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c5b096b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | """DiaFoot.AI v2 — Data Composition Ablation.
The most important experiment: prove that adding healthy + non-DFU data helps.
Trains 3 segmentation models:
(a) DFU-only: Train only on DFU images
(b) DFU + non-DFU: Train on DFU + non-DFU (current best)
(c) All: Train on all three classes (including healthy)
Usage:
python scripts/run_ablation.py --variant dfu_only --device cuda --epochs 50
python scripts/run_ablation.py --variant dfu_nondfu --device cuda --epochs 50
python scripts/run_ablation.py --variant all --device cuda --epochs 50
"""
from __future__ import annotations
import argparse
import logging
import sys
from pathlib import Path
import torch
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from scripts.train import build_dataloaders
from src.models.unetpp import build_unetpp
from src.training.losses import DiceCELoss
from src.training.schedulers import CosineAnnealingWithWarmup
from src.training.trainer import TrainConfig, Trainer
ABLATION_CONFIGS = {
"dfu_only": {
"classes": ["dfu"],
"checkpoint_dir": "checkpoints/ablation_dfu_only",
"description": "DFU images only (no negatives)",
},
"dfu_nondfu": {
"classes": ["dfu", "non_dfu"],
"checkpoint_dir": "checkpoints/ablation_dfu_nondfu",
"description": "DFU + non-DFU wounds (current approach)",
},
"all": {
"classes": None, # No filter = all classes
"checkpoint_dir": "checkpoints/ablation_all",
"description": "All classes including healthy",
},
}
def main() -> None:
"""Run data composition ablation."""
parser = argparse.ArgumentParser(description="Data Composition Ablation")
parser.add_argument(
"--variant",
type=str,
required=True,
choices=list(ABLATION_CONFIGS.keys()),
)
parser.add_argument("--splits-dir", type=str, default="data/splits")
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("--epochs", type=int, default=50)
parser.add_argument("--batch-size", type=int, default=16)
parser.add_argument("--num-workers", type=int, default=8)
parser.add_argument("--verbose", action="store_true")
args = parser.parse_args()
logging.basicConfig(
level=logging.DEBUG if args.verbose else logging.INFO,
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%H:%M:%S",
)
logger = logging.getLogger("ablation")
config = ABLATION_CONFIGS[args.variant]
logger.info("Ablation: %s — %s", args.variant, config["description"])
model = build_unetpp(
encoder_name="efficientnet-b4",
encoder_weights="imagenet",
classes=1,
decoder_attention_type="scse",
)
train_loader, val_loader = build_dataloaders(
args.splits_dir,
args.batch_size,
args.num_workers,
filter_classes=config["classes"],
)
logger.info(
"Data: %d train, %d val batches",
len(train_loader),
len(val_loader),
)
loss_fn = DiceCELoss()
optimizer = torch.optim.AdamW(
model.parameters(),
lr=1e-4,
weight_decay=1e-2,
)
scheduler = CosineAnnealingWithWarmup(
optimizer,
warmup_epochs=5,
max_epochs=args.epochs,
)
torch.manual_seed(42)
trainer_config = TrainConfig(
epochs=args.epochs,
precision="bf16-mixed",
compile_model=False,
gradient_clip=1.0,
checkpoint_dir=config["checkpoint_dir"],
monitor_metric="val/loss",
monitor_mode="min",
device=args.device,
early_stopping_patience=15,
)
trainer = Trainer(model=model, config=trainer_config)
trainer.fit(train_loader, val_loader, loss_fn, optimizer, scheduler)
logger.info("Ablation %s complete.", args.variant)
if __name__ == "__main__":
main()
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