adaptive pool2d pass fix (#39600)

* first commit * teller fix * bug fix * enable for pool2d only * fix global_pooling issue * pooling_type * fix test

Author: b3602sss

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 @@ def sample_program_config(self, draw):
  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 @@ def sample_predictor_configs(self, program_config):
  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,