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206d8b5 12d831f 206d8b5 12d831f 206d8b5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | """
Run the feedback-to-model refresh pipeline in one command.
Usage:
python scripts/retrain_from_feedback.py
python scripts/retrain_from_feedback.py --skip_train
python scripts/retrain_from_feedback.py --dry_run
"""
import argparse
import subprocess
import sys
from pathlib import Path
def run_step(command: list[str], dry_run: bool) -> None:
rendered = " ".join(command)
print(f"\n>> {rendered}")
if dry_run:
return
subprocess.run(command, check=True)
def count_feedback_records(feedback_dir: Path) -> int:
if not feedback_dir.exists():
return 0
return sum(1 for path in feedback_dir.rglob("*.json") if path.is_file())
def main() -> None:
parser = argparse.ArgumentParser(description="Ingest reviewed feedback and optionally retrain the model.")
parser.add_argument("--feedback_dir", default="feedback_queue")
parser.add_argument("--feedback_output_dir", default="data/feedback_labeled")
parser.add_argument("--trashnet_dir", default="data/raw/trashnet_sample/dataset-resized")
parser.add_argument("--realwaste_dir", default="data/raw/realwaste")
parser.add_argument("--extra_dir", default="data/local_boost")
parser.add_argument("--processed_dir", default="data/processed")
parser.add_argument("--models_dir", default="models")
parser.add_argument("--phase1_epochs", type=int, default=3)
parser.add_argument("--phase2_epochs", type=int, default=12)
parser.add_argument("--balance_strategy", choices=["class_weight", "oversample"], default="class_weight")
parser.add_argument("--skip_train", action="store_true")
parser.add_argument("--skip_export", action="store_true")
parser.add_argument("--benchmark", action="store_true")
parser.add_argument("--overwrite_feedback", action="store_true")
parser.add_argument("--min_feedback", type=int, default=20)
parser.add_argument("--force_train", action="store_true")
parser.add_argument("--dry_run", action="store_true")
args = parser.parse_args()
project_root = Path(__file__).resolve().parent.parent
python_executable = Path(sys.executable)
ingest_command = [
str(python_executable),
"scripts/ingest_feedback.py",
"--feedback_dir",
args.feedback_dir,
"--output_dir",
args.feedback_output_dir,
]
if args.overwrite_feedback:
ingest_command.append("--overwrite")
prepare_command = [
str(python_executable),
"scripts/download_data.py",
"--trashnet_dir",
args.trashnet_dir,
"--realwaste_dir",
args.realwaste_dir,
"--feedback_dir",
args.feedback_output_dir,
"--extra_dir",
args.extra_dir,
"--output_dir",
args.processed_dir,
]
train_command = [
str(python_executable),
"scripts/train.py",
"--data_dir",
args.processed_dir,
"--output_dir",
args.models_dir,
"--phase1_epochs",
str(args.phase1_epochs),
"--phase2_epochs",
str(args.phase2_epochs),
"--balance_strategy",
args.balance_strategy,
]
export_command = [
str(python_executable),
"scripts/export_tflite.py",
"--saved_model",
str(Path(args.models_dir) / "waste_classifier_v1"),
"--data_dir",
args.processed_dir,
"--output",
str(Path(args.models_dir) / "model.tflite"),
]
if args.benchmark:
export_command.append("--benchmark")
print("Feedback Retraining Pipeline")
print(f"Project root : {project_root}")
print(f"Python : {python_executable}")
run_step(ingest_command, args.dry_run)
run_step(prepare_command, args.dry_run)
feedback_count = count_feedback_records(project_root / args.feedback_dir)
print(f"Feedback count: {feedback_count}")
should_train = not args.skip_train
if should_train and not args.force_train and feedback_count < args.min_feedback:
print(
f"\nSkipping training because feedback count is below --min_feedback "
f"({feedback_count} < {args.min_feedback})."
)
print("Use --force_train to retrain anyway.")
should_train = False
if should_train:
run_step(train_command, args.dry_run)
if not args.skip_export:
run_step(export_command, args.dry_run)
elif not args.skip_export:
print("\nSkipping export because training was skipped.")
print("\nPipeline complete.")
if __name__ == "__main__":
main()
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