diff --git a/paddle/fluid/inference/tensorrt/convert/pool2d_op.cc b/paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
index c031630f36d789bb205c23ae7a899089761f5391..303130e74f512abe041fd14a8bcbaea9400fa902 100644
--- a/paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
+++ b/paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
@@ -88,6 +88,9 @@ class Pool2dOpConverter : public OpConverter {
         BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("strides"));
     std::vector<int> paddings =
         BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("paddings"));
+    bool exclusive = op_desc.HasAttr("exclusive")
+                         ? BOOST_GET_CONST(bool, op_desc.GetAttr("exclusive"))
+                         : true;
     bool ceil_mode = BOOST_GET_CONST(bool, op_desc.GetAttr("ceil_mode"));
     bool adaptive = false;
     if (op_desc.HasAttr("adaptive"))
@@ -166,7 +169,7 @@ class Pool2dOpConverter : public OpConverter {
       return;
     }
 
-    if (!adaptive && pool_type == "max") {
+    if (!adaptive) {
       // Under ceil mode, the pre_pad and post_pad are used to
       // record the the padding size. In some ceil mode cases,
       // we do not need padding, so we initialize the two vars to 0.
@@ -194,6 +197,7 @@ class Pool2dOpConverter : public OpConverter {
                           "trt pool layer in converter could not be created."));
       pool_layer->setStride(nv_strides);
       pool_layer->setPadding(nv_paddings);
+      pool_layer->setAverageCountExcludesPadding(exclusive);
       layer = pool_layer;
     } else {
       // Average pooling needs to exclude the padding pixels from the average
@@ -213,7 +217,6 @@ class Pool2dOpConverter : public OpConverter {
               "trt pool plugin layer in converter could not be created."));
       layer = pool_layer;
     }
-
     auto output_name = op_desc.Output("Out")[0];
     RreplenishLayerAndOutput(layer, "pool2d", {output_name}, test_mode);
   }