[bugfix] - support large CPU-side datasets; fix numpy/CuPy interop

train.py: add _to_gpu() helper; convert mini-batches from CPU numpy to GPU
CuPy on the fly so large datasets can stay in RAM. Use numpy permutation for
numpy batches during shuffle. Limit status err_rms to first 5000 samples to
avoid GPU OOM on status checks.

model.py: replace np.copy() with np.array() — cupy.copy() does not convert
numpy arrays to CuPy; cupy.array() does. Fixes entity.forward() crash after
training when batch was passed as numpy.

test_faces_sub_image.py: load_patches normalizes on CPU and returns numpy,
avoiding multi-copy GPU OOM for large datasets (500 images ≈ 1.4 GB).
show_reconstructions converts numpy patches to CuPy before inference.
Add --n_images CLI arg.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/rbm/model.py

@@ -22,7 +22,7 @@ class Model(ABC):
 
 	def train(self, batch: Mat):
 		entities = self.objects(Entity)
-		_batch = np.copy(batch)
+		_batch = np.array(batch)   # np.array() converts numpy→CuPy; np.copy() does not
 		for entity in entities:
 			if entity.enable_training:
 				train(entity, _batch, Status())

src/rbm/train.py

@@ -1,7 +1,14 @@
+import numpy as _np_cpu
 from .matrix import sample, sample_gaussian, prob, rms_error_accu, Mat, np
 from .entity import Entity
 from .status import Status
 
+def _to_gpu(arr):
+	"""Convert numpy array to CuPy if needed; CuPy arrays pass through unchanged."""
+	if isinstance(arr, _np_cpu.ndarray):
+		return np.array(arr)
+	return arr
+
 # %%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 #   data = batchdata(:,:,batch);
 #   poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1)));
@@ -182,10 +189,11 @@ def train(entity: Entity, batch: Mat, status: Status):
 		if not keep_running:
 			break
 
-		batch = batch[np.random.permutation(batch.shape[0])]
+		perm  = _np_cpu.random.permutation(batch.shape[0]) if isinstance(batch, _np_cpu.ndarray) else np.random.permutation(batch.shape[0])
+		batch = batch[perm]
 		for mini_batch in to_mini_batch(batch, mini_batch_size):
 			# Contrastive divergence learning: calculate gradients
-			dwhv, dbv, dbh = cd_func(entity, mini_batch)
+			dwhv, dbv, dbh = cd_func(entity, _to_gpu(mini_batch))
 
 			# Adjust weight and biases
 			grad = entity.grad_compute(dbv, dbh, dwhv)
@@ -193,8 +201,9 @@ def train(entity: Entity, batch: Mat, status: Status):
 
 			# check if status update is needed
 			if status.want_report(round(progress)):
-				# Calculate	error
-				err_rms = rms_error_accu(batch - entity.reconstruct(entity.forward(batch)))
+				# Calculate error on a GPU-safe subset (avoids OOM for large CPU datasets)
+				status_batch = _to_gpu(batch[:min(5000, batch.shape[0])])
+				err_rms = rms_error_accu(status_batch - entity.reconstruct(entity.forward(status_batch)))
 				if not status.on_change(entity, {"progress": {"value": round(progress), "unit": "%"},
 										 "err_rms": {"value": err_rms, "unit": ""}}):
 					keep_running = False
@@ -203,7 +212,8 @@ def train(entity: Entity, batch: Mat, status: Status):
 			progress += d_progress*mini_batch.shape[0]
 
 	# Update final status
-	err_rms = rms_error_accu(batch - entity.reconstruct(entity.forward(batch)))
+	status_batch = _to_gpu(batch[:min(5000, batch.shape[0])])
+	err_rms = rms_error_accu(status_batch - entity.reconstruct(entity.forward(status_batch)))
 	status.on_change(entity, {"progress": {"value": round(progress), "unit": "%"},
 					  "err_rms_total": {"value": err_rms, "unit": ""}})
 

src/tests/test_faces_sub_image.py

@@ -77,7 +77,11 @@ def load_patches(data_dir: str, n_images: int = N_IMAGES, start: int = 0):
 	patches_np = numpy.concatenate(all_patches, axis=0)
 	std = numpy.std(patches_np, axis=1)
 	patches_np = patches_np[std > 0.01]
-	return normalize(np.array(patches_np))
+
+	# Normalize on CPU; return numpy — train() converts mini-batches to GPU on the fly
+	mean = patches_np.mean(axis=1, keepdims=True)
+	std2 = patches_np.std(axis=1, keepdims=True)
+	return (patches_np - mean) / numpy.where(std2 < 1e-8, 1.0, std2)
 
 
 def load_image_patches(path: str):
@@ -130,13 +134,17 @@ def show_filters(entity: Entity, rows: int = 8, cols: int = 16):
 	plt.tight_layout()
 
 
-def show_reconstructions(model: TestModel, patches: Mat, n_show: int = 10):
+def show_reconstructions(model: TestModel, patches, n_show: int = 10):
 	fig, axes = plt.subplots(2, n_show, figsize=(n_show * 1.5, 3.5))
 	axes[0, 0].set_ylabel('Original')
 	axes[1, 0].set_ylabel('Recon')
 	fig.suptitle('Patch reconstructions (normalised display)', fontsize=10)
 	err_total = 0.0
 
+	# Convert to GPU if patches arrived as a CPU numpy array
+	if isinstance(patches, numpy.ndarray):
+		patches = np.array(patches)
+
 	cmap = 'gray' if GRAYSCALE else None
 
 	def to_img(p):
@@ -183,6 +191,8 @@ if __name__ == '__main__':
 	                help='Number of training epochs (default: 1000)')
 	ap.add_argument('--mini_batch_size', type=int,   default=1000,
 	                help='Mini-batch size (default: 1000)')
+	ap.add_argument('--n_images',        type=int,   default=N_IMAGES,
+	                help=f'Number of training images (default: {N_IMAGES})')
 	args = ap.parse_args()
 
 	if args.grayscale:
@@ -201,8 +211,8 @@ if __name__ == '__main__':
 		model.load()
 
 	if args.do_train:
-		print(f'Loading {N_IMAGES} training images (stride={STRIDE})...')
-		train_patches = load_patches(DATA_DIR, N_IMAGES)
+		print(f'Loading {args.n_images} training images (stride={STRIDE})...')
+		train_patches = load_patches(DATA_DIR, args.n_images)
 		print(f'Training on {train_patches.shape[0]} patches ({N_VIS}-dim each)')
 		model.train(train_patches)
 		model.save()