def test_multiclass_nms(self):
boxes = np.array(
[[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
dtype=np.float32)
mask0 = np.array([[0, 0, 0, 0, 0],
[0, 0, 1, 1, 0],
[0, 0, 1, 1, 0],
[0, 0, 1, 1, 0],
[0, 0, 0, 0, 0]],
dtype=np.uint8)
mask1 = np.array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]],
dtype=np.uint8)
mask2 = np.array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]],
dtype=np.uint8)
masks = np.stack([mask0, mask1, mask2])
box_mask_list = np_box_mask_list.BoxMaskList(
box_data=boxes, mask_data=masks)
scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
[0.7, -0.7, 0.6, 0.2, -0.9],
[0.4, 0.34, -0.9, 0.2, 0.31]],
dtype=np.float32)
box_mask_list.add_field('scores', scores)
box_mask_list_clean = np_box_mask_list_ops.multi_class_non_max_suppression(
box_mask_list, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)
scores_clean = box_mask_list_clean.get_field('scores')
classes_clean = box_mask_list_clean.get_field('classes')
boxes = box_mask_list_clean.get()
masks = box_mask_list_clean.get_masks()
expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
expected_classes = np.array([0, 2, 1, 4])
expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
[0.4, 0.2, 0.8, 0.8],
[0.6, 0.0, 1.0, 1.0],
[0.6, 0.0, 1.0, 1.0]],
dtype=np.float32)
self.assertAllClose(scores_clean, expected_scores)
self.assertAllClose(classes_clean, expected_classes)
self.assertAllClose(boxes, expected_boxes)
def test_multiclass_nms(self):
boxes = np.array(
[[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
dtype=np.float32)
mask0 = np.array([[0, 0, 0, 0, 0],
[0, 0, 1, 1, 0],
[0, 0, 1, 1, 0],
[0, 0, 1, 1, 0],
[0, 0, 0, 0, 0]],
dtype=np.uint8)
mask1 = np.array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]],
dtype=np.uint8)
mask2 = np.array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]],
dtype=np.uint8)
masks = np.stack([mask0, mask1, mask2])
box_mask_list = np_box_mask_list.BoxMaskList(
box_data=boxes, mask_data=masks)
scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
[0.7, -0.7, 0.6, 0.2, -0.9],
[0.4, 0.34, -0.9, 0.2, 0.31]],
dtype=np.float32)
box_mask_list.add_field('scores', scores)
box_mask_list_clean = np_box_mask_list_ops.multi_class_non_max_suppression(
box_mask_list, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)
scores_clean = box_mask_list_clean.get_field('scores')
classes_clean = box_mask_list_clean.get_field('classes')
boxes = box_mask_list_clean.get()
masks = box_mask_list_clean.get_masks()
expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
expected_classes = np.array([0, 2, 1, 4])
expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
[0.4, 0.2, 0.8, 0.8],
[0.6, 0.0, 1.0, 1.0],
[0.6, 0.0, 1.0, 1.0]],
dtype=np.float32)
self.assertAllClose(scores_clean, expected_scores)
self.assertAllClose(classes_clean, expected_classes)
self.assertAllClose(boxes, expected_boxes)