feat(cnn_v3/training): add --single-sample option + doc fixes

- train_cnn_v3.py: --single-sample <dir> implies --full-image + --batch-size 1
- cnn_v3_utils.py: CNNv3Dataset accepts single_sample= kwarg (explicit override)
- HOWTO.md: document --single-sample workflow, fix pack_photo_sample.py usage (--target required)
- HOW_TO_CNN.md: fix GBufferEffect seq input (prev_cnn→source), fix binary name (demo→demo64k), add --resume to flag table, remove stale "pack without target" block

handoff(Gemini): --single-sample <dir> added to train_cnn_v3.py; docs audited and corrected

1 files changed, 7 insertions, 0 deletions

diff --git a/cnn_v3/training/train_cnn_v3.py b/cnn_v3/training/train_cnn_v3.py
index de10d6a..31cfd9d 100644
--- a/cnn_v3/training/train_cnn_v3.py
+++ b/cnn_v3/training/train_cnn_v3.py
@@ -104,6 +104,10 @@ def train(args):
     enc_channels = [int(c) for c in args.enc_channels.split(',')]
     print(f"Device: {device}")
 
+    if args.single_sample:
+        args.full_image  = True
+        args.batch_size  = 1
+
     dataset = CNNv3Dataset(
         dataset_dir=args.input,
         input_mode=args.input_mode,
@@ -115,6 +119,7 @@ def train(args):
         detector=args.detector,
         augment=True,
         patch_search_window=args.patch_search_window,
+        single_sample=args.single_sample,
     )
     loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True,
                         num_workers=0, drop_last=False)
@@ -222,6 +227,8 @@ def main():
     p = argparse.ArgumentParser(description='Train CNN v3 (U-Net + FiLM)')
 
     # Dataset
+    p.add_argument('--single-sample', default='', metavar='DIR',
+                   help='Train on a single sample directory; implies --full-image and --batch-size 1')
     p.add_argument('--input', default='training/dataset',
                    help='Dataset root (contains full/ or simple/ subdirs)')
     p.add_argument('--input-mode', default='simple', choices=['simple', 'full'],