[Relay] symbolic max_output_size (apache#5844)

* symbolic max_output_size

* pylint

* fix ci

include/tvm/relay/attrs/vision.h

@@ -88,7 +88,7 @@ struct GetValidCountsAttrs : public tvm::AttrsNode<GetValidCountsAttrs> {
 
 /*! \brief Attributes used in non_maximum_suppression operator */
 struct NonMaximumSuppressionAttrs : public tvm::AttrsNode<NonMaximumSuppressionAttrs> {
-  int max_output_size;
+  Optional<Integer> max_output_size;
   double iou_threshold;
   bool force_suppress;
   int top_k;
@@ -99,11 +99,7 @@ struct NonMaximumSuppressionAttrs : public tvm::AttrsNode<NonMaximumSuppressionA
   bool invalid_to_bottom;
 
   TVM_DECLARE_ATTRS(NonMaximumSuppressionAttrs, "relay.attrs.NonMaximumSuppressionAttrs") {
-    TVM_ATTR_FIELD(max_output_size)
-        .set_default(-1)
-        .describe(
-            "Max number of output valid boxes for each instance."
-            "By default all valid boxes are returned.");
+    TVM_ATTR_FIELD(max_output_size).describe("Max number of output valid boxes for each instance.");
     TVM_ATTR_FIELD(iou_threshold)
         .set_default(0.5)
         .describe("Non-maximum suppression iou threshold.");

python/tvm/relay/frontend/tensorflow.py

@@ -625,7 +625,6 @@ def _impl(inputs, attr, params, mod):
 def _nms():
     def _impl(inputs, attr, params, mod):
         # Get parameter values
-        # TODO(yongwww) change nms in relay to support symbolic max_output_size
         try:
             max_output_size = int(np.atleast_1d(inputs[2].data.asnumpy()
                                                 .astype("int64"))[0])
@@ -634,7 +633,7 @@ def _impl(inputs, attr, params, mod):
                 max_output_size = _infer_value(inputs[2], params,
                                                mod).asnumpy().astype("int64").tolist()[0]
             except Exception:
-                max_output_size = -1
+                max_output_size = inputs[2]
         iou_threshold = np.atleast_1d(inputs[3].data.asnumpy())[0]
         # score_threshold was introduced from V3
         score_threshold = np.atleast_1d(inputs[4].data.asnumpy())[0] if len(inputs) > 4 else 0.0

python/tvm/relay/op/strategy/generic.py

@@ -747,8 +747,10 @@ def get_valid_counts_strategy(attrs, inputs, out_type, target):
 def wrap_compute_nms(topi_compute):
     """wrap nms topi compute"""
     def _compute_nms(attrs, inputs, out_type):
+        max_output_size = inputs[3]
+        if attrs.max_output_size is not None:
+            max_output_size = attrs.max_output_size
         return_indices = bool(get_const_int(attrs.return_indices))
-        max_output_size = get_const_int(attrs.max_output_size)
         iou_threshold = get_const_float(attrs.iou_threshold)
         force_suppress = bool(get_const_int(attrs.force_suppress))
         top_k = get_const_int(attrs.top_k)

python/tvm/relay/op/vision/nms.py

@@ -91,9 +91,9 @@ def non_max_suppression(data,
         second dimension are like the output of arange(num_anchors)
         if get_valid_counts is not used before non_max_suppression.
 
-    max_output_size : int, optional
+    max_output_size : int or relay.Expr, optional
         Max number of output valid boxes for each instance.
-        By default all valid boxes are returned.
+        Return all valid boxes if the value of max_output_size is less than 0.
 
     iou_threshold : float, optional
         Non-maximum suppression threshold.
@@ -124,9 +124,11 @@ def non_max_suppression(data,
     out : relay.Expr or relay.Tuple
         return relay.Expr if return_indices is disabled, a 3-D tensor
         with shape [batch_size, num_anchors, 6] or [batch_size, num_anchors, 5].
-        if return_indices is True, return relay.Tuple of two 2-D tensors, with
+        If return_indices is True, return relay.Tuple of two 2-D tensors, with
         shape [batch_size, num_anchors] and [batch_size, num_valid_anchors] respectively.
     """
+    if isinstance(max_output_size, int):
+        max_output_size = expr.const(max_output_size, "int32")
     out = _make.non_max_suppression(data,
                                     valid_count,
                                     indices,

src/relay/op/vision/nms.cc

@@ -73,7 +73,7 @@ TVM_REGISTER_NODE_TYPE(NonMaximumSuppressionAttrs);
 
 bool NMSRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
             const TypeReporter& reporter) {
-  CHECK_EQ(types.size(), 4);
+  CHECK_EQ(types.size(), 5);
   const auto* data = types[0].as<TensorTypeNode>();
   const auto* valid_count = types[1].as<TensorTypeNode>();
   const NonMaximumSuppressionAttrs* param = attrs.as<NonMaximumSuppressionAttrs>();
@@ -90,18 +90,17 @@ bool NMSRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
     fields.push_back(TensorType(oshape, DataType::Int(32)));
     std::vector<IndexExpr> countshape({dshape[0], 1});
     fields.push_back(TensorType(countshape, DataType::Int(32)));
-    reporter->Assign(types[3], TupleType(Array<Type>(fields)));
+    reporter->Assign(types[4], TupleType(Array<Type>(fields)));
   } else {
-    reporter->Assign(types[3], TensorType(dshape, data->dtype));
+    reporter->Assign(types[4], TensorType(dshape, data->dtype));
   }
   return true;
 }
 
-Expr MakeNMS(Expr data, Expr valid_count, Expr indices, int max_output_size, double iou_threshold,
+Expr MakeNMS(Expr data, Expr valid_count, Expr indices, Expr max_output_size, double iou_threshold,
              bool force_suppress, int top_k, int coord_start, int score_index, int id_index,
              bool return_indices, bool invalid_to_bottom) {
   auto attrs = make_object<NonMaximumSuppressionAttrs>();
-  attrs->max_output_size = max_output_size;
   attrs->iou_threshold = iou_threshold;
   attrs->force_suppress = force_suppress;
   attrs->top_k = top_k;
@@ -111,7 +110,7 @@ Expr MakeNMS(Expr data, Expr valid_count, Expr indices, int max_output_size, dou
   attrs->return_indices = return_indices;
   attrs->invalid_to_bottom = invalid_to_bottom;
   static const Op& op = Op::Get("vision.non_max_suppression");
-  return Call(op, {data, valid_count, indices}, Attrs(attrs), {});
+  return Call(op, {data, valid_count, indices, max_output_size}, Attrs(attrs), {});
 }
 
 TVM_REGISTER_GLOBAL("relay.op.vision._make.non_max_suppression").set_body_typed(MakeNMS);
@@ -122,10 +121,11 @@ be in the format of [class_id, score, left, top, right, bottom]
 or [score, left, top, right, bottom]. Set id_index to be -1 to
 ignore class_id axis.
 )doc" TVM_ADD_FILELINE)
-    .set_num_inputs(3)
+    .set_num_inputs(4)
     .add_argument("data", "Tensor", "Input data.")
     .add_argument("valid_count", "Tensor", "Number of valid anchor boxes.")
     .add_argument("indices", "Tensor", "Corresponding indices in original input tensor.")
+    .add_argument("max_output_size", "Tensor", "Max number of output valid boxes.")
     .set_support_level(5)
     .add_type_rel("NMS", NMSRel);
 

tests/python/frontend/tensorflow/test_forward.py

@@ -2035,22 +2035,24 @@ def test_forward_crop_and_resize():
 def _test_forward_nms_v3(bx_shape, score_shape, iou_threshold, score_threshold, out_size, dtype="float32"):
     boxes = np.random.uniform(0, 10, size=bx_shape).astype(dtype)
     scores = np.random.uniform(size=score_shape).astype(dtype)
+    max_output_size = np.int32(out_size)
     tf.reset_default_graph()
     in_data_1 = tf.placeholder(dtype, boxes.shape, name="in_data_1")
     in_data_2 = tf.placeholder(dtype, scores.shape, name="in_data_2")
-    tf.image.non_max_suppression(boxes=in_data_1, scores=in_data_2,
-                                 max_output_size=out_size, iou_threshold=iou_threshold,
-                                 score_threshold=score_threshold, name="nms")
-    compare_tf_with_tvm([boxes, scores], ['in_data_1:0', 'in_data_2:0'],
+    in_data_3 = tf.placeholder(tf.int32, name="in_data_3")
+    tf.image.non_max_suppression(boxes=in_data_1, scores=in_data_2, max_output_size=in_data_3,
+                                 iou_threshold=iou_threshold, score_threshold=score_threshold, name="nms")
+    compare_tf_with_tvm([boxes, scores, max_output_size], ['in_data_1:0', 'in_data_2:0', 'in_data_3:0'],
                         'nms/NonMaxSuppressionV3:0', mode='vm')
-    compare_tf_with_tvm([boxes, scores], ['in_data_1:0', 'in_data_2:0'],
+    compare_tf_with_tvm([boxes, scores, max_output_size], ['in_data_1:0', 'in_data_2:0', 'in_data_3:0'],
                         'nms/NonMaxSuppressionV3:0', mode='debug')
 
 def test_forward_nms_v3():
     """ NonMaxSuppressionV3 """
     _test_forward_nms_v3((5, 4), (5,), 0.7, 0.5, 5)
     _test_forward_nms_v3((20, 4), (20,), 0.5, 0.6, 10)
     _test_forward_nms_v3((1000, 4), (1000,), 0.3, 0.7, 1000)
+    _test_forward_nms_v3((2000, 4), (2000,), 0.4, 0.6, 7)
 
 
 #######################################################################

tests/python/relay/test_op_level5.py

@@ -283,16 +283,17 @@ def verify_get_valid_counts(dshape, score_threshold, id_index, score_index):
 
 
 def test_non_max_suppression():
-    def verify_nms(x0_data, x1_data, x2_data, dshape, ref_res, ref_indices_res,
-                   iou_threshold=0.5, force_suppress=False, top_k=-1,
-                   check_type_only=False):
+    def verify_nms(x0_data, x1_data, x2_data, x3_data, dshape, ref_res,
+                   ref_indices_res, iou_threshold=0.5, force_suppress=False,
+                   top_k=-1, check_type_only=False):
         x0 = relay.var("x0", relay.ty.TensorType(dshape, "float32"))
         x1 = relay.var("x1", relay.ty.TensorType((dshape[0],), "int32"))
         x2 = relay.var("x2", relay.ty.TensorType((dshape[0], dshape[1]), "int32"))
-        z = relay.vision.non_max_suppression(x0, x1, x2, max_output_size=-1, \
+        x3 = relay.var("x3", relay.ty.TensorType((), "int32"))
+        z = relay.vision.non_max_suppression(x0, x1, x2, x3, \
             iou_threshold=iou_threshold, force_suppress=force_suppress, \
             top_k=top_k, return_indices=False)
-        z_indices = relay.vision.non_max_suppression(x0, x1, x2, max_output_size=-1, \
+        z_indices = relay.vision.non_max_suppression(x0, x1, x2, x3, \
                     iou_threshold=iou_threshold, force_suppress=force_suppress, \
                     top_k=top_k, return_indices=True)
         if isinstance(z_indices, relay.expr.TupleWrapper):
@@ -309,30 +310,30 @@ def verify_nms(x0_data, x1_data, x2_data, dshape, ref_res, ref_indices_res,
         if check_type_only:
             return
 
-        func = relay.Function([x0, x1, x2], z)
+        func = relay.Function([x0, x1, x2, x3], z)
         func = run_infer_type(func)
-        func_indices = relay.Function([x0, x1, x2], z_indices)
+        func_indices = relay.Function([x0, x1, x2, x3], z_indices)
         func_indices = run_infer_type(func_indices)
         for target, ctx in ctx_list():
             intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
-            op_res1 = intrp1.evaluate(func)(x0_data, x1_data, x2_data)
+            op_res1 = intrp1.evaluate(func)(x0_data, x1_data, x2_data, x3_data)
             tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5)
             intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
-            op_res2 = intrp2.evaluate(func)(x0_data, x1_data, x2_data)
+            op_res2 = intrp2.evaluate(func)(x0_data, x1_data, x2_data, x3_data)
             tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-5)
             if target == 'cuda':
                 return
-            op_indices_res1 = intrp1.evaluate(func_indices)(x0_data, x1_data, x2_data)
+            op_indices_res1 = intrp1.evaluate(func_indices)(x0_data, x1_data, x2_data, x3_data)
             tvm.testing.assert_allclose(op_indices_res1[0].asnumpy(), ref_indices_res, rtol=1e-5)
-            op_indices_res2 = intrp2.evaluate(func_indices)(x0_data, x1_data, x2_data)
+            op_indices_res2 = intrp2.evaluate(func_indices)(x0_data, x1_data, x2_data, x3_data)
             tvm.testing.assert_allclose(op_indices_res2[0].asnumpy(), ref_indices_res, rtol=1e-5)
 
     np_data = np.array([[[0, 0.8, 1, 20, 25, 45], [1, 0.7, 30, 60, 50, 80],
                          [0, 0.4, 4, 21, 19, 40], [2, 0.9, 35, 61, 52, 79],
                          [1, 0.5, 100, 60, 70, 110]]]).astype("float32")
     np_valid_count = np.array([4]).astype("int32")
-
     np_indices = np.array([[0, 1, 3, 4, -1]]).astype("int32")
+    np_max_output_size = -1
 
     np_result = np.array([[[2, 0.9, 35, 61, 52, 79], [0, 0.8, 1, 20, 25, 45],
                            [-1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1],
@@ -341,22 +342,23 @@ def verify_nms(x0_data, x1_data, x2_data, dshape, ref_res, ref_indices_res,
     num_anchors = 5
 
     dshape = (te.size_var("n"), num_anchors, 6)
-    verify_nms(np_data, np_valid_count, np_indices, dshape, np_result, np_indices_result,
+    verify_nms(np_data, np_valid_count, np_indices, np_max_output_size, dshape, np_result, np_indices_result,
                force_suppress=True, top_k=2, check_type_only=True)
     dshape = (1, num_anchors, 6)
-    verify_nms(np_data, np_valid_count, np_indices, dshape, np_result, np_indices_result,
+    verify_nms(np_data, np_valid_count, np_indices, np_max_output_size, dshape, np_result, np_indices_result,
                force_suppress=True, top_k=2, check_type_only=False)
 
     np_result = np.array([[[2, 0.9, 35, 61, 52, 79], [0, 0.8, 1, 20, 25, 45],
-                           [1, 0.7, 30, 60, 50, 80], [-1, -1, -1, -1, -1, -1],
+                           [-1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1],
                            [-1, -1, -1, -1, -1, -1]]])
-    np_indices_result = np.array([[4, 0, 1, -1, -1]])
+    np_indices_result = np.array([[4, 0, -1, -1, -1]])
+    np_max_output_size = 2
     dshape = (te.size_var("n"), num_anchors, 6)
-    verify_nms(np_data, np_valid_count, np_indices, dshape, np_result,
+    verify_nms(np_data, np_valid_count, np_indices, np_max_output_size, dshape, np_result,
                np_indices_result, check_type_only=True)
     dshape = (1, num_anchors, 6)
-    verify_nms(np_data, np_valid_count, np_indices, dshape, np_result,
-               np_indices_result, top_k=3)
+    verify_nms(np_data, np_valid_count, np_indices, np_max_output_size, dshape, np_result,
+               np_indices_result, top_k=2)
 
 
 def test_multibox_transform_loc():

topi/python/topi/cuda/nms.py

@@ -458,7 +458,7 @@ def non_max_suppression(data, valid_count, indices, max_output_size=-1,
             in_buffers=[data_buf, sort_tensor_buf, valid_count_buf],
             name="nms",
             tag="nms")
-
+    # TODO(yongwww): Update cuda nms to be consistent with cpu version
     if return_indices:
         return box_indices