adaptive pool2d pass fix (#39600)

* first commit

* teller fix

* bug fix

* enable for pool2d only

* fix global_pooling issue

* pooling_type

* fix test

paddle/fluid/framework/ir/adaptive_pool2d_convert_global_pass.cc

@@ -72,7 +72,18 @@ void AdaptivePool2dConvertGlobalPass::ApplyImpl(ir::Graph* graph) const {
   for (const Node* n : graph->Nodes()) {
     if (n->IsOp()) {
       auto* op = n->Op();
-      if (op->HasAttr("adaptive") && op->HasAttr("ksize")) {
+      if (op->Type() == "pool2d" && op->HasAttr("adaptive") &&
+          op->HasAttr("ksize")) {
+        if (op->HasAttr("global_pooling")) {
+          bool global_pooling =
+              BOOST_GET_CONST(bool, op->GetAttr("global_pooling"));
+          if (global_pooling) return;
+        }
+        if (!op->HasAttr("pooling_type")) return;
+        std::string type =
+            BOOST_GET_CONST(std::string, op->GetAttr("pooling_type"));
+        // adaptive has no effect on max pooling
+        if (type == "max") return;
         bool adaptive = BOOST_GET_CONST(bool, op->GetAttr("adaptive"));
         std::vector<int> ksize =
             BOOST_GET_CONST(std::vector<int>, op->GetAttr("ksize"));

paddle/fluid/framework/ir/adaptive_pool2d_convert_global_pass_tester.cc

@@ -29,6 +29,8 @@ TEST(AdaptivePool2dConvertGlobalPass, basic) {
   AttributeMap attrs;
   attrs["adaptive"] = true;
   attrs["ksize"] = std::vector<int>{1, 1};
+  attrs["pooling_type"] =
+      std::string("avg");  // adaptive has no effect on max pooling
   layers.pool2d(x, false, &attrs);
 
   std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -225,6 +225,13 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
                 << desc.Output("Out").size();
         return false;
       }
+      if (desc.HasAttr("data_format")) {
+        std::string data_format =
+            BOOST_GET_CONST(std::string, desc.GetAttr("data_format"));
+        if (data_format == "NHWC" || data_format == "NDHWC") {
+          return false;
+        }
+      }
       if (!desc.HasAttr("pooling_type")) {
         return false;
       } else {

python/paddle/fluid/tests/unittests/ir/inference/test_adaptive_pool2d_convert_global_pass_autoscan.py

@@ -42,10 +42,14 @@ class TestAdaptivePool2dConvertGlobalPass(PassAutoScanTest):
                 st.integers(
                     min_value=1, max_value=4), min_size=2, max_size=2))
 
-        paddings = [0, 0]  # only 0 0 is right
+        paddings = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=4), min_size=2, max_size=2))
+
         ceil_mode = draw(st.booleans())
         exclusive = draw(st.booleans())
-        global_pooling = False  #only false is right
+        global_pooling = draw(st.booleans())
         padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VAILD"]))
 
         pool_op = OpConfig(
@@ -83,29 +87,6 @@ class TestAdaptivePool2dConvertGlobalPass(PassAutoScanTest):
             use_calib_mode=False)
         yield config, ['pool2d'], (1e-5, 1e-5)
 
-    def add_ignore_pass_case(self):
-        # Here we put some skip rules to avoid known bugs
-        def teller1(program_config, predictor_config):
-            if program_config.ops[0].attrs["pooling_type"] == "max":
-                x_shape = list(program_config.inputs["input_data"].shape)
-                if x_shape[-1] != 1 or x_shape[-2] != 1:
-                    return True
-            return False
-
-        def teller2(program_config, predictor_config):
-            if program_config.ops[0].attrs["padding_algorithm"] == "SAME":
-                return True
-            return False
-
-        self.add_ignore_check_case(
-            teller1,
-            IgnoreReasons.PASS_ACCURACY_ERROR,
-            "max pooling has diff if H or W is not equals to 1", )
-        self.add_ignore_check_case(
-            teller2,
-            IgnoreReasons.PASS_ACCURACY_ERROR,
-            "output has wrong result if padding_algorithm equals to SAME", )
-
     def test(self):
         self.run_and_statis(
             quant=False,