pct": 4.31, + "tn_pct": 88.74, + "precision": 0.8672, + "recall": 0.9569, + "f1": 0.9098, + "fpr": 0.1126, + "mean_ttd_seconds": 21.9, + "median_ttd_seconds": 5.0 + } +} \ No newline at end of file diff --git a/experiments/temporal-models/pyro-detector-baseline/dvc.lock b/experiments/temporal-models/pyro-detector-baseline/dvc.lock new file mode 100644 index 0000000..0aa609f --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/dvc.lock @@ -0,0 +1,446 @@ +schema: '2.0' +stages: + prepare: + cmd: uv run python scripts/prepare.py --model-repo + pyronear/yolo11s_nimble-narwhal_v6.0.0 --model-filename onnx_cpu.tar.gz + --output-model-dir data/01_raw/models + deps: + - path: scripts/prepare.py + hash: md5 + md5: a06968e134716e738d04f1fe5cffa356 + size: 2463 + params: + params.yaml: + prepare: + model_repo: pyronear/yolo11s_nimble-narwhal_v6.0.0 + model_filename: onnx_cpu.tar.gz + outs: + - path: data/01_raw/models + hash: md5 + md5: cb02d212f75c68f1c83fffdfa64d66f4.dir + size: 71127408 + nfiles: 4 + predict@val: + cmd: uv run python scripts/predict.py --infer-dir data/03_primary/val + --data-dir data/01_raw/datasets/val --output-dir data/07_model_output/val + --conf-thresh 0.2 --nb-consecutive-frames 4 + deps: + - path: data/01_raw/datasets/val + hash: md5 + md5: 2ca47638a4b0cc9ca7d4011376c54e45.dir + size: 631259421 + nfiles: 12448 + - path: data/03_primary/val + hash: md5 + md5: 586e0caef5bcd2373a4e099f984586da.dir + size: 1002853 + nfiles: 302 + - path: scripts/predict.py + hash: md5 + md5: c468ccec035e1bfadf4ea6d06a338717 + size: 4469 + - path: src/pyro_detector_baseline/data.py + hash: md5 + md5: 2ff239d58eac9694ef8ae3d07e5410ee + size: 3553 + - path: src/pyro_detector_baseline/predictor_wrapper.py + hash: md5 + md5: 1d8b78500bab5f515aaa058b3802f24d + size: 5259 + params: + params.yaml: + predict.conf_thresh: 0.2 + predict.nb_consecutive_frames: 4 + outs: + - path: data/07_model_output/val + hash: md5 + md5: 9250810f37dfc6b2564d26744ebd7b06.dir + size: 221826 + nfiles: 1 + evaluate@val_all: + cmd: 'uv run python scripts/evaluate.py --track-dir data/07_model_output/val --output-dir + data/08_reporting/val/all ' + deps: + - path: data/07_model_output/val + hash: md5 + md5: 9250810f37dfc6b2564d26744ebd7b06.dir + size: 221826 + nfiles: 1 + - path: scripts/evaluate.py + hash: md5 + md5: fb9c7ea2e105f28eb457932ed6c3cc76 + size: 3608 + - path: src/pyro_detector_baseline/evaluator.py + hash: md5 + md5: 873acf4e0538965efb5ac88164252c3a + size: 8280 + outs: + - path: data/08_reporting/val/all/metrics.json + hash: md5 + md5: d2f29617e99d52c22fd4a57023285fea + size: 781 + - path: data/08_reporting/val/all/plots + hash: md5 + md5: a1d7010477118f1c1df3d9afcceca5c8.dir + size: 101491 + nfiles: 4 + evaluate@val_pyronear: + cmd: uv run python scripts/evaluate.py --track-dir data/07_model_output/val + --output-dir data/08_reporting/val/pyronear --filter-prefix pyronear + deps: + - path: data/07_model_output/val + hash: md5 + md5: 9250810f37dfc6b2564d26744ebd7b06.dir + size: 221826 + nfiles: 1 + - path: scripts/evaluate.py + hash: md5 + md5: fb9c7ea2e105f28eb457932ed6c3cc76 + size: 3608 + - path: src/pyro_detector_baseline/evaluator.py + hash: md5 + md5: 873acf4e0538965efb5ac88164252c3a + size: 8280 + outs: + - path: data/08_reporting/val/pyronear/metrics.json + hash: md5 + md5: f29f0a62679c1453517896e2e56b3fc5 + size: 780 + - path: data/08_reporting/val/pyronear/plots + hash: md5 + md5: ce652e293b54c3ca86ba8a2a66df4d27.dir + size: 95991 + nfiles: 4 + package: + cmd: uv run python scripts/package.py --model-dir data/01_raw/models + --params-path params.yaml --output-path data/06_models/model.zip + deps: + - path: data/01_raw/models + hash: md5 + md5: cb02d212f75c68f1c83fffdfa64d66f4.dir + size: 71127408 + nfiles: 4 + - path: scripts/package.py + hash: md5 + md5: af6f6892ef8728a6b5a0ddb4d150ba76 + size: 2053 + - path: src/pyro_detector_baseline/package.py + hash: md5 + md5: ac261bf1ba687d687967f8891783d1d8 + size: 3569 + params: + params.yaml: + predict: + conf_thresh: 0.2 + model_conf_thresh: 0.05 + nb_consecutive_frames: 4 + max_bbox_size: 0.4 + outs: + - path: data/06_models/model.zip + hash: md5 + md5: 8df77be9c5cd9a120569730c4160098a + size: 38513179 + infer@val: + cmd: uv run python scripts/infer.py --data-dir data/01_raw/datasets/val + --model-dir data/01_raw/models --output-dir data/03_primary/val + --model-conf-thresh 0.05 --max-bbox-size 0.4 + deps: + - path: data/01_raw/datasets/val + hash: md5 + md5: 2ca47638a4b0cc9ca7d4011376c54e45.dir + size: 631259421 + nfiles: 12448 + - path: data/01_raw/models + hash: md5 + md5: cb02d212f75c68f1c83fffdfa64d66f4.dir + size: 71127408 + nfiles: 4 + - path: scripts/infer.py + hash: md5 + md5: 240ebf07e769eb6f189f517bbff22127 + size: 3414 + - path: src/pyro_detector_baseline/data.py + hash: md5 + md5: 2ff239d58eac9694ef8ae3d07e5410ee + size: 3553 + params: + params.yaml: + predict.max_bbox_size: 0.4 + predict.model_conf_thresh: 0.05 + outs: + - path: data/03_primary/val + hash: md5 + md5: 586e0caef5bcd2373a4e099f984586da.dir + size: 1002853 + nfiles: 302 + sweep@val_all: + cmd: 'uv run python scripts/sweep.py --infer-dir data/03_primary/val --data-dir + data/01_raw/datasets/val --output-dir data/08_reporting/sweep/val/all ' + deps: + - path: data/01_raw/datasets/val + hash: md5 + md5: 2ca47638a4b0cc9ca7d4011376c54e45.dir + size: 631259421 + nfiles: 12448 + - path: data/03_primary/val + hash: md5 + md5: 586e0caef5bcd2373a4e099f984586da.dir + size: 1002853 + nfiles: 302 + - path: scripts/sweep.py + hash: md5 + md5: 7e150b4201e840171ec29fdea8551c0d + size: 7164 + - path: src/pyro_detector_baseline/data.py + hash: md5 + md5: 2ff239d58eac9694ef8ae3d07e5410ee + size: 3553 + - path: src/pyro_detector_baseline/evaluator.py + hash: md5 + md5: 873acf4e0538965efb5ac88164252c3a + size: 8280 + - path: src/pyro_detector_baseline/predictor_wrapper.py + hash: md5 + md5: 1d8b78500bab5f515aaa058b3802f24d + size: 5259 + outs: + - path: data/08_reporting/sweep/val/all + hash: md5 + md5: ffce0faf22f724c67f77dc0ae5958ff6.dir + size: 6161 + nfiles: 1 + infer@train: + cmd: uv run python scripts/infer.py --data-dir data/01_raw/datasets/train + --model-dir data/01_raw/models --output-dir data/03_primary/train + --model-conf-thresh 0.05 --max-bbox-size 0.4 + deps: + - path: data/01_raw/datasets/train + hash: md5 + md5: 22ee5d57943a0ff900121ca28b18f2a8.dir + size: 7149007108 + nfiles: 135742 + - path: data/01_raw/models + hash: md5 + md5: cb02d212f75c68f1c83fffdfa64d66f4.dir + size: 71127408 + nfiles: 4 + - path: scripts/infer.py + hash: md5 + md5: 240ebf07e769eb6f189f517bbff22127 + size: 3414 + - path: src/pyro_detector_baseline/data.py + hash: md5 + md5: 2ff239d58eac9694ef8ae3d07e5410ee + size: 3553 + params: + params.yaml: + predict.max_bbox_size: 0.4 + predict.model_conf_thresh: 0.05 + outs: + - path: data/03_primary/train + hash: md5 + md5: 23cde1b84bcf6d46a92d7047375b0325.dir + size: 11627115 + nfiles: 2933 + sweep@train_all: + cmd: 'uv run python scripts/sweep.py --infer-dir data/03_primary/train --data-dir + data/01_raw/datasets/train --output-dir data/08_reporting/sweep/train/all ' + deps: + - path: data/01_raw/datasets/train + hash: md5 + md5: 22ee5d57943a0ff900121ca28b18f2a8.dir + size: 7149007108 + nfiles: 135742 + - path: data/03_primary/train + hash: md5 + md5: 23cde1b84bcf6d46a92d7047375b0325.dir + size: 11627115 + nfiles: 2933 + - path: scripts/sweep.py + hash: md5 + md5: 7e150b4201e840171ec29fdea8551c0d + size: 7164 + - path: src/pyro_detector_baseline/data.py + hash: md5 + md5: 2ff239d58eac9694ef8ae3d07e5410ee + size: 3553 + - path: src/pyro_detector_baseline/evaluator.py + hash: md5 + md5: 873acf4e0538965efb5ac88164252c3a + size: 8280 + - path: src/pyro_detector_baseline/predictor_wrapper.py + hash: md5 + md5: 1d8b78500bab5f515aaa058b3802f24d + size: 5259 + outs: + - path: data/08_reporting/sweep/train/all + hash: md5 + md5: 03fcfa0ec3f410a88e32ea7471f879cd.dir + size: 6637 + nfiles: 1 + predict@train: + cmd: uv run python scripts/predict.py --infer-dir data/03_primary/train + --data-dir data/01_raw/datasets/train --output-dir + data/07_model_output/train --conf-thresh 0.2 --nb-consecutive-frames 4 + deps: + - path: data/01_raw/datasets/train + hash: md5 + md5: 22ee5d57943a0ff900121ca28b18f2a8.dir + size: 7149007108 + nfiles: 135742 + - path: data/03_primary/train + hash: md5 + md5: 23cde1b84bcf6d46a92d7047375b0325.dir + size: 11627115 + nfiles: 2933 + - path: scripts/predict.py + hash: md5 + md5: c468ccec035e1bfadf4ea6d06a338717 + size: 4469 + - path: src/pyro_detector_baseline/data.py + hash: md5 + md5: 2ff239d58eac9694ef8ae3d07e5410ee + size: 3553 + - path: src/pyro_detector_baseline/predictor_wrapper.py + hash: md5 + md5: 1d8b78500bab5f515aaa058b3802f24d + size: 5259 + params: + params.yaml: + predict.conf_thresh: 0.2 + predict.nb_consecutive_frames: 4 + outs: + - path: data/07_model_output/train + hash: md5 + md5: a34c4541dd5e7b40c5e2e16fd950a362.dir + size: 2392061 + nfiles: 1 + sweep@train_pyronear: + cmd: uv run python scripts/sweep.py --infer-dir data/03_primary/train + --data-dir data/01_raw/datasets/train --output-dir + data/08_reporting/sweep/train/pyronear --filter-prefix pyronear + deps: + - path: data/01_raw/datasets/train + hash: md5 + md5: 22ee5d57943a0ff900121ca28b18f2a8.dir + size: 7149007108 + nfiles: 135742 + - path: data/03_primary/train + hash: md5 + md5: 23cde1b84bcf6d46a92d7047375b0325.dir + size: 11627115 + nfiles: 2933 + - path: scripts/sweep.py + hash: md5 + md5: 7e150b4201e840171ec29fdea8551c0d + size: 7164 + - path: src/pyro_detector_baseline/data.py + hash: md5 + md5: 2ff239d58eac9694ef8ae3d07e5410ee + size: 3553 + - path: src/pyro_detector_baseline/evaluator.py + hash: md5 + md5: 873acf4e0538965efb5ac88164252c3a + size: 8280 + - path: src/pyro_detector_baseline/predictor_wrapper.py + hash: md5 + md5: 1d8b78500bab5f515aaa058b3802f24d + size: 5259 + outs: + - path: data/08_reporting/sweep/train/pyronear + hash: md5 + md5: 7236534aa05ac83af7ef6e84192a5ee9.dir + size: 6557 + nfiles: 1 + sweep@val_pyronear: + cmd: uv run python scripts/sweep.py --infer-dir data/03_primary/val + --data-dir data/01_raw/datasets/val --output-dir + data/08_reporting/sweep/val/pyronear --filter-prefix pyronear + deps: + - path: data/01_raw/datasets/val + hash: md5 + md5: 2ca47638a4b0cc9ca7d4011376c54e45.dir + size: 631259421 + nfiles: 12448 + - path: data/03_primary/val + hash: md5 + md5: 586e0caef5bcd2373a4e099f984586da.dir + size: 1002853 + nfiles: 302 + - path: scripts/sweep.py + hash: md5 + md5: 7e150b4201e840171ec29fdea8551c0d + size: 7164 + - path: src/pyro_detector_baseline/data.py + hash: md5 + md5: 2ff239d58eac9694ef8ae3d07e5410ee + size: 3553 + - path: src/pyro_detector_baseline/evaluator.py + hash: md5 + md5: 873acf4e0538965efb5ac88164252c3a + size: 8280 + - path: src/pyro_detector_baseline/predictor_wrapper.py + hash: md5 + md5: 1d8b78500bab5f515aaa058b3802f24d + size: 5259 + outs: + - path: data/08_reporting/sweep/val/pyronear + hash: md5 + md5: 4018fc6999fbc8cc9ae8f8a2b29af91b.dir + size: 6052 + nfiles: 1 + evaluate@train_all: + cmd: 'uv run python scripts/evaluate.py --track-dir data/07_model_output/train + --output-dir data/08_reporting/train/all ' + deps: + - path: data/07_model_output/train + hash: md5 + md5: a34c4541dd5e7b40c5e2e16fd950a362.dir + size: 2392061 + nfiles: 1 + - path: scripts/evaluate.py + hash: md5 + md5: fb9c7ea2e105f28eb457932ed6c3cc76 + size: 3608 + - path: src/pyro_detector_baseline/evaluator.py + hash: md5 + md5: 873acf4e0538965efb5ac88164252c3a + size: 8280 + outs: + - path: data/08_reporting/train/all/metrics.json + hash: md5 + md5: 19bb37502e9ccb286cb8eb2cff922514 + size: 795 + - path: data/08_reporting/train/all/plots + hash: md5 + md5: 6c270fe1974ca2bd89fab127708a3cc1.dir + size: 103113 + nfiles: 4 + evaluate@train_pyronear: + cmd: uv run python scripts/evaluate.py --track-dir + data/07_model_output/train --output-dir data/08_reporting/train/pyronear + --filter-prefix pyronear + deps: + - path: data/07_model_output/train + hash: md5 + md5: a34c4541dd5e7b40c5e2e16fd950a362.dir + size: 2392061 + nfiles: 1 + - path: scripts/evaluate.py + hash: md5 + md5: fb9c7ea2e105f28eb457932ed6c3cc76 + size: 3608 + - path: src/pyro_detector_baseline/evaluator.py + hash: md5 + md5: 873acf4e0538965efb5ac88164252c3a + size: 8280 + outs: + - path: data/08_reporting/train/pyronear/metrics.json + hash: md5 + md5: 6ac45e595aba2722ff1e3b35e8ef7292 + size: 794 + - path: data/08_reporting/train/pyronear/plots + hash: md5 + md5: 53168deb07bc9fde372ce9159a8090a1.dir + size: 99793 + nfiles: 4 diff --git a/experiments/temporal-models/pyro-detector-baseline/dvc.yaml b/experiments/temporal-models/pyro-detector-baseline/dvc.yaml new file mode 100644 index 0000000..53aa3c1 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/dvc.yaml @@ -0,0 +1,156 @@ +stages: + # --- Prepare: download ONNX model weights --- + prepare: + desc: "Download ONNX model weights from HuggingFace Hub" + cmd: >- + uv run python scripts/prepare.py + --model-repo ${prepare.model_repo} + --model-filename ${prepare.model_filename} + --output-model-dir data/01_raw/models + deps: + - scripts/prepare.py + params: + - prepare + outs: + - data/01_raw/models + + # --- Predict --- + predict: + foreach: + - train + - val + do: + desc: "Replay temporal logic on ${item} set using cached detections" + cmd: >- + uv run python scripts/predict.py + --infer-dir data/03_primary/${item} + --data-dir data/01_raw/datasets/${item} + --output-dir data/07_model_output/${item} + --conf-thresh ${predict.conf_thresh} + --nb-consecutive-frames ${predict.nb_consecutive_frames} + deps: + - scripts/predict.py + - src/pyro_detector_baseline/data.py + - src/pyro_detector_baseline/predictor_wrapper.py + - data/03_primary/${item} + - data/01_raw/datasets/${item} + params: + - predict.conf_thresh + - predict.nb_consecutive_frames + outs: + - data/07_model_output/${item} + + # --- Package --- + package: + desc: "Bundle ONNX weights and predictor config into a single deployable archive" + cmd: >- + uv run python scripts/package.py + --model-dir data/01_raw/models + --params-path params.yaml + --output-path data/06_models/model.zip + deps: + - scripts/package.py + - src/pyro_detector_baseline/package.py + - data/01_raw/models + params: + - predict + outs: + - data/06_models/model.zip + + # --- Infer (YOLO-only, cached for sweep) --- + infer: + foreach: + - train + - val + do: + desc: "Run YOLO classifier on ${item} set frames (no temporal logic)" + cmd: >- + uv run python scripts/infer.py + --data-dir data/01_raw/datasets/${item} + --model-dir data/01_raw/models + --output-dir data/03_primary/${item} + --model-conf-thresh ${predict.model_conf_thresh} + --max-bbox-size ${predict.max_bbox_size} + deps: + - scripts/infer.py + - src/pyro_detector_baseline/data.py + - data/01_raw/datasets/${item} + - data/01_raw/models + params: + - predict.model_conf_thresh + - predict.max_bbox_size + outs: + - data/03_primary/${item} + + # --- Sweep --- + sweep: + foreach: + train_all: + split: train + subset: all + filter_flag: "" + val_all: + split: val + subset: all + filter_flag: "" + train_pyronear: + split: train + subset: pyronear + filter_flag: "--filter-prefix pyronear" + val_pyronear: + split: val + subset: pyronear + filter_flag: "--filter-prefix pyronear" + do: + desc: "Grid-search predictor parameters on ${item.split} set (${item.subset})" + cmd: >- + uv run python scripts/sweep.py + --infer-dir data/03_primary/${item.split} + --data-dir data/01_raw/datasets/${item.split} + --output-dir data/08_reporting/sweep/${item.split}/${item.subset} + ${item.filter_flag} + deps: + - scripts/sweep.py + - src/pyro_detector_baseline/evaluator.py + - src/pyro_detector_baseline/predictor_wrapper.py + - src/pyro_detector_baseline/data.py + - data/03_primary/${item.split} + - data/01_raw/datasets/${item.split} + outs: + - data/08_reporting/sweep/${item.split}/${item.subset} + + # --- Evaluate --- + evaluate: + foreach: + train_all: + split: train + subset: all + filter_flag: "" + val_all: + split: val + subset: all + filter_flag: "" + train_pyronear: + split: train + subset: pyronear + filter_flag: "--filter-prefix pyronear" + val_pyronear: + split: val + subset: pyronear + filter_flag: "--filter-prefix pyronear" + do: + desc: "Compute sequence-level metrics on ${item.split} set (${item.subset})" + cmd: >- + uv run python scripts/evaluate.py + --track-dir data/07_model_output/${item.split} + --output-dir data/08_reporting/${item.split}/${item.subset} + ${item.filter_flag} + deps: + - scripts/evaluate.py + - src/pyro_detector_baseline/evaluator.py + - data/07_model_output/${item.split} + metrics: + - data/08_reporting/${item.split}/${item.subset}/metrics.json: + cache: false + plots: + - data/08_reporting/${item.split}/${item.subset}/plots diff --git a/experiments/temporal-models/pyro-detector-baseline/params.yaml b/experiments/temporal-models/pyro-detector-baseline/params.yaml new file mode 100644 index 0000000..a03a1a7 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/params.yaml @@ -0,0 +1,10 @@ +prepare: + model_repo: "pyronear/yolo11s_nimble-narwhal_v6.0.0" + model_filename: "onnx_cpu.tar.gz" + +# Production defaults from pyro-engine's Engine class +predict: + conf_thresh: 0.2 + model_conf_thresh: 0.05 + nb_consecutive_frames: 4 + max_bbox_size: 0.4 diff --git a/experiments/temporal-models/pyro-detector-baseline/pyproject.toml b/experiments/temporal-models/pyro-detector-baseline/pyproject.toml new file mode 100644 index 0000000..532cdaf --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/pyproject.toml @@ -0,0 +1,50 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/pyro_detector_baseline"] + +[project] +name = "pyro-detector-baseline" +version = "0.1.0" +description = "Production pyro-predictor wrapped as TemporalModel baseline for comparison" +requires-python = ">=3.11" +dependencies = [ + "pyrocore", + "pyro-predictor", + "huggingface-hub>=0.24", + "matplotlib>=3.8", + "numpy>=1.26,<2", + "opencv-python-headless>=4.8", + "onnxruntime>=1.17", + "pandas>=2.1", + "pillow>=10.0", + "pyyaml>=6.0", + "seaborn>=0.13", + "tqdm>=4.66", + "ncnn>=1.0.20260114", +] + +[tool.uv.sources] +pyrocore = { path = "../../../lib/pyrocore" } +pyro-predictor = { git = "https://github.com/pyronear/pyro-engine", subdirectory = "pyro-predictor" } + +[dependency-groups] +dev = [ + "dvc[s3]>=3.56", + "nbqa>=1.9", + "nbstripout>=0.8", + "pytest>=8.0", + "ruff>=0.9", +] + +[tool.ruff] +target-version = "py311" +line-length = 88 + +[tool.ruff.lint] +select = ["E", "F", "I", "W", "UP", "B", "SIM", "PLC0415"] + +[tool.ruff.format] +quote-style = "double" diff --git a/experiments/temporal-models/pyro-detector-baseline/scripts/evaluate.py b/experiments/temporal-models/pyro-detector-baseline/scripts/evaluate.py new file mode 100644 index 0000000..6921723 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/scripts/evaluate.py @@ -0,0 +1,114 @@ +"""Evaluate prediction results against ground truth. + +Loads tracking_results.json, computes sequence-level metrics (precision, +recall, F1, FPR, time-to-detection) for both the Predictor and a +single-frame baseline (any detection = alarm). Generates plots: +confusion matrix, metric comparison bar chart, and TTD histogram. + +Usage: + uv run python scripts/evaluate.py \ + --track-dir data/07_model_output/val \ + --output-dir data/08_reporting/val/all +""" + +import argparse +import json +import logging +from pathlib import Path + +from pyro_detector_baseline.evaluator import ( + compute_metrics, + compute_single_frame_baseline, + load_tracking_results, + plot_comparison, + plot_confusion_matrix, + plot_confusion_matrix_percentages, + plot_ttd_histogram, +) + +logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") +logger = logging.getLogger(__name__) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Evaluate prediction results.") + parser.add_argument( + "--track-dir", + type=Path, + required=True, + help="Path to prediction results directory.", + ) + parser.add_argument( + "--output-dir", + type=Path, + required=True, + help="Output directory for metrics and plots.", + ) + parser.add_argument( + "--filter-prefix", + type=str, + default=None, + help="Only include sequences whose ID starts with this prefix.", + ) + args = parser.parse_args() + + args.output_dir.mkdir(parents=True, exist_ok=True) + plots_dir = args.output_dir / "plots" + plots_dir.mkdir(parents=True, exist_ok=True) + + results = load_tracking_results(args.track_dir / "tracking_results.json") + if args.filter_prefix: + results = [ + r for r in results if r["sequence_id"].startswith(args.filter_prefix) + ] + logger.info("Loaded %d sequence results.", len(results)) + + # Compute metrics + predictor_metrics = compute_metrics(results) + baseline_metrics = compute_single_frame_baseline(results) + + # Save metrics + all_metrics = { + "single_frame": baseline_metrics, + "predictor": predictor_metrics, + } + metrics_path = args.output_dir / "metrics.json" + metrics_path.write_text(json.dumps(all_metrics, indent=2)) + logger.info("Saved metrics to %s", metrics_path) + + # Print summary + logger.info("=== Single-frame Baseline ===") + logger.info( + " P=%.3f R=%.3f F1=%.3f FPR=%.3f", + baseline_metrics["precision"], + baseline_metrics["recall"], + baseline_metrics["f1"], + baseline_metrics["fpr"], + ) + logger.info("=== Predictor (temporal) ===") + logger.info( + " P=%.3f R=%.3f F1=%.3f FPR=%.3f", + predictor_metrics["precision"], + predictor_metrics["recall"], + predictor_metrics["f1"], + predictor_metrics["fpr"], + ) + if predictor_metrics["mean_ttd_seconds"] is not None: + logger.info( + " Mean TTD=%.1fs Median TTD=%.1fs", + predictor_metrics["mean_ttd_seconds"], + predictor_metrics["median_ttd_seconds"], + ) + + # Generate plots + plot_confusion_matrix(predictor_metrics, plots_dir / "confusion_matrix.png") + plot_confusion_matrix_percentages( + predictor_metrics, plots_dir / "confusion_matrix_percentages.png" + ) + plot_comparison(baseline_metrics, predictor_metrics, plots_dir / "comparison.png") + plot_ttd_histogram(results, plots_dir / "ttd_histogram.png") + logger.info("Saved plots to %s", plots_dir) + + +if __name__ == "__main__": + main() diff --git a/experiments/temporal-models/pyro-detector-baseline/scripts/infer.py b/experiments/temporal-models/pyro-detector-baseline/scripts/infer.py new file mode 100644 index 0000000..3b3b261 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/scripts/infer.py @@ -0,0 +1,120 @@ +"""Run YOLO classifier on all frames and cache per-frame detections. + +Saves post-processed bounding-box predictions as one JSON per sequence, +enabling fast parameter sweeps without re-running model inference. + +Usage: + uv run python scripts/infer.py \ + --data-dir data/01_raw/datasets/val \ + --model-dir data/01_raw/models \ + --output-dir data/03_primary/val \ + --model-conf-thresh 0.05 \ + --max-bbox-size 0.4 +""" + +import argparse +import json +import logging +from pathlib import Path + +from PIL import Image +from pyro_predictor.vision import Classifier +from tqdm import tqdm + +from pyro_detector_baseline.data import get_sorted_frames, list_sequences + +logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") +logger = logging.getLogger(__name__) + + +def _find_onnx_model(model_dir: Path) -> str | None: + """Find the ONNX model file in a directory, skipping macOS resource forks.""" + onnx_files = [f for f in model_dir.glob("**/*.onnx") if not f.name.startswith("._")] + return str(onnx_files[0]) if onnx_files else None + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Run YOLO classifier on sequence frames." + ) + parser.add_argument( + "--data-dir", + type=Path, + required=True, + help="Path to sequence data directory.", + ) + parser.add_argument( + "--model-dir", + type=Path, + required=True, + help="Path to directory containing the ONNX model.", + ) + parser.add_argument( + "--output-dir", + type=Path, + required=True, + help="Output directory for per-sequence detection JSONs.", + ) + parser.add_argument( + "--model-conf-thresh", + type=float, + default=0.05, + help="Per-frame YOLO confidence threshold.", + ) + parser.add_argument( + "--max-bbox-size", + type=float, + default=0.4, + help="Maximum detection width as image fraction.", + ) + args = parser.parse_args() + + args.output_dir.mkdir(parents=True, exist_ok=True) + + model_path = _find_onnx_model(args.model_dir) + logger.info("Using model: %s", model_path) + + classifier = Classifier( + model_path=model_path, + conf=args.model_conf_thresh, + max_bbox_size=args.max_bbox_size, + verbose=False, + ) + + sequences = list_sequences(args.data_dir) + logger.info("Found %d sequences.", len(sequences)) + + total_frames = 0 + for seq_dir in tqdm(sequences, desc="Inferring"): + seq_id = seq_dir.name + frame_paths = get_sorted_frames(seq_dir) + + if not frame_paths: + logger.warning("No frames in %s, skipping.", seq_id) + continue + + frames_data: list[dict] = [] + for frame_path in frame_paths: + pil_img = Image.open(frame_path).convert("RGB") + preds = classifier(pil_img) + frames_data.append( + { + "filename": frame_path.name, + "detections": preds.tolist(), + } + ) + total_frames += 1 + + output_path = args.output_dir / f"{seq_id}.json" + output_path.write_text(json.dumps(frames_data)) + + logger.info( + "Saved detections for %d sequences (%d frames) to %s", + len(sequences), + total_frames, + args.output_dir, + ) + + +if __name__ == "__main__": + main() diff --git a/experiments/temporal-models/pyro-detector-baseline/scripts/package.py b/experiments/temporal-models/pyro-detector-baseline/scripts/package.py new file mode 100644 index 0000000..145b6dc --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/scripts/package.py @@ -0,0 +1,69 @@ +"""Bundle ONNX weights and predictor config into a single model archive. + +Reads the experiment ``params.yaml``, extracts the predict parameters, +and packages them together with the ONNX weights into a ``.zip`` +archive that can be deployed independently. + +Usage: + uv run python scripts/package.py \ + --model-dir data/01_raw/models \ + --params-path params.yaml \ + --output-path data/06_models/model.zip +""" + +import argparse +import logging +from pathlib import Path + +import yaml + +from pyro_detector_baseline.package import build_model_package + +logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") +logger = logging.getLogger(__name__) + + +def _find_onnx_model(model_dir: Path) -> Path: + """Find the ONNX model file in a directory, skipping macOS resource forks.""" + onnx_files = [f for f in model_dir.glob("**/*.onnx") if not f.name.startswith("._")] + if not onnx_files: + raise FileNotFoundError(f"No ONNX model found in {model_dir}") + return onnx_files[0] + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Bundle ONNX weights and predictor config into a model archive." + ) + parser.add_argument( + "--model-dir", + type=Path, + required=True, + help="Path to directory containing the ONNX model.", + ) + parser.add_argument( + "--params-path", + type=Path, + required=True, + help="Path to params.yaml.", + ) + parser.add_argument( + "--output-path", + type=Path, + required=True, + help="Destination path for the model archive (.zip).", + ) + args = parser.parse_args() + + weights_path = _find_onnx_model(args.model_dir) + logger.info("Using weights: %s", weights_path) + + params = yaml.safe_load(args.params_path.read_text()) + output = build_model_package(weights_path, params, args.output_path) + + size_mb = output.stat().st_size / (1024 * 1024) + logger.info("Model archive saved to %s (%.1f MB)", output, size_mb) + + +if __name__ == "__main__": + main() diff --git a/experiments/temporal-models/pyro-detector-baseline/scripts/predict.py b/experiments/temporal-models/pyro-detector-baseline/scripts/predict.py new file mode 100644 index 0000000..e15b755 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/scripts/predict.py @@ -0,0 +1,138 @@ +"""Replay temporal logic on cached YOLO detections for a data split. + +Loads per-frame detections produced by the infer stage and replays them +through the Predictor's sliding-window temporal logic. Writes +sequence-level results to tracking_results.json (same format consumed +by the evaluate stage). + +Usage: + uv run python scripts/predict.py \ + --infer-dir data/03_primary/val \ + --data-dir data/01_raw/datasets/val \ + --output-dir data/07_model_output/val \ + --conf-thresh 0.35 \ + --nb-consecutive-frames 7 +""" + +import argparse +import json +import logging +from pathlib import Path + +from tqdm import tqdm + +from pyro_detector_baseline.data import is_wf_sequence, parse_timestamp +from pyro_detector_baseline.predictor_wrapper import ( + create_replay_predictor, + load_detections, + replay_sequence, +) + +logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") +logger = logging.getLogger(__name__) + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Replay temporal logic on cached detections." + ) + parser.add_argument( + "--infer-dir", + type=Path, + required=True, + help="Path to cached inference results directory.", + ) + parser.add_argument( + "--data-dir", + type=Path, + required=True, + help="Path to sequence data directory (for GT labels).", + ) + parser.add_argument( + "--output-dir", + type=Path, + required=True, + help="Output directory for prediction results.", + ) + parser.add_argument( + "--conf-thresh", + type=float, + default=0.35, + help="Predictor confidence threshold for alerts.", + ) + parser.add_argument( + "--nb-consecutive-frames", + type=int, + default=7, + help="Temporal sliding window size.", + ) + args = parser.parse_args() + + args.output_dir.mkdir(parents=True, exist_ok=True) + + replay = create_replay_predictor(args.conf_thresh, args.nb_consecutive_frames) + + infer_files = sorted(args.infer_dir.glob("*.json")) + logger.info("Found %d inference files.", len(infer_files)) + + results: list[dict] = [] + for infer_path in tqdm(infer_files, desc="Predicting"): + seq_id = infer_path.stem + frame_detections = load_detections(infer_path) + + if not frame_detections: + logger.warning("No frames in %s, skipping.", seq_id) + continue + + # Locate sequence dir for GT (handles nested wildfire/fp layout) + seq_dir = args.data_dir / seq_id + if not seq_dir.is_dir(): + candidates = list(args.data_dir.glob(f"*/{seq_id}")) + if not candidates: + logger.warning("No data dir for %s, skipping.", seq_id) + continue + seq_dir = candidates[0] + + gt = is_wf_sequence(seq_dir) + first_ts = parse_timestamp(frame_detections[0][0]) + + trigger_idx, confidences = replay_sequence(replay, frame_detections, seq_id) + + confirmed_ts = ( + parse_timestamp(frame_detections[trigger_idx][0]) + if trigger_idx is not None + else None + ) + + results.append( + { + "sequence_id": seq_id, + "is_positive_gt": gt, + "is_positive_pred": trigger_idx is not None, + "num_frames": len(frame_detections), + "num_detections_total": sum(1 for c in confidences if c > 0), + "confirmed_frame_index": trigger_idx, + "confirmed_timestamp": ( + confirmed_ts.isoformat() if confirmed_ts else None + ), + "first_timestamp": first_ts.isoformat(), + "per_frame_confidences": confidences, + } + ) + + output_path = args.output_dir / "tracking_results.json" + output_path.write_text(json.dumps(results, indent=2)) + logger.info("Saved %d results to %s", len(results), output_path) + + # Quick summary + tp = sum(1 for r in results if r["is_positive_gt"] and r["is_positive_pred"]) + fp = sum(1 for r in results if not r["is_positive_gt"] and r["is_positive_pred"]) + fn = sum(1 for r in results if r["is_positive_gt"] and not r["is_positive_pred"]) + tn = sum( + 1 for r in results if not r["is_positive_gt"] and not r["is_positive_pred"] + ) + logger.info(" TP=%d FP=%d FN=%d TN=%d", tp, fp, fn, tn) + + +if __name__ == "__main__": + main() diff --git a/experiments/temporal-models/pyro-detector-baseline/scripts/prepare.py b/experiments/temporal-models/pyro-detector-baseline/scripts/prepare.py new file mode 100644 index 0000000..541f24e --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/scripts/prepare.py @@ -0,0 +1,75 @@ +"""Download ONNX model weights from HuggingFace Hub. + +Fetches the ONNX model archive from a HuggingFace repository, extracts +it, and saves it to the local data directory. Skips the download if +the extracted model already exists. + +Usage: + uv run python scripts/prepare.py \ + --model-repo pyronear/yolo11s_mighty-mongoose_v5.1.0 \ + --model-filename onnx_cpu_yolo11s_mighty-mongoose_v5.1.0.tar.gz \ + --output-model-dir data/01_raw/models +""" + +import argparse +import logging +import tarfile +from pathlib import Path + +from huggingface_hub import hf_hub_download + +logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") +logger = logging.getLogger(__name__) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Download ONNX model.") + parser.add_argument( + "--model-repo", + type=str, + required=True, + help="HuggingFace model repo ID.", + ) + parser.add_argument( + "--model-filename", + type=str, + required=True, + help="Model archive filename in the HuggingFace repo.", + ) + parser.add_argument( + "--output-model-dir", + type=Path, + required=True, + help="Output directory for model weights.", + ) + args = parser.parse_args() + + args.output_model_dir.mkdir(parents=True, exist_ok=True) + + # Check if archive already downloaded + archive_path = args.output_model_dir / args.model_filename + if not archive_path.exists(): + logger.info("Downloading %s from %s...", args.model_filename, args.model_repo) + hf_hub_download( + repo_id=args.model_repo, + filename=args.model_filename, + local_dir=args.output_model_dir, + ) + logger.info("Archive saved to %s", archive_path) + else: + logger.info("Archive already exists at %s, skipping download.", archive_path) + + # Extract tar.gz if not already extracted + onnx_files = list(args.output_model_dir.glob("**/*.onnx")) + if not onnx_files: + logger.info("Extracting %s...", archive_path) + with tarfile.open(archive_path, "r:gz") as tar: + tar.extractall(path=args.output_model_dir) + onnx_files = list(args.output_model_dir.glob("**/*.onnx")) + logger.info("Extracted ONNX model(s): %s", [str(f) for f in onnx_files]) + else: + logger.info("ONNX model already extracted: %s", [str(f) for f in onnx_files]) + + +if __name__ == "__main__": + main() diff --git a/experiments/temporal-models/pyro-detector-baseline/scripts/sweep.py b/experiments/temporal-models/pyro-detector-baseline/scripts/sweep.py new file mode 100644 index 0000000..c56da66 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/scripts/sweep.py @@ -0,0 +1,232 @@ +"""Grid-search predictor temporal parameters on cached inference results. + +Loads per-frame YOLO detections from the infer stage, then sweeps over a +grid of (conf_thresh, nb_consecutive_frames) combinations using +multiprocessing. For each combo the Predictor's temporal logic is replayed +and sequence-level metrics are computed. Results are written to a CSV +sorted by F1 descending, and the top-10 configurations are logged. + +Usage: + uv run python scripts/sweep.py \ + --infer-dir data/03_primary/val \ + --data-dir data/01_raw/datasets/val \ + --output-dir data/08_reporting/sweep/val/all + + # Pyronear-only subset: + uv run python scripts/sweep.py \ + --infer-dir data/03_primary/val \ + --data-dir data/01_raw/datasets/val \ + --output-dir data/08_reporting/sweep/val/pyronear \ + --filter-prefix pyronear +""" + +import argparse +import csv +import itertools +import logging +import multiprocessing +import os +from pathlib import Path + +import numpy as np +from tqdm import tqdm + +from pyro_detector_baseline.data import is_wf_sequence, parse_timestamp +from pyro_detector_baseline.evaluator import compute_metrics +from pyro_detector_baseline.predictor_wrapper import ( + create_replay_predictor, + load_detections, + replay_sequence, +) + +logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") +logger = logging.getLogger(__name__) + +DEFAULT_CONF_THRESHOLDS = [0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5] +DEFAULT_NB_CONSECUTIVE_FRAMES = [2, 3, 4, 5, 6, 7, 8, 10] + +# Module-level shared state set via _init_worker +_all_sequences: list[tuple[str, bool, list[tuple[str, np.ndarray]], str]] = [] + + +def _init_worker( + sequences: list[tuple[str, bool, list[tuple[str, np.ndarray]], str]], +) -> None: + global _all_sequences + _all_sequences = sequences + + +def _evaluate_combo(combo: tuple[float, int]) -> dict: + conf_thresh, nb_frames = combo + replay = create_replay_predictor(conf_thresh, nb_frames) + + results: list[dict] = [] + for seq_id, is_positive_gt, frame_detections, first_ts in _all_sequences: + trigger_idx, _confidences = replay_sequence(replay, frame_detections, seq_id) + + confirmed_ts = None + if trigger_idx is not None: + trigger_filename = frame_detections[trigger_idx][0] + confirmed_ts = parse_timestamp(trigger_filename).isoformat() + + results.append( + { + "is_positive_gt": is_positive_gt, + "is_positive_pred": trigger_idx is not None, + "confirmed_timestamp": confirmed_ts, + "first_timestamp": first_ts, + } + ) + + metrics = compute_metrics(results) + return { + "conf_thresh": conf_thresh, + "nb_consecutive_frames": nb_frames, + **metrics, + } + + +def main() -> None: + parser = argparse.ArgumentParser(description="Sweep predictor temporal parameters.") + parser.add_argument( + "--infer-dir", + type=Path, + required=True, + help="Path to cached inference results directory.", + ) + parser.add_argument( + "--data-dir", + type=Path, + required=True, + help="Path to sequence data directory (for GT labels).", + ) + parser.add_argument( + "--output-dir", + type=Path, + required=True, + help="Output directory for sweep results CSV.", + ) + parser.add_argument( + "--workers", + type=int, + default=None, + help="Number of worker processes (default: number of CPUs).", + ) + parser.add_argument( + "--filter-prefix", + type=str, + default=None, + help="Only include sequences whose ID starts with this prefix.", + ) + parser.add_argument( + "--conf-thresholds", + type=float, + nargs="+", + default=DEFAULT_CONF_THRESHOLDS, + help="Confidence thresholds to sweep (default: %(default)s).", + ) + parser.add_argument( + "--nb-frames-values", + type=int, + nargs="+", + default=DEFAULT_NB_CONSECUTIVE_FRAMES, + help="Sliding window sizes to sweep (default: %(default)s).", + ) + args = parser.parse_args() + args.output_dir.mkdir(parents=True, exist_ok=True) + + # Load all cached inference results + infer_files = sorted(args.infer_dir.glob("*.json")) + logger.info("Loading %d inference files...", len(infer_files)) + + all_sequences: list[tuple[str, bool, list[tuple[str, np.ndarray]], str]] = [] + for infer_path in infer_files: + seq_id = infer_path.stem + if args.filter_prefix and not seq_id.startswith(args.filter_prefix): + continue + + seq_dir = args.data_dir / seq_id + # Handle nested layout (wildfire/seq_id or fp/seq_id) + if not seq_dir.is_dir(): + seq_dir_candidates = list(args.data_dir.glob(f"*/{seq_id}")) + if not seq_dir_candidates: + logger.warning("No data dir for %s, skipping.", seq_id) + continue + seq_dir = seq_dir_candidates[0] + + frame_detections = load_detections(infer_path) + if not frame_detections: + continue + + gt = is_wf_sequence(seq_dir) + first_ts = parse_timestamp(frame_detections[0][0]).isoformat() + all_sequences.append((seq_id, gt, frame_detections, first_ts)) + + logger.info("Loaded %d sequences.", len(all_sequences)) + + if not all_sequences: + logger.error("No sequences found — nothing to sweep.") + return + + # Build parameter grid + combos = list(itertools.product(args.conf_thresholds, args.nb_frames_values)) + n_workers = args.workers or os.cpu_count() or 1 + logger.info( + "Running %d parameter combinations with %d workers...", + len(combos), + n_workers, + ) + + with multiprocessing.Pool( + processes=n_workers, + initializer=_init_worker, + initargs=(all_sequences,), + ) as pool: + rows = list( + tqdm( + pool.imap_unordered(_evaluate_combo, combos), + total=len(combos), + desc="Sweep", + ) + ) + + # Sort by F1 descending + rows.sort(key=lambda r: -r["f1"]) + + # Write CSV + csv_path = args.output_dir / "sweep_results.csv" + with csv_path.open("w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=rows[0].keys()) + writer.writeheader() + writer.writerows(rows) + logger.info("Saved %d results to %s", len(rows), csv_path) + + # Print top 10 + logger.info("Top 10 by F1:") + logger.info( + " %-6s %-6s | %-6s %-6s %-6s %-6s | %-8s", + "conf", + "nbF", + "P", + "R", + "F1", + "FPR", + "TTD(s)", + ) + for row in rows[:10]: + ttd = row.get("mean_ttd_seconds") + ttd_str = f"{ttd:.0f}" if ttd is not None else "N/A" + logger.info( + " %-6s %-6s | %-6s %-6s %-6s %-6s | %-8s", + row["conf_thresh"], + row["nb_consecutive_frames"], + row["precision"], + row["recall"], + row["f1"], + row["fpr"], + ttd_str, + ) + + +if __name__ == "__main__": + main() diff --git a/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/__init__.py b/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/data.py b/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/data.py new file mode 100644 index 0000000..b8457f6 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/data.py @@ -0,0 +1,107 @@ +"""File I/O and data utilities for sequence datasets. + +Provides helpers to discover sequences on disk, parse timestamps from +filenames, and determine ground-truth labels from the pyro-dataset label +convention. +""" + +import re +from datetime import datetime +from pathlib import Path + +_CATEGORY_DIRS = {"wildfire", "fp"} + + +def list_sequences(split_dir: Path) -> list[Path]: + """List all sequence directories in a split, sorted by name. + + Supports two layouts: + + * **Nested** (pyro-dataset v2.2.0): ``split_dir/{wildfire,fp}/seq/`` + * **Flat** (legacy): ``split_dir/seq/`` + + In the nested layout the ``wildfire/`` and ``fp/`` category directories + are transparently expanded so callers always receive a flat list of + individual sequence paths. + """ + subdirs = sorted(d for d in split_dir.iterdir() if d.is_dir()) + child_names = {d.name for d in subdirs} + + # Detect nested layout: top-level contains only the category dirs + if child_names <= _CATEGORY_DIRS: + sequences: list[Path] = [] + for cat_dir in subdirs: + sequences.extend(sorted(d for d in cat_dir.iterdir() if d.is_dir())) + return sorted(sequences, key=lambda p: p.name) + + return subdirs + + +def parse_timestamp(filename: str) -> datetime: + """Extract timestamp from an image filename. + + Expected pattern: ..._YYYY-MM-DDTHH-MM-SS.jpg + """ + stem = Path(filename).stem + match = re.search(r"(\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2})$", stem) + if not match: + raise ValueError(f"Cannot parse timestamp from: {filename}") + return datetime.strptime(match.group(1), "%Y-%m-%dT%H-%M-%S") + + +def get_sorted_frames(sequence_dir: Path) -> list[Path]: + """Return image paths from ``sequence_dir/images/`` sorted by timestamp. + + Args: + sequence_dir: Path to a sequence directory containing an ``images/`` + subdirectory with ``.jpg`` files. + + Returns: + Sorted list of image paths, or an empty list if ``images/`` does not + exist. + """ + images_dir = sequence_dir / "images" + if not images_dir.is_dir(): + return [] + images = sorted(images_dir.glob("*.jpg"), key=lambda p: parse_timestamp(p.name)) + return images + + +def is_wf_sequence(sequence_dir: Path) -> bool: + """Determine if a sequence is wildfire (positive). + + Detection strategy (in priority order): + + 1. **Parent directory name** — if the sequence lives under a + ``wildfire/`` or ``fp/`` category directory (pyro-dataset v2.2.0 + nested layout), the parent name is authoritative. + 2. **Label column count** — WF labels have 5 columns + (``class_id cx cy w h``), FP labels have 6 + (``class_id cx cy w h confidence``). + 3. **Fallback** — sequences with only empty labels default to FP. + """ + # Strategy 1: parent directory name + parent_name = sequence_dir.parent.name + if parent_name == "wildfire": + return True + if parent_name == "fp": + return False + + # Strategy 2: label column count + labels_dir = sequence_dir / "labels" + if not labels_dir.is_dir(): + return False + for label_file in labels_dir.iterdir(): + if label_file.suffix != ".txt": + continue + content = label_file.read_text().strip() + if not content: + continue + first_line = content.split("\n")[0].strip() + n_cols = len(first_line.split()) + if n_cols == 5: + return True + if n_cols == 6: + return False + # No non-empty label files — treat as FP + return False diff --git a/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/evaluator.py b/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/evaluator.py new file mode 100644 index 0000000..bacb696 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/evaluator.py @@ -0,0 +1,248 @@ +"""Sequence-level metrics computation and visualization. + +Computes classification metrics (precision, recall, F1, FPR) and +time-to-detection from prediction results, and generates comparison plots +against a single-frame detection baseline. +""" + +import json +import statistics +from datetime import datetime +from pathlib import Path + +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns + + +def load_tracking_results(results_path: Path) -> list[dict]: + """Load prediction results from a JSON file. + + Args: + results_path: Path to ``tracking_results.json``. + + Returns: + List of per-sequence result dicts. + """ + return json.loads(results_path.read_text()) + + +def _extract_ttd_seconds(results: list[dict]) -> list[float]: + """Extract time-to-detection in seconds for true-positive sequences. + + Only considers sequences that are both ground-truth positive and + predicted positive (TP), and where both timestamps are available. + """ + ttd_seconds = [] + for r in results: + if ( + r["is_positive_gt"] + and r["is_positive_pred"] + and r["confirmed_timestamp"] + and r["first_timestamp"] + ): + t_first = datetime.fromisoformat(r["first_timestamp"]) + t_confirmed = datetime.fromisoformat(r["confirmed_timestamp"]) + ttd_seconds.append((t_confirmed - t_first).total_seconds()) + return ttd_seconds + + +def compute_metrics(results: list[dict]) -> dict: + """Compute sequence-level classification metrics. + + Args: + results: List of per-sequence result dicts, each containing at least + ``is_positive_gt``, ``is_positive_pred``, ``confirmed_timestamp``, + and ``first_timestamp``. + + Returns: + Dict with keys: ``num_sequences``, ``num_positive_gt``, + ``num_negative_gt``, ``tp``, ``fp``, ``fn``, ``tn``, ``precision``, + ``recall``, ``f1``, ``fpr``, ``mean_ttd_seconds``, + ``median_ttd_seconds``. TTD values are ``None`` when there are no + true positives. + """ + tp = sum(1 for r in results if r["is_positive_gt"] and r["is_positive_pred"]) + fp = sum(1 for r in results if not r["is_positive_gt"] and r["is_positive_pred"]) + fn = sum(1 for r in results if r["is_positive_gt"] and not r["is_positive_pred"]) + tn = sum( + 1 for r in results if not r["is_positive_gt"] and not r["is_positive_pred"] + ) + + precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0 + recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0 + f1 = ( + 2 * precision * recall / (precision + recall) + if (precision + recall) > 0 + else 0.0 + ) + + n_positive_gt = tp + fn + n_negative_gt = fp + tn + fpr = fp / n_negative_gt if n_negative_gt > 0 else 0.0 + + ttd_seconds = _extract_ttd_seconds(results) + mean_ttd = sum(ttd_seconds) / len(ttd_seconds) if ttd_seconds else None + median_ttd = statistics.median(ttd_seconds) if ttd_seconds else None + + def row_pct(v: int, row_total: int) -> float: + return round(v / row_total * 100, 2) if row_total > 0 else 0.0 + + return { + "num_sequences": len(results), + "num_positive_gt": n_positive_gt, + "num_negative_gt": n_negative_gt, + "tp": tp, + "fp": fp, + "fn": fn, + "tn": tn, + "tp_pct": row_pct(tp, n_positive_gt), + "fp_pct": row_pct(fp, n_negative_gt), + "fn_pct": row_pct(fn, n_positive_gt), + "tn_pct": row_pct(tn, n_negative_gt), + "precision": round(precision, 4), + "recall": round(recall, 4), + "f1": round(f1, 4), + "fpr": round(fpr, 4), + "mean_ttd_seconds": (round(mean_ttd, 1) if mean_ttd is not None else None), + "median_ttd_seconds": ( + round(median_ttd, 1) if median_ttd is not None else None + ), + } + + +def compute_single_frame_baseline(results: list[dict]) -> dict: + """Compute baseline metrics where any detection triggers an alarm. + + Simulates a naive strategy with no temporal filtering: a sequence is + predicted positive if it contains at least one frame where the + predictor returned a nonzero confidence. + + Args: + results: List of per-sequence result dicts (same format as + :func:`compute_metrics`). + + Returns: + Metrics dict (same keys as :func:`compute_metrics`). + """ + baseline_results = [] + for r in results: + baseline_results.append( + { + "is_positive_gt": r["is_positive_gt"], + "is_positive_pred": r["num_detections_total"] > 0, + "confirmed_timestamp": r["first_timestamp"], + "first_timestamp": r["first_timestamp"], + } + ) + return compute_metrics(baseline_results) + + +def plot_confusion_matrix(metrics: dict, output_path: Path) -> None: + """Plot a confusion matrix heatmap and save it as PNG.""" + sns.set_theme(style="whitegrid") + cm = [[metrics["tp"], metrics["fn"]], [metrics["fp"], metrics["tn"]]] + fig, ax = plt.subplots(figsize=(6, 5)) + sns.heatmap( + cm, + annot=True, + fmt="d", + cmap="Blues", + xticklabels=["Predicted +", "Predicted -"], + yticklabels=["Actual +", "Actual -"], + ax=ax, + ) + ax.set_title("Confusion Matrix") + fig.tight_layout() + fig.savefig(output_path, dpi=100) + plt.close(fig) + + +def plot_confusion_matrix_percentages(metrics: dict, output_path: Path) -> None: + """Plot a confusion matrix heatmap with percentages and save it as PNG.""" + sns.set_theme(style="whitegrid") + cm = [[metrics["tp"], metrics["fn"]], [metrics["fp"], metrics["tn"]]] + row_totals = [sum(row) for row in cm] + cm_pct = [ + [v / rt * 100 if rt > 0 else 0 for v in row] + for row, rt in zip(cm, row_totals, strict=True) + ] + labels = [ + [f"{v / rt * 100:.1f}%" if rt > 0 else "0.0%" for v in row] + for row, rt in zip(cm, row_totals, strict=True) + ] + fig, ax = plt.subplots(figsize=(6, 5)) + sns.heatmap( + cm_pct, + annot=labels, + fmt="", + cmap="Blues", + xticklabels=["Predicted +", "Predicted -"], + yticklabels=["Actual +", "Actual -"], + ax=ax, + ) + ax.set_title("Confusion Matrix (%)") + fig.tight_layout() + fig.savefig(output_path, dpi=100) + plt.close(fig) + + +def plot_comparison( + baseline_metrics: dict, predictor_metrics: dict, output_path: Path +) -> None: + """Bar chart comparing single-frame vs predictor on precision, recall, F1.""" + sns.set_theme(style="whitegrid") + data = pd.DataFrame( + { + "Metric": ["Precision", "Recall", "F1"] * 2, + "Score": [ + baseline_metrics["precision"], + baseline_metrics["recall"], + baseline_metrics["f1"], + predictor_metrics["precision"], + predictor_metrics["recall"], + predictor_metrics["f1"], + ], + "Method": ["Single-frame"] * 3 + ["Predictor (temporal)"] * 3, + } + ) + fig, ax = plt.subplots(figsize=(8, 5)) + sns.barplot(data=data, x="Metric", y="Score", hue="Method", ax=ax) + ax.set_ylim(0, 1.05) + ax.set_title("Single-frame vs Predictor (temporal)") + + for container in ax.containers: + ax.bar_label(container, fmt="%.3f", fontsize=9, padding=3) + + fig.tight_layout() + fig.savefig(output_path, dpi=100) + plt.close(fig) + + +def plot_ttd_histogram(results: list[dict], output_path: Path) -> None: + """Histogram of time-to-detection for true-positive wildfire sequences. + + Skips plotting entirely if there are no true positives. + """ + sns.set_theme(style="whitegrid") + ttd_seconds = _extract_ttd_seconds(results) + + if not ttd_seconds: + return + + fig, ax = plt.subplots(figsize=(8, 5)) + sns.histplot(ttd_seconds, bins=20, kde=True, ax=ax) + mean_val = sum(ttd_seconds) / len(ttd_seconds) + ax.axvline( + mean_val, + color="red", + linestyle="--", + label=f"Mean: {mean_val:.0f}s", + ) + ax.set_xlabel("Time to Detection (seconds)") + ax.set_ylabel("Count") + ax.set_title("Time to Detection Distribution (True Positives)") + ax.legend() + fig.tight_layout() + fig.savefig(output_path, dpi=100) + plt.close(fig) diff --git a/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/model.py b/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/model.py new file mode 100644 index 0000000..aade62c --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/model.py @@ -0,0 +1,110 @@ +"""TemporalModel implementation backed by pyro-predictor.""" + +from pathlib import Path +from typing import Self + +from PIL import Image +from pyrocore import Frame, TemporalModel, TemporalModelOutput + +from .package import load_model_package +from .predictor_wrapper import create_predictor + + +class PyroDetectorModel(TemporalModel): + """Production smoke detection baseline using pyro-predictor. + + Wraps the pyro-engine Predictor (YOLO ONNX + sliding-window temporal + smoothing) as a :class:`TemporalModel` subclass for comparison with + other temporal models. + + The Predictor maintains per-camera sliding-window state. Each call + to :meth:`predict` uses a unique camera ID so sequences are isolated. + The alarm threshold is the Predictor's own ``conf_thresh``, matching + production behavior. + """ + + def __init__( + self, + model_path: str | None = None, + conf_thresh: float = 0.35, + model_conf_thresh: float = 0.05, + nb_consecutive_frames: int = 7, + max_bbox_size: float = 0.4, + frame_size: tuple[int, int] | None = None, + ) -> None: + self._predictor = create_predictor( + model_path=model_path, + conf_thresh=conf_thresh, + model_conf_thresh=model_conf_thresh, + nb_consecutive_frames=nb_consecutive_frames, + max_bbox_size=max_bbox_size, + frame_size=frame_size, + ) + self._sequence_counter = 0 + + @classmethod + def from_package(cls, package_path: Path) -> Self: + """Load from a packaged model archive. + + Args: + package_path: Path to a ``.zip`` archive created by + :func:`~pyro_detector_baseline.package.build_model_package`. + """ + pkg = load_model_package(package_path) + return cls( + model_path=str(pkg.model_path), + **pkg.predict_params, + ) + + @classmethod + def from_model_dir(cls, model_dir: Path, **kwargs) -> Self: + """Load from a directory containing the extracted ONNX model. + + Args: + model_dir: Directory containing the ONNX model files. + **kwargs: Additional arguments forwarded to the constructor. + """ + onnx_files = [ + f for f in model_dir.glob("**/*.onnx") if not f.name.startswith("._") + ] + model_path = str(onnx_files[0]) if onnx_files else None + return cls(model_path=model_path, **kwargs) + + def predict(self, frames: list[Frame]) -> TemporalModelOutput: + """Run pyro-predictor on a loaded sequence. + + Args: + frames: Temporally ordered :class:`Frame` objects. + + Returns: + :class:`TemporalModelOutput` with classification decision and + per-frame confidences in ``details``. + """ + cam_id = f"seq_{self._sequence_counter}" + self._sequence_counter += 1 + + threshold = self._predictor.conf_thresh + confidences: list[float] = [] + trigger_frame_index: int | None = None + + for i, frame in enumerate(frames): + pil_img = Image.open(frame.image_path) + confidence = self._predictor.predict(pil_img, cam_id=cam_id) + confidences.append(float(confidence)) + + if confidence > threshold and trigger_frame_index is None: + trigger_frame_index = i + + is_positive = trigger_frame_index is not None + + return TemporalModelOutput( + is_positive=is_positive, + trigger_frame_index=trigger_frame_index, + details={ + "per_frame_confidences": confidences, + "conf_thresh": threshold, + "num_frames": len(frames), + "num_detections_total": sum(1 for c in confidences if c > 0), + "max_confidence": max(confidences) if confidences else 0.0, + }, + ) diff --git a/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/package.py b/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/package.py new file mode 100644 index 0000000..d65e6ce --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/package.py @@ -0,0 +1,117 @@ +"""Model packaging: bundle ONNX weights and predictor config into an archive. + +The archive is a standard ``.zip`` file containing: + +- ``manifest.yaml`` -- Entry point with format version and file pointers. +- ``best.onnx`` -- ONNX model weights. +- ``config.yaml`` -- Predictor parameters. + +This allows shipping the complete pyro-predictor pipeline as one +self-contained artefact. +""" + +import zipfile +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import yaml + +FORMAT_VERSION = 1 +MANIFEST_FILENAME = "manifest.yaml" +WEIGHTS_FILENAME = "best.onnx" +CONFIG_FILENAME = "config.yaml" +DEFAULT_EXTRACT_DIR = Path(".cache/model") + + +@dataclass +class ModelPackage: + """A loaded model package with ONNX model path and predictor config.""" + + model_path: Path + config: dict[str, Any] + + @property + def predict_params(self) -> dict[str, Any]: + """Predictor constructor parameters.""" + return self.config["predict"] + + +def build_model_package( + weights_path: Path, + params: dict[str, Any], + output_path: Path, +) -> Path: + """Bundle ONNX weights and predictor config into a ``.zip`` archive. + + Args: + weights_path: Path to the ONNX weights file. + params: Full ``params.yaml`` dictionary (must contain ``predict``). + output_path: Destination path for the archive. + + Returns: + The resolved *output_path*. + """ + if not weights_path.exists(): + raise FileNotFoundError(f"Model weights not found: {weights_path}") + + config = {"predict": params["predict"]} + manifest = { + "format_version": FORMAT_VERSION, + "weights": WEIGHTS_FILENAME, + "config": CONFIG_FILENAME, + } + + output_path.parent.mkdir(parents=True, exist_ok=True) + + with zipfile.ZipFile(output_path, "w", compression=zipfile.ZIP_STORED) as zf: + zf.writestr(MANIFEST_FILENAME, yaml.dump(manifest, default_flow_style=False)) + zf.write(weights_path, WEIGHTS_FILENAME) + zf.writestr(CONFIG_FILENAME, yaml.dump(config, default_flow_style=False)) + + return output_path.resolve() + + +def load_model_package( + package_path: Path, + extract_dir: Path = DEFAULT_EXTRACT_DIR, +) -> ModelPackage: + """Load a packaged model archive. + + Args: + package_path: Path to a ``.zip`` archive created by + :func:`build_model_package`. + extract_dir: Directory to extract the weights into. + + Returns: + A :class:`ModelPackage` instance. + """ + if not package_path.exists(): + raise FileNotFoundError(f"Archive not found: {package_path}") + + with zipfile.ZipFile(package_path, "r") as zf: + names = zf.namelist() + if MANIFEST_FILENAME not in names: + raise KeyError(f"Archive missing {MANIFEST_FILENAME}") + + manifest = yaml.safe_load(zf.read(MANIFEST_FILENAME)) + + version = manifest.get("format_version") + if version != FORMAT_VERSION: + raise ValueError( + f"Unsupported format_version {version} (expected {FORMAT_VERSION})" + ) + + weights_name = manifest["weights"] + config_name = manifest["config"] + + if weights_name not in names: + raise KeyError(f"Archive missing {weights_name}") + if config_name not in names: + raise KeyError(f"Archive missing {config_name}") + + extract_dir.mkdir(parents=True, exist_ok=True) + zf.extract(weights_name, extract_dir) + config = yaml.safe_load(zf.read(config_name)) + + return ModelPackage(model_path=extract_dir / weights_name, config=config) diff --git a/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/predictor_wrapper.py b/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/predictor_wrapper.py new file mode 100644 index 0000000..3537ee0 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/src/pyro_detector_baseline/predictor_wrapper.py @@ -0,0 +1,152 @@ +"""Wrapper around pyro-predictor's Predictor for sequence-level evaluation. + +Provides factory and helper functions to run the production Predictor on +a sequence of frames and collect per-frame confidences. + +The ``create_predictor`` factory creates a full Predictor (with YOLO model) +for live inference. The ``create_replay_predictor``, ``load_detections``, +and ``replay_sequence`` helpers support offline replay of cached YOLO +detections through the Predictor's temporal logic without loading the model. +""" + +import json +from pathlib import Path + +import numpy as np +import pyro_predictor +from PIL import Image +from pyro_predictor.predictor import Predictor + +_DUMMY_FRAME = Image.new("RGB", (1, 1)) + + +def create_predictor( + model_path: str | None = None, + conf_thresh: float = 0.35, + model_conf_thresh: float = 0.05, + nb_consecutive_frames: int = 7, + max_bbox_size: float = 0.4, + frame_size: tuple[int, int] | None = None, +): + """Create a pyro-predictor Predictor instance. + + Args: + model_path: Path to an ONNX model file, or ``None`` to use the + default model downloaded from HuggingFace Hub. + conf_thresh: Confidence threshold for active alerts. + model_conf_thresh: Per-frame YOLO confidence threshold. + nb_consecutive_frames: Temporal sliding window size. + max_bbox_size: Maximum detection width as image fraction. + frame_size: Optional ``(width, height)`` to resize frames. + + Returns: + A ``pyro_predictor.Predictor`` instance. + """ + return pyro_predictor.Predictor( + model_path=model_path, + conf_thresh=conf_thresh, + model_conf_thresh=model_conf_thresh, + nb_consecutive_frames=nb_consecutive_frames, + max_bbox_size=max_bbox_size, + frame_size=frame_size, + verbose=False, + ) + + +def create_replay_predictor( + conf_thresh: float, + nb_consecutive_frames: int, +) -> Predictor: + """Create a lightweight Predictor for temporal replay without loading YOLO. + + Uses ``object.__new__`` to skip ``__init__`` (and ONNX model loading), + setting only the attributes that ``_update_states`` and ``_new_state`` + require: ``conf_thresh``, ``nb_consecutive_frames``, and ``_states``. + """ + replay = object.__new__(Predictor) + replay.conf_thresh = conf_thresh + replay.nb_consecutive_frames = nb_consecutive_frames + replay._states = {} + return replay + + +def load_detections(json_path: Path) -> list[tuple[str, np.ndarray]]: + """Load cached per-frame detections from an infer JSON. + + Args: + json_path: Path to a sequence JSON produced by the infer stage. + + Returns: + List of ``(filename, detections)`` tuples where *detections* is + a ``(N, 5)`` numpy array of ``[x1, y1, x2, y2, conf]``. + """ + data = json.loads(json_path.read_text()) + return [ + (d["filename"], np.array(d["detections"], dtype=np.float64).reshape(-1, 5)) + for d in data + ] + + +def replay_sequence( + predictor: Predictor, + frame_detections: list[tuple[str, np.ndarray]], + cam_key: str, +) -> tuple[int | None, list[float]]: + """Replay cached detections through the Predictor's temporal logic. + + Args: + predictor: A replay Predictor created via ``create_replay_predictor``. + frame_detections: Per-frame detections from ``load_detections``. + cam_key: Unique identifier for this sequence (used as camera ID). + + Returns: + A tuple of ``(trigger_frame_index, per_frame_confidences)``. + ``trigger_frame_index`` is ``None`` if no alarm was triggered. + """ + predictor._states[cam_key] = predictor._new_state() + + confidences: list[float] = [] + trigger_idx: int | None = None + for i, (_filename, preds) in enumerate(frame_detections): + conf = predictor._update_states(_DUMMY_FRAME, preds, cam_key) + confidences.append(float(conf)) + if conf > predictor.conf_thresh and trigger_idx is None: + trigger_idx = i + + del predictor._states[cam_key] + return trigger_idx, confidences + + +def predict_sequence( + predictor, + frame_paths: list[Path], + cam_id: str, +) -> tuple[bool, int | None, list[float]]: + """Run the predictor on all frames in a sequence. + + Uses a unique *cam_id* per sequence so the Predictor's internal + sliding-window state is isolated. The alarm threshold is taken from + the predictor's own ``conf_thresh`` to match production behavior. + + Args: + predictor: A ``pyro_predictor.Predictor`` instance. + frame_paths: Temporally ordered image paths. + cam_id: Unique identifier for this sequence (used as camera ID). + + Returns: + A tuple of ``(is_alarm, trigger_frame_index, per_frame_confidences)``. + """ + threshold = predictor.conf_thresh + confidences: list[float] = [] + trigger_frame_index: int | None = None + + for i, frame_path in enumerate(frame_paths): + pil_img = Image.open(frame_path) + confidence = predictor.predict(pil_img, cam_id=cam_id) + confidences.append(float(confidence)) + + if confidence > threshold and trigger_frame_index is None: + trigger_frame_index = i + + is_alarm = trigger_frame_index is not None + return is_alarm, trigger_frame_index, confidences diff --git a/experiments/temporal-models/pyro-detector-baseline/tests/__init__.py b/experiments/temporal-models/pyro-detector-baseline/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/experiments/temporal-models/pyro-detector-baseline/tests/test_data.py b/experiments/temporal-models/pyro-detector-baseline/tests/test_data.py new file mode 100644 index 0000000..89d2759 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/tests/test_data.py @@ -0,0 +1,130 @@ +"""Tests for data utilities.""" + +from datetime import datetime + +import pytest + +from pyro_detector_baseline.data import ( + get_sorted_frames, + is_wf_sequence, + list_sequences, + parse_timestamp, +) + +# --- parse_timestamp --- + + +class TestParseTimestamp: + def test_standard_filename(self): + ts = parse_timestamp("adf_site_999_2023-05-23T17-18-31.jpg") + assert ts == datetime(2023, 5, 23, 17, 18, 31) + + def test_stem_only(self): + ts = parse_timestamp("adf_site_999_2023-05-23T17-18-31") + assert ts == datetime(2023, 5, 23, 17, 18, 31) + + def test_invalid_raises(self): + with pytest.raises(ValueError, match="Cannot parse timestamp"): + parse_timestamp("no_timestamp_here.jpg") + + +# --- is_wf_sequence --- + + +class TestIsWfSequence: + def test_five_column_is_wf(self, tmp_path): + labels = tmp_path / "labels" + labels.mkdir() + (labels / "frame.txt").write_text("0 0.5 0.5 0.1 0.1\n") + assert is_wf_sequence(tmp_path) is True + + def test_six_column_is_fp(self, tmp_path): + labels = tmp_path / "labels" + labels.mkdir() + (labels / "frame.txt").write_text("0 0.5 0.5 0.1 0.1 0.9\n") + assert is_wf_sequence(tmp_path) is False + + def test_empty_labels_default_fp(self, tmp_path): + labels = tmp_path / "labels" + labels.mkdir() + (labels / "frame.txt").write_text("") + assert is_wf_sequence(tmp_path) is False + + def test_no_labels_dir(self, tmp_path): + assert is_wf_sequence(tmp_path) is False + + def test_non_txt_files_ignored(self, tmp_path): + labels = tmp_path / "labels" + labels.mkdir() + (labels / "frame.csv").write_text("0 0.5 0.5 0.1 0.1\n") + assert is_wf_sequence(tmp_path) is False + + def test_parent_wildfire_is_positive(self, tmp_path): + wf_dir = tmp_path / "wildfire" / "seq_a" + wf_dir.mkdir(parents=True) + assert is_wf_sequence(wf_dir) is True + + def test_parent_fp_is_negative(self, tmp_path): + fp_dir = tmp_path / "fp" / "seq_a" + fp_dir.mkdir(parents=True) + assert is_wf_sequence(fp_dir) is False + + +# --- list_sequences --- + + +class TestListSequences: + def test_lists_directories_sorted(self, tmp_path): + (tmp_path / "seq_b").mkdir() + (tmp_path / "seq_a").mkdir() + (tmp_path / "file.txt").write_text("not a dir") + result = list_sequences(tmp_path) + assert result == [tmp_path / "seq_a", tmp_path / "seq_b"] + + def test_empty_directory(self, tmp_path): + assert list_sequences(tmp_path) == [] + + def test_nested_layout(self, tmp_path): + """Nested pyro-dataset layout: split/{wildfire,fp}/seq/.""" + wf = tmp_path / "wildfire" + fp = tmp_path / "fp" + wf.mkdir() + fp.mkdir() + (wf / "seq_wf_a").mkdir() + (wf / "seq_wf_b").mkdir() + (fp / "seq_fp_c").mkdir() + result = list_sequences(tmp_path) + names = [p.name for p in result] + assert names == ["seq_fp_c", "seq_wf_a", "seq_wf_b"] + + def test_nested_layout_wildfire_only(self, tmp_path): + wf = tmp_path / "wildfire" + wf.mkdir() + (wf / "seq_a").mkdir() + result = list_sequences(tmp_path) + assert result == [wf / "seq_a"] + + +# --- get_sorted_frames --- + + +class TestGetSortedFrames: + def test_returns_sorted_by_timestamp(self, tmp_path): + images = tmp_path / "images" + images.mkdir() + (images / "cam_2023-01-01T00-00-30.jpg").write_text("") + (images / "cam_2023-01-01T00-00-00.jpg").write_text("") + (images / "cam_2023-01-01T00-01-00.jpg").write_text("") + result = get_sorted_frames(tmp_path) + assert [p.name for p in result] == [ + "cam_2023-01-01T00-00-00.jpg", + "cam_2023-01-01T00-00-30.jpg", + "cam_2023-01-01T00-01-00.jpg", + ] + + def test_no_images_dir(self, tmp_path): + assert get_sorted_frames(tmp_path) == [] + + def test_empty_images_dir(self, tmp_path): + (tmp_path / "images").mkdir() + assert get_sorted_frames(tmp_path) == [] diff --git a/experiments/temporal-models/pyro-detector-baseline/tests/test_evaluator.py b/experiments/temporal-models/pyro-detector-baseline/tests/test_evaluator.py new file mode 100644 index 0000000..6063e7e --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/tests/test_evaluator.py @@ -0,0 +1,121 @@ +"""Tests for evaluator module.""" + +from pyro_detector_baseline.evaluator import ( + compute_metrics, + compute_single_frame_baseline, +) + + +def _result(gt, pred, n_det=0, first_ts=None, conf_ts=None): + """Build a minimal sequence result dict.""" + return { + "is_positive_gt": gt, + "is_positive_pred": pred, + "num_detections_total": n_det, + "confirmed_timestamp": conf_ts, + "first_timestamp": first_ts, + } + + +class TestComputeMetrics: + def test_perfect_predictions(self): + results = [ + _result(True, True), + _result(False, False), + ] + m = compute_metrics(results) + assert m["tp"] == 1 + assert m["tn"] == 1 + assert m["fp"] == 0 + assert m["fn"] == 0 + assert m["precision"] == 1.0 + assert m["recall"] == 1.0 + assert m["f1"] == 1.0 + assert m["fpr"] == 0.0 + + def test_all_false_positives(self): + results = [ + _result(False, True), + _result(False, True), + ] + m = compute_metrics(results) + assert m["fp"] == 2 + assert m["precision"] == 0.0 + assert m["fpr"] == 1.0 + + def test_all_false_negatives(self): + results = [ + _result(True, False), + _result(True, False), + ] + m = compute_metrics(results) + assert m["fn"] == 2 + assert m["recall"] == 0.0 + + def test_mixed(self): + results = [ + _result(True, True), + _result(True, False), + _result(False, True), + _result(False, False), + ] + m = compute_metrics(results) + assert m["tp"] == 1 + assert m["fp"] == 1 + assert m["fn"] == 1 + assert m["tn"] == 1 + assert m["precision"] == 0.5 + assert m["recall"] == 0.5 + + def test_empty(self): + m = compute_metrics([]) + assert m["num_sequences"] == 0 + assert m["precision"] == 0.0 + + def test_ttd_computed(self): + results = [ + _result( + True, + True, + first_ts="2024-01-01T12:00:00", + conf_ts="2024-01-01T12:01:00", + ), + _result( + True, + True, + first_ts="2024-01-01T12:00:00", + conf_ts="2024-01-01T12:02:00", + ), + ] + m = compute_metrics(results) + assert m["mean_ttd_seconds"] == 90.0 + assert m["median_ttd_seconds"] == 90.0 + + def test_ttd_none_when_no_tp(self): + results = [_result(True, False)] + m = compute_metrics(results) + assert m["mean_ttd_seconds"] is None + assert m["median_ttd_seconds"] is None + + +class TestComputeSingleFrameBaseline: + def test_any_detection_triggers_alarm(self): + results = [ + _result(True, False, n_det=3, first_ts="2024-01-01T12:00:00"), + _result(False, False, n_det=1, first_ts="2024-01-01T12:00:00"), + ] + m = compute_single_frame_baseline(results) + # Both have detections, so both predicted positive + assert m["tp"] == 1 + assert m["fp"] == 1 + + def test_no_detections_all_negative(self): + results = [ + _result(True, False, n_det=0, first_ts="2024-01-01T12:00:00"), + _result(False, False, n_det=0, first_ts="2024-01-01T12:00:00"), + ] + m = compute_single_frame_baseline(results) + assert m["fn"] == 1 + assert m["tn"] == 1 + assert m["tp"] == 0 + assert m["fp"] == 0 diff --git a/experiments/temporal-models/pyro-detector-baseline/tests/test_model.py b/experiments/temporal-models/pyro-detector-baseline/tests/test_model.py new file mode 100644 index 0000000..630d18e --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/tests/test_model.py @@ -0,0 +1,125 @@ +"""Tests for PyroDetectorModel.""" + +import json +from unittest.mock import MagicMock, patch + +from PIL import Image as PILImage +from pyrocore import Frame, TemporalModel + +from pyro_detector_baseline.model import PyroDetectorModel + + +def _make_frames(tmp_path, n=5): + """Create Frame objects with valid 1x1 JPEG image files.""" + frames = [] + for i in range(n): + img = tmp_path / f"cam_2023-01-01T00-00-{i:02d}.jpg" + PILImage.new("RGB", (1, 1)).save(img) + frames.append( + Frame( + frame_id=img.stem, + image_path=img, + timestamp=None, + ) + ) + return frames + + +@patch("pyro_detector_baseline.predictor_wrapper.pyro_predictor") +class TestPyroDetectorModel: + def _make_model(self, mock_pyro, predict_values=None, conf_thresh=0.35): + """Create a PyroDetectorModel with mocked Predictor.""" + mock_predictor = MagicMock() + mock_predictor.conf_thresh = conf_thresh + if predict_values is not None: + mock_predictor.predict.side_effect = list(predict_values) + else: + mock_predictor.predict.return_value = 0.0 + mock_pyro.Predictor.return_value = mock_predictor + + model = PyroDetectorModel(conf_thresh=conf_thresh) + return model + + def test_is_temporal_model_subclass(self, mock_pyro): + model = self._make_model(mock_pyro) + assert isinstance(model, TemporalModel) + + def test_predict_no_detections(self, mock_pyro, tmp_path): + model = self._make_model(mock_pyro) + frames = _make_frames(tmp_path, n=5) + + output = model.predict(frames) + + assert output.is_positive is False + assert output.trigger_frame_index is None + assert output.details["max_confidence"] == 0.0 + + def test_predict_above_threshold(self, mock_pyro, tmp_path): + model = self._make_model(mock_pyro, predict_values=[0.0, 0.0, 0.6, 0.7, 0.8]) + frames = _make_frames(tmp_path, n=5) + + output = model.predict(frames) + + assert output.is_positive is True + assert output.trigger_frame_index == 2 + + def test_output_details_keys(self, mock_pyro, tmp_path): + model = self._make_model(mock_pyro) + frames = _make_frames(tmp_path, n=3) + + output = model.predict(frames) + + expected_keys = { + "per_frame_confidences", + "conf_thresh", + "num_frames", + "num_detections_total", + "max_confidence", + } + assert set(output.details.keys()) == expected_keys + + def test_details_json_serializable(self, mock_pyro, tmp_path): + model = self._make_model(mock_pyro, predict_values=[0.1, 0.2, 0.3]) + frames = _make_frames(tmp_path, n=3) + + output = model.predict(frames) + + # Should not raise + json.dumps(output.details) + + def test_unique_cam_ids_across_calls(self, mock_pyro, tmp_path): + model = self._make_model(mock_pyro) + frames = _make_frames(tmp_path, n=2) + + # Call predict twice + model.predict(frames) + model.predict(frames) + + # Extract cam_ids from all predict calls + mock_predictor = mock_pyro.Predictor.return_value + cam_ids = set() + for call in mock_predictor.predict.call_args_list: + cam_id = call.kwargs.get("cam_id") or call[1].get("cam_id") + cam_ids.add(cam_id) + + # Should have 2 different cam_ids + assert len(cam_ids) == 2 + + def test_num_detections_total(self, mock_pyro, tmp_path): + model = self._make_model(mock_pyro, predict_values=[0.0, 0.1, 0.0, 0.2, 0.0]) + frames = _make_frames(tmp_path, n=5) + + output = model.predict(frames) + + # 2 frames had confidence > 0 + assert output.details["num_detections_total"] == 2 + + def test_empty_frames(self, mock_pyro, tmp_path): + model = self._make_model(mock_pyro) + + output = model.predict([]) + + assert output.is_positive is False + assert output.trigger_frame_index is None + assert output.details["num_frames"] == 0 + assert output.details["max_confidence"] == 0.0 diff --git a/experiments/temporal-models/pyro-detector-baseline/tests/test_package.py b/experiments/temporal-models/pyro-detector-baseline/tests/test_package.py new file mode 100644 index 0000000..3312b95 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/tests/test_package.py @@ -0,0 +1,115 @@ +"""Tests for model packaging.""" + +import zipfile + +import pytest +import yaml + +from pyro_detector_baseline.package import ( + CONFIG_FILENAME, + FORMAT_VERSION, + MANIFEST_FILENAME, + WEIGHTS_FILENAME, + build_model_package, + load_model_package, +) + + +@pytest.fixture() +def dummy_weights(tmp_path): + weights = tmp_path / "best.onnx" + weights.write_bytes(b"fake-onnx-weights") + return weights + + +@pytest.fixture() +def dummy_params(): + return { + "predict": { + "conf_thresh": 0.35, + "model_conf_thresh": 0.05, + "nb_consecutive_frames": 7, + "max_bbox_size": 0.4, + } + } + + +class TestBuildModelPackage: + def test_creates_valid_zip(self, tmp_path, dummy_weights, dummy_params): + out = tmp_path / "model.zip" + build_model_package(dummy_weights, dummy_params, out) + assert out.exists() + assert zipfile.is_zipfile(out) + + def test_expected_entries(self, tmp_path, dummy_weights, dummy_params): + out = tmp_path / "model.zip" + build_model_package(dummy_weights, dummy_params, out) + with zipfile.ZipFile(out) as zf: + names = zf.namelist() + assert MANIFEST_FILENAME in names + assert WEIGHTS_FILENAME in names + assert CONFIG_FILENAME in names + + def test_manifest_content(self, tmp_path, dummy_weights, dummy_params): + out = tmp_path / "model.zip" + build_model_package(dummy_weights, dummy_params, out) + with zipfile.ZipFile(out) as zf: + manifest = yaml.safe_load(zf.read(MANIFEST_FILENAME)) + assert manifest["format_version"] == FORMAT_VERSION + assert manifest["weights"] == WEIGHTS_FILENAME + assert manifest["config"] == CONFIG_FILENAME + + def test_config_content(self, tmp_path, dummy_weights, dummy_params): + out = tmp_path / "model.zip" + build_model_package(dummy_weights, dummy_params, out) + with zipfile.ZipFile(out) as zf: + config = yaml.safe_load(zf.read(CONFIG_FILENAME)) + assert config["predict"]["conf_thresh"] == 0.35 + assert config["predict"]["nb_consecutive_frames"] == 7 + + def test_weights_preserved(self, tmp_path, dummy_weights, dummy_params): + out = tmp_path / "model.zip" + build_model_package(dummy_weights, dummy_params, out) + with zipfile.ZipFile(out) as zf: + assert zf.read(WEIGHTS_FILENAME) == b"fake-onnx-weights" + + def test_missing_weights_raises(self, tmp_path, dummy_params): + missing = tmp_path / "missing.onnx" + out = tmp_path / "model.zip" + with pytest.raises(FileNotFoundError): + build_model_package(missing, dummy_params, out) + + +class TestLoadModelPackage: + def _build(self, tmp_path, dummy_weights, dummy_params): + out = tmp_path / "model.zip" + build_model_package(dummy_weights, dummy_params, out) + return out + + def test_roundtrip(self, tmp_path, dummy_weights, dummy_params): + archive = self._build(tmp_path, dummy_weights, dummy_params) + extract = tmp_path / "extract" + pkg = load_model_package(archive, extract_dir=extract) + assert pkg.model_path.exists() + assert pkg.predict_params["conf_thresh"] == 0.35 + + def test_missing_archive_raises(self, tmp_path): + with pytest.raises(FileNotFoundError): + load_model_package(tmp_path / "missing.zip") + + def test_missing_manifest_raises(self, tmp_path): + archive = tmp_path / "bad.zip" + with zipfile.ZipFile(archive, "w") as zf: + zf.writestr("something.txt", "data") + with pytest.raises(KeyError, match="manifest"): + load_model_package(archive) + + def test_unsupported_version_raises(self, tmp_path, dummy_weights): + archive = tmp_path / "bad.zip" + manifest = {"format_version": 999, "weights": "w", "config": "c"} + with zipfile.ZipFile(archive, "w") as zf: + zf.writestr(MANIFEST_FILENAME, yaml.dump(manifest)) + zf.writestr("w", b"data") + zf.writestr("c", yaml.dump({"predict": {}})) + with pytest.raises(ValueError, match="format_version"): + load_model_package(archive) diff --git a/experiments/temporal-models/pyro-detector-baseline/tests/test_predictor_wrapper.py b/experiments/temporal-models/pyro-detector-baseline/tests/test_predictor_wrapper.py new file mode 100644 index 0000000..79963d5 --- /dev/null +++ b/experiments/temporal-models/pyro-detector-baseline/tests/test_predictor_wrapper.py @@ -0,0 +1,105 @@ +"""Tests for predictor_wrapper module.""" + +from unittest.mock import MagicMock + +from PIL import Image + +from pyro_detector_baseline.predictor_wrapper import predict_sequence + + +def _make_frame_paths(tmp_path, n=5): + """Create valid 1x1 JPEG image files and return their paths.""" + images = tmp_path / "images" + images.mkdir(exist_ok=True) + paths = [] + for i in range(n): + p = images / f"cam_2023-01-01T00-00-{i:02d}.jpg" + Image.new("RGB", (1, 1)).save(p) + paths.append(p) + return paths + + +def _mock_predictor(conf_thresh=0.35, predict_values=None): + """Create a mock Predictor with conf_thresh attribute.""" + predictor = MagicMock() + predictor.conf_thresh = conf_thresh + if predict_values is not None: + predictor.predict.side_effect = list(predict_values) + else: + predictor.predict.return_value = 0.0 + return predictor + + +class TestPredictSequence: + def test_all_zeros_returns_negative(self, tmp_path): + paths = _make_frame_paths(tmp_path, n=5) + predictor = _mock_predictor() + + is_alarm, trigger_idx, confs = predict_sequence(predictor, paths, cam_id="test") + + assert is_alarm is False + assert trigger_idx is None + assert confs == [0.0] * 5 + assert predictor.predict.call_count == 5 + + def test_above_threshold_returns_positive(self, tmp_path): + paths = _make_frame_paths(tmp_path, n=5) + predictor = _mock_predictor( + conf_thresh=0.35, predict_values=[0.0, 0.0, 0.6, 0.7, 0.8] + ) + + is_alarm, trigger_idx, confs = predict_sequence(predictor, paths, cam_id="test") + + assert is_alarm is True + assert trigger_idx == 2 + assert confs == [0.0, 0.0, 0.6, 0.7, 0.8] + + def test_trigger_at_first_frame(self, tmp_path): + paths = _make_frame_paths(tmp_path, n=3) + predictor = _mock_predictor(conf_thresh=0.35, predict_values=[0.9, 0.8, 0.7]) + + is_alarm, trigger_idx, confs = predict_sequence(predictor, paths, cam_id="test") + + assert is_alarm is True + assert trigger_idx == 0 + + def test_empty_frames(self, tmp_path): + predictor = _mock_predictor() + + is_alarm, trigger_idx, confs = predict_sequence(predictor, [], cam_id="test") + + assert is_alarm is False + assert trigger_idx is None + assert confs == [] + predictor.predict.assert_not_called() + + def test_cam_id_passed_to_predictor(self, tmp_path): + paths = _make_frame_paths(tmp_path, n=2) + predictor = _mock_predictor() + + predict_sequence(predictor, paths, cam_id="my_seq") + + for call in predictor.predict.call_args_list: + assert ( + call.kwargs.get("cam_id") == "my_seq" + or call[1].get("cam_id") == "my_seq" + ) + + def test_confidence_just_below_threshold(self, tmp_path): + paths = _make_frame_paths(tmp_path, n=3) + predictor = _mock_predictor(conf_thresh=0.35, predict_values=[0.34, 0.34, 0.35]) + + is_alarm, trigger_idx, confs = predict_sequence(predictor, paths, cam_id="test") + + # 0.35 is not > 0.35 (strict inequality matches production) + assert is_alarm is False + assert trigger_idx is None + + def test_confidence_above_threshold(self, tmp_path): + paths = _make_frame_paths(tmp_path, n=3) + predictor = _mock_predictor(conf_thresh=0.35, predict_values=[0.0, 0.36, 0.6]) + + is_alarm, trigger_idx, confs = predict_sequence(predictor, paths, cam_id="test") + + assert 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