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This commit is contained in:
rpotter6298
2026-02-26 06:47:58 +01:00
parent 17a255daa8
commit 8980cf5f9b
10 changed files with 977 additions and 63 deletions
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scripts/main/refuge/refuge_build.py
+30 -40
View File
@@ -2,7 +2,6 @@
Usage examples (after activating .venv_refuge):
python refuge_build.py --train-seg
python refuge_build.py --train-clf
python refuge_build.py --eval --with-ttt
@@ -297,6 +296,8 @@ def build_papila_records(
papila_clf.backbone.to(args.device)
papila_clf.classifier_head.to(args.device)
papila_clf.rotation_head.to(args.device)
if getattr(args, "clear_clf_cache", False):
papila_clf.clear_disk_cache()
records = papila_clf.build_records_for_samples(
samples, crop_scale=args.crop_scale, progress_prefix="papila"
@@ -305,24 +306,6 @@ def build_papila_records(
return records, papila_clf
def train_segmentation(args: argparse.Namespace) -> None:
pre = ensure_preprocessing()
seg = RefugeSegmentation(pre)
seg.build_datasets(
image_size=args.seg_image_size,
batch_size=args.seg_batch_size,
num_workers=args.num_workers,
)
history = seg.train(
epochs=args.seg_epochs,
lr=args.seg_lr,
weight_decay=args.seg_weight_decay,
checkpoint_dir=SEG_CKPT.parent,
device=args.device,
)
print("Segmentation training complete. Best Dice:", history.get("best_dice"))
def train_unet_segmenter(args: argparse.Namespace) -> None:
manifest_path = args.seg_manifest or Path("manifest.csv")
mask_cache_dir = None if args.in_memory_cache else args.mask_cache_dir
@@ -402,6 +385,8 @@ def train_classifier(args: argparse.Namespace) -> None:
use_all_labeled=args.clf_use_all,
auto_val_ratio=args.clf_auto_val_ratio,
)
if args.clear_clf_cache:
clf.clear_disk_cache()
clf.build_datasets(
crop_scale=args.crop_scale,
crop_size=args.crop_size,
@@ -531,6 +516,8 @@ def evaluate(args: argparse.Namespace) -> None:
pre = ensure_preprocessing()
seg = _load_segmentation(pre, args)
clf, clf_ckpt = _load_classifier(pre, seg, args)
if args.clear_clf_cache:
clf.clear_disk_cache()
def evaluate_subset(
clf_obj: RefugeClassification,
@@ -637,23 +624,27 @@ def evaluate_segmentation(args: argparse.Namespace) -> None:
in_memory_cache=args.in_memory_cache,
loader_workers=args.loader_workers,
)
if args.seg_weights is None:
raise SystemExit(
"--seg-weights must be specified for --eval-seg; "
"e.g. --seg-weights models/v2/refuge/segmentation/per_image_refuge_build/best.pt"
)
ckpt = args.seg_weights
if not ckpt.exists():
raise FileNotFoundError(f"Segmentation weights not found at {ckpt}")
state = torch.load(ckpt, map_location=segmenter.device)
state_dict = state.get("model", state)
segmenter.model.load_state_dict(state_dict, strict=False)
print(f"[seg-eval] Loaded weights from {ckpt}")
if args.in_memory_cache:
segmenter.prebuild_in_memory_cache(
cache_workers=max(0, int(args.cache_workers)),
include_train=False,
include_val=bool(args.eval_seg_splits is None or "val" in args.eval_seg_splits),
include_holdout=bool(args.eval_seg_splits is None or "holdout" in args.eval_seg_splits),
include_val="val" in args.eval_seg_splits,
include_holdout="holdout" in args.eval_seg_splits,
)
ckpt = resolve_unet_weights(args.seg_weights)
if ckpt.exists():
state = torch.load(ckpt, map_location=segmenter.device)
state_dict = state.get("model", state)
segmenter.model.load_state_dict(state_dict, strict=False)
print(f"[seg-eval] Loaded weights from {ckpt}")
else:
raise FileNotFoundError(f"Segmentation weights not found at {ckpt}")
dataset_filter = args.eval_seg_datasets
split_filter = args.eval_seg_splits
output_dir = args.eval_seg_output or Path("analysis_data/segmenter_eval")
@@ -672,9 +663,6 @@ def evaluate_segmentation(args: argparse.Namespace) -> None:
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="REFUGE pipeline helper")
parser.add_argument(
"--train-seg", action="store_true", help="Train the segmentation model"
)
parser.add_argument(
"--train-unet-seg",
action="store_true",
@@ -809,9 +797,14 @@ def parse_args() -> argparse.Namespace:
parser.add_argument(
"--clf-cache-dir",
type=Path,
default=Path("analysis_data/classifier_cache"),
default=Path("cache_data/classifier_cache"),
help="Directory to cache classifier preprocessing artifacts",
)
parser.add_argument(
"--clear-clf-cache",
action="store_true",
help="Delete all cached geometry/mask files before running (use when segmenter weights have changed)",
)
parser.add_argument(
"--clf-use-all",
action="store_true",
@@ -850,7 +843,8 @@ def parse_args() -> argparse.Namespace:
"--eval-seg-splits",
nargs="+",
choices=["train", "val", "holdout"],
help="Segmentation splits to evaluate (default: val)",
default=["holdout"],
help="Segmentation splits to evaluate (default: holdout)",
)
parser.add_argument(
"--eval-seg-output",
@@ -935,7 +929,6 @@ def main() -> None:
if not any(
[
args.train_seg,
args.train_unet_seg,
args.train_clf,
args.eval,
@@ -944,12 +937,9 @@ def main() -> None:
]
):
raise SystemExit(
"Specify at least one action: --train-seg, --train-unet-seg, --train-clf, --eval, --eval-seg, or --export-backbone"
"Specify at least one action: --train-unet-seg, --train-clf, --eval, --eval-seg, or --export-backbone"
)
if args.train_seg:
train_segmentation(args)
if args.train_unet_seg:
train_unet_segmenter(args)
scripts/main/v2/run_modes_roi_overnight.sh
Executable
+34
View File
@@ -0,0 +1,34 @@
#!/usr/bin/env bash
set -euo pipefail
# Runs two back-to-back Hypertower mode comparisons with ROI cropping:
# 1) GT masks
# 2) UNet masks
ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/../../.." && pwd)"
cd "$ROOT_DIR"
COMMON_ARGS=(
--eval-modes binary multiclass
--tower-modes single ensemble bilateral
--epochs 40
--n-splits 5
--batch-size 8
--backbone refugelike
--img-crop-manifest manifest.csv
)
echo "[1/2] Starting GT ROI run..."
python3 scripts/basic_analysis/compare_hypertower_modes.py \
"${COMMON_ARGS[@]}" \
--img-crop-gt \
--run-name v2_modes_full_40ep_5fold_roi_gt_holdout
echo "[2/2] Starting UNet ROI run..."
python3 scripts/basic_analysis/compare_hypertower_modes.py \
"${COMMON_ARGS[@]}" \
--img-crop-weights models/v2/refuge/segmentation/per_image_refuge_build/best.pt \
--img-crop-normalize per_image \
--run-name v2_modes_full_40ep_5fold_roi_unet_perimage_refugebuild_holdout
echo "All runs complete."
scripts/main/v2/run_modes_roi_smoke.sh
Executable
+42
View File
@@ -0,0 +1,42 @@
#!/usr/bin/env bash
set -euo pipefail
# Quick smoke test for ROI mode runs:
# 1) GT masks
# 2) UNet masks
# Uses 1 epoch and 1 fold for fast validation.
ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/../../.." && pwd)"
cd "$ROOT_DIR"
COMMON_ARGS=(
--eval-modes binary multiclass
--tower-modes single ensemble bilateral
--epochs 1
--n-splits 2
--folds 1
--batch-size 8
--backbone refugelike
--img-crop-manifest manifest.csv
--warmup-tower-epochs 0
--warmup-fused-epochs 0
--single-warmup-tower-epochs 0
--single-warmup-fused-epochs 0
--bilat-warmup-tower-epochs 0
--bilat-warmup-fused-epochs 0
)
echo "[smoke 1/2] Starting GT ROI run..."
python3 scripts/basic_analysis/compare_hypertower_modes.py \
"${COMMON_ARGS[@]}" \
--img-crop-gt \
--run-name smoke_v2_modes_roi_gt
echo "[smoke 2/2] Starting UNet ROI run..."
python3 scripts/basic_analysis/compare_hypertower_modes.py \
"${COMMON_ARGS[@]}" \
--img-crop-weights models/v2/refuge/segmentation/per_image_refuge_build/best.pt \
--img-crop-normalize per_image \
--run-name smoke_v2_modes_roi_unet_perimage
echo "Smoke runs complete."