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

2 files changed, 21 insertions, 11 deletions

diff --git a/cnn_v3/training/cnn_v3_utils.py b/cnn_v3/training/cnn_v3_utils.py
index bef4091..50707a2 100644
--- a/cnn_v3/training/cnn_v3_utils.py
+++ b/cnn_v3/training/cnn_v3_utils.py
@@ -286,7 +286,8 @@ class CNNv3Dataset(Dataset):
                  channel_dropout_p: float = 0.3,
                  detector: str = 'harris',
                  augment: bool = True,
-                 patch_search_window: int = 0):
+                 patch_search_window: int = 0,
+                 single_sample: str = ''):
         self.patch_size          = patch_size
         self.patches_per_image   = patches_per_image
         self.image_size          = image_size
@@ -296,16 +297,18 @@ class CNNv3Dataset(Dataset):
         self.augment             = augment
         self.patch_search_window = patch_search_window
 
-        root       = Path(dataset_dir)
-        subdir     = 'full' if input_mode == 'full' else 'simple'
-        search_dir = root / subdir
-        if not search_dir.exists():
-            search_dir = root
-
-        self.samples = sorted([
-            d for d in search_dir.iterdir()
-            if d.is_dir() and (d / 'albedo.png').exists()
-        ])
+        if single_sample:
+            self.samples = [Path(single_sample)]
+        else:
+            root       = Path(dataset_dir)
+            subdir     = 'full' if input_mode == 'full' else 'simple'
+            search_dir = root / subdir
+            if not search_dir.exists():
+                search_dir = root
+            self.samples = sorted([
+                d for d in search_dir.iterdir()
+                if d.is_dir() and (d / 'albedo.png').exists()
+            ])
         if not self.samples:
             raise RuntimeError(f"No samples found in {search_dir}")
 
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'],