Support empty bbox in bipartite math op (#26488)

paddle/fluid/operators/detection/bipartite_match_op.cc

@@ -222,10 +222,12 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
     } else {
       auto lod = dist_mat->lod().back();
       for (size_t i = 0; i < lod.size() - 1; ++i) {
-        Tensor one_ins = dist_mat->Slice(lod[i], lod[i + 1]);
-        BipartiteMatch(one_ins, indices + i * col, dist + i * col);
-        if (type == "per_prediction") {
-          ArgMaxMatch(one_ins, indices + i * col, dist + i * col, threshold);
+        if (lod[i + 1] > lod[i]) {
+          Tensor one_ins = dist_mat->Slice(lod[i], lod[i + 1]);
+          BipartiteMatch(one_ins, indices + i * col, dist + i * col);
+          if (type == "per_prediction") {
+            ArgMaxMatch(one_ins, indices + i * col, dist + i * col, threshold);
+          }
         }
       }
     }

python/paddle/fluid/tests/unittests/test_bipartite_match_op.py

@@ -65,7 +65,7 @@ def batch_bipartite_match(distance, lod, match_type=None, dist_threshold=None):
     """Bipartite Matching algorithm for batch input.
     Arg:
         distance (numpy.array) : The distance of two entries with shape [M, N].
-        lod (list of int): The offsets of each input in this batch.
+        lod (list of int): The length of each input in this batch.
     """
     n = len(lod)
     m = distance.shape[1]
@@ -73,6 +73,7 @@ def batch_bipartite_match(distance, lod, match_type=None, dist_threshold=None):
     match_dist = np.zeros((n, m), dtype=np.float32)
     cur_offset = 0
     for i in range(n):
+        if lod[i] == 0: continue
         bipartite_match(distance[cur_offset:(cur_offset + lod[i]), :],
                         match_indices[i, :], match_dist[i, :])
         if match_type == 'per_prediction':
@@ -155,5 +156,22 @@ class TestBipartiteMatchOpWithPerPredictionType(OpTest):
         self.check_output()
 
 
+class TestBipartiteMatchOpWithEmptyLoD(OpTest):
+    def setUp(self):
+        self.op_type = 'bipartite_match'
+        lod = [[5, 6, 0, 12]]
+        dist = np.random.random((23, 217)).astype('float32')
+        match_indices, match_dist = batch_bipartite_match(dist, lod[0])
+
+        self.inputs = {'DistMat': (dist, lod)}
+        self.outputs = {
+            'ColToRowMatchIndices': match_indices,
+            'ColToRowMatchDist': match_dist,
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
 if __name__ == '__main__':
     unittest.main()