Enhance bipartite_match_op to support argmax matching after bipartite matching. (#8580)

* Enhance bipartite_match_op to support argmax matching after bipartite matching.

* Fix typo error.

paddle/fluid/operators/bipartite_match_op.cc

@@ -94,6 +94,38 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
     }
   }
 
+  void ArgMaxMatch(const Tensor& dist, int* match_indices, T* match_dist,
+                   T overlap_threshold) const {
+    constexpr T kEPS = static_cast<T>(1e-6);
+    int64_t row = dist.dims()[0];
+    int64_t col = dist.dims()[1];
+    auto* dist_data = dist.data<T>();
+    for (int64_t j = 0; j < col; ++j) {
+      if (match_indices[j] != -1) {
+        // the j-th column has been matched to one entity.
+        continue;
+      }
+      int max_row_idx = -1;
+      T max_dist = -1;
+      for (int i = 0; i < row; ++i) {
+        T dist = dist_data[i * col + j];
+        if (dist < kEPS) {
+          // distance is 0 between m-th row and j-th column
+          continue;
+        }
+        if (dist >= overlap_threshold && dist > max_dist) {
+          max_row_idx = i;
+          max_dist = dist;
+        }
+      }
+      if (max_row_idx != -1) {
+        PADDLE_ENFORCE_EQ(match_indices[j], -1);
+        match_indices[j] = max_row_idx;
+        match_dist[j] = max_dist;
+      }
+    }
+  }
+
   void Compute(const framework::ExecutionContext& context) const override {
     auto* dist_mat = context.Input<LoDTensor>("DistMat");
     auto* match_indices = context.Output<Tensor>("ColToRowMatchIndices");
@@ -120,13 +152,21 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
 
     int* indices = match_indices->data<int>();
     T* dist = match_dist->data<T>();
+    auto type = context.Attr<std::string>("match_type");
+    auto threshold = context.Attr<float>("dist_threshold");
     if (n == 1) {
       BipartiteMatch(*dist_mat, indices, dist);
+      if (type == "per_prediction") {
+        ArgMaxMatch(*dist_mat, indices, dist, threshold);
+      }
     } 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);
+        }
       }
     }
   }
@@ -147,6 +187,19 @@ class BipartiteMatchOpMaker : public framework::OpProtoAndCheckerMaker {
         "This tensor can contain LoD information to represent a batch of "
         "inputs. One instance of this batch can contain different numbers of "
         "entities.");
+    AddAttr<std::string>(
+        "match_type",
+        "(string, defalut: per_prediction) "
+        "The type of matching method, should be 'bipartite' or "
+        "'per_prediction', 'bipartite' by defalut.")
+        .SetDefault("bipartite")
+        .InEnum({"bipartite", "per_prediction"});
+    AddAttr<float>(
+        "dist_threshold",
+        "(float, defalut: 0.5) "
+        "If `match_type` is 'per_prediction', this threshold is to determine "
+        "the extra matching bboxes based on the maximum distance.")
+        .SetDefault(0.5);
     AddOutput("ColToRowMatchIndices",
               "(Tensor) A 2-D Tensor with shape [N, M] in int type. "
               "N is the batch size. If ColToRowMatchIndices[i][j] is -1, it "
@@ -168,10 +221,10 @@ distance matrix. For input 2D matrix, the bipartite matching algorithm can
 find the matched column for each row, also can find the matched row for
 each column. And this operator only calculate matched indices from column
 to row. For each instance, the number of matched indices is the number of
-of columns of the input ditance matrix.
+of columns of the input distance matrix.
 
 There are two outputs to save matched indices and distance.
-A simple description, this algothrim matched the best (maximum distance)
+A simple description, this algorithm matched the best (maximum distance)
 row entity to the column entity and the matched indices are not duplicated
 in each row of ColToRowMatchIndices. If the column entity is not matched
 any row entity, set -1 in ColToRowMatchIndices.

python/paddle/fluid/layers/detection.py

@@ -132,7 +132,10 @@ def detection_output(scores,
     return nmsed_outs
 
 
-def bipartite_match(dist_matrix, name=None):
+def bipartite_match(dist_matrix,
+                    match_type=None,
+                    dist_threshold=None,
+                    name=None):
     """
     **Bipartite matchint operator**
 
@@ -164,6 +167,11 @@ def bipartite_match(dist_matrix, name=None):
             This tensor can contain LoD information to represent a batch of
             inputs. One instance of this batch can contain different numbers of
             entities.
+        match_type(string|None): The type of matching method, should be
+           'bipartite' or 'per_prediction', 'bipartite' by defalut.
+        dist_threshold(float|None): If `match_type` is 'per_prediction',
+            this threshold is to determine the extra matching bboxes based
+            on the maximum distance, 0.5 by defalut.
     Returns:
         match_indices(Variable): A 2-D Tensor with shape [N, M] in int type.
             N is the batch size. If match_indices[i][j] is -1, it
@@ -183,6 +191,10 @@ def bipartite_match(dist_matrix, name=None):
     helper.append_op(
         type='bipartite_match',
         inputs={'DistMat': dist_matrix},
+        attrs={
+            'match_type': match_type,
+            'dist_threshold': dist_threshold,
+        },
         outputs={
             'ColToRowMatchIndices': match_indices,
             'ColToRowMatchDist': match_distance
@@ -333,7 +345,7 @@ def ssd_loss(location,
         loc_loss_weight (float): Weight for localization loss, 1.0 by default.
         conf_loss_weight (float): Weight for confidence loss, 1.0 by default.
         match_type (str): The type of matching method during training, should
-            be 'bipartite' or 'per_prediction'.
+            be 'bipartite' or 'per_prediction', 'per_prediction' by defalut.
         mining_type (str): The hard example mining type, should be 'hard_example'
             or 'max_negative', now only support `max_negative`.
 
@@ -381,7 +393,8 @@ def ssd_loss(location,
     #   1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
     iou = iou_similarity(x=gt_box, y=prior_box)
     #   1.2 Compute matched boundding box by bipartite matching algorithm.
-    matched_indices, matched_dist = bipartite_match(iou)
+    matched_indices, matched_dist = bipartite_match(iou, match_type,
+                                                    overlap_threshold)
 
     # 2. Compute confidence for mining hard examples
     # 2.1. Get the target label based on matched indices

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

@@ -46,7 +46,20 @@ def bipartite_match(distance, match_indices, match_dist):
             idx += 1
 
 
-def batch_bipartite_match(distance, lod):
+def argmax_match(distance, match_indices, match_dist, threshold):
+    r, c = distance.shape
+    for j in xrange(c):
+        if match_indices[j] != -1:
+            continue
+        col_dist = distance[:, j]
+        indices = np.argwhere(col_dist >= threshold).flatten()
+        if len(indices) < 1:
+            continue
+        match_indices[j] = indices[np.argmax(col_dist[indices])]
+        match_dist[j] = col_dist[match_indices[j]]
+
+
+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].
@@ -59,6 +72,9 @@ def batch_bipartite_match(distance, lod):
     for i in range(len(lod) - 1):
         bipartite_match(distance[lod[i]:lod[i + 1], :], match_indices[i, :],
                         match_dist[i, :])
+        if match_type == 'per_prediction':
+            argmax_match(distance[lod[i]:lod[i + 1], :], match_indices[i, :],
+                         match_dist[i, :], dist_threshold)
     return match_indices, match_dist
 
 
@@ -71,8 +87,8 @@ class TestBipartiteMatchOpWithLoD(OpTest):
 
         self.inputs = {'DistMat': (dist, lod)}
         self.outputs = {
-            'ColToRowMatchIndices': (match_indices),
-            'ColToRowMatchDist': (match_dist),
+            'ColToRowMatchIndices': match_indices,
+            'ColToRowMatchDist': match_dist,
         }
 
     def test_check_output(self):
@@ -96,5 +112,27 @@ class TestBipartiteMatchOpWithoutLoD(OpTest):
         self.check_output()
 
 
+class TestBipartiteMatchOpWithPerPredictionType(OpTest):
+    def setUp(self):
+        self.op_type = 'bipartite_match'
+        lod = [[0, 5, 11, 23]]
+        dist = np.random.random((23, 237)).astype('float32')
+        match_indices, match_dist = batch_bipartite_match(dist, lod[0],
+                                                          'per_prediction', 0.5)
+
+        self.inputs = {'DistMat': (dist, lod)}
+        self.outputs = {
+            'ColToRowMatchIndices': match_indices,
+            'ColToRowMatchDist': match_dist,
+        }
+        self.attrs = {
+            'match_type': 'per_prediction',
+            'dist_threshold': 0.5,
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
 if __name__ == '__main__':
     unittest.main()