fix(detectnet): truncate clustered boxes to MAX_BOXES to avoid broadc…

python/caffe/layers/detectnet/clustering.py

@@ -228,6 +228,7 @@ def cluster(self, net_cvg, net_boxes):
 
         if (boxes_cur_image.shape[0] != 0):
             [r, c] = boxes_cur_image.shape
-            boxes[i, 0:r, 0:c] = boxes_cur_image
+            r = min(r, MAX_BOXES)
+            boxes[i, 0:r, 0:c] = boxes_cur_image[0:r]
 
     return boxes

python/caffe/test/test_detectnet_cluster_max_boxes.py

@@ -0,0 +1,65 @@
+import sys
+import types
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+
+def _stub_caffe_and_cv2():
+    """Provide minimal stubs so clustering.py can be imported without pycaffe."""
+    caffe_pkg = types.ModuleType("caffe")
+    caffe_pkg.__path__ = []
+    caffe_pkg.Layer = object
+    sys.modules["caffe"] = caffe_pkg
+
+    caffe_layers = types.ModuleType("caffe.layers")
+    caffe_layers.__path__ = []
+    sys.modules["caffe.layers"] = caffe_layers
+
+    detectnet_dir = str(Path(__file__).resolve().parent.parent / "layers" / "detectnet")
+    caffe_detectnet = types.ModuleType("caffe.layers.detectnet")
+    caffe_detectnet.__path__ = [detectnet_dir]
+    sys.modules["caffe.layers.detectnet"] = caffe_detectnet
+
+    cv2_stub = types.ModuleType("cv2")
+    cv2_stub.groupRectangles = lambda boxes, *args, **kwargs: ([], [])
+    sys.modules["cv2"] = cv2_stub
+
+
+_stub_caffe_and_cv2()
+from caffe.layers.detectnet.clustering import (  # noqa: E402
+    MAX_BOXES,
+    cluster,
+)
+
+
+class _FakeGroundTruthLayer:
+    is_groundtruth = True
+    image_size_x = 16
+    image_size_y = 16
+    stride = 1
+    coverage_threshold = 0.0
+
+
+def test_cluster_truncates_boxes_to_max_boxes():
+    """cluster() must not crash when an image produces more than MAX_BOXES proposals.
+
+    Regression: the output blob is fixed at [batch_size, MAX_BOXES, 5], but the
+    number of proposals was unbounded, so assigning more than MAX_BOXES rows
+    raised ValueError: could not broadcast input array from shape (256,4)
+    into shape (50,4).
+    """
+    layer = _FakeGroundTruthLayer()
+
+    # 16x16 grid, stride 1, every cell covered => 256 ground-truth proposals.
+    net_cvg = np.ones((1, 1, 16, 16), dtype=np.float32)
+    net_boxes = np.zeros((1, 4, 16, 16), dtype=np.float32)
+    net_boxes[0, 2, :, :] = 1.0  # width
+    net_boxes[0, 3, :, :] = 1.0  # height
+
+    result = cluster(layer, net_cvg, net_boxes)
+
+    assert result.shape == (1, MAX_BOXES, 5)
+    # All MAX_BOXES slots should be populated (no all-zero padding from a crash).
+    assert np.count_nonzero(result[0, :, :]) > 0