[PT FE] Torchvision NMS can accept negative scores (#19826)

src/frontends/pytorch/src/op/nms.cpp

@@ -34,8 +34,10 @@ OutputVector translate_nms(const NodeContext& context) {
         context.mark_node(v0::Constant::create(element::i32, Shape{1}, {std::numeric_limits<int32_t>::max()}));
     auto iou_threshold = context.get_input(2);
 
-    auto nms_out =
-        context.mark_node(std::make_shared<v9::NonMaxSuppression>(boxes, scores, max_output_per_class, iou_threshold));
+    auto score_threshold =
+        context.mark_node(v0::Constant::create(element::f32, Shape{}, {std::numeric_limits<float>::lowest()}));
+    auto nms_out = context.mark_node(
+        std::make_shared<v9::NonMaxSuppression>(boxes, scores, max_output_per_class, iou_threshold, score_threshold));
     auto select = context.mark_node(std::make_shared<v8::Gather>(nms_out, const_2, const_1));
 
     return {context.mark_node(std::make_shared<v0::Squeeze>(select, const_1))};

tests/layer_tests/pytorch_tests/test_nms.py

@@ -15,7 +15,8 @@ class TestNms(PytorchLayerTest):
         # PyTorch requires that boxes are in (x1, y1, x2, y2) format, where 0<=x1<x2 and 0<=y1<y2
         boxes = np.array([[np.random.uniform(1, 3), np.random.uniform(2, 6),
                            np.random.uniform(4, 6), np.random.uniform(7, 9)] for _ in range(self.boxes_num)]).astype(np.float32)
-        scores = np.abs(np.random.randn(self.boxes_num).astype(np.float32))
+        # scores can be negative
+        scores = np.random.randn(self.boxes_num).astype(np.float32)
         return (boxes, scores)
 
     def create_model(self):