change pool to pool2d to fix the bug of pooling unit test, and refine code

test=develop

paddle/fluid/lite/api/cxx_api_bin.cc

@@ -13,11 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/lite/api/cxx_api.h"
-
-// #ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
 #include "paddle/fluid/lite/core/mir/passes.h"
-// #endif
-
 #include "paddle/fluid/lite/core/op_registry.h"
 
 namespace paddle {

paddle/fluid/lite/core/mir/passes.h

@@ -21,6 +21,7 @@ namespace mir {}  // namespace mir
 }  // namespace lite
 }  // namespace paddle
 
+#ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
 USE_MIR_PASS(demo);
 USE_MIR_PASS(static_kernel_pick_pass);
 USE_MIR_PASS(variable_place_inference_pass);
@@ -28,4 +29,5 @@ USE_MIR_PASS(type_target_transform_pass);
 USE_MIR_PASS(generate_program_pass);
 USE_MIR_PASS(io_copy_kernel_pick_pass);
 USE_MIR_PASS(argument_type_display_pass);
+#endif
 USE_MIR_PASS(runtime_context_assign_pass);

paddle/fluid/lite/core/optimizer.h

@@ -46,18 +46,19 @@ class Optimizer {
     SpecifyKernelPickTactic(kernel_pick_factor);
     InitTargetTypeTransformPass();
 
-    // #ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
     if (passes.empty()) {
       RunPasses(std::vector<std::string>{{
-          // "static_kernel_pick_pass",        //
-          // "variable_place_inference_pass",  //
-          // "argument_type_display_pass",     //
-          // "type_target_transform_pass",     //
-          // "argument_type_display_pass",     //
-          // "variable_place_inference_pass",  //
-          // "argument_type_display_pass",     //
-          // "io_copy_kernel_pick_pass",       //
-          // "variable_place_inference_pass",  //
+#ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
+          "static_kernel_pick_pass",        //
+          "variable_place_inference_pass",  //
+          "argument_type_display_pass",     //
+          "type_target_transform_pass",     //
+          "argument_type_display_pass",     //
+          "variable_place_inference_pass",  //
+          "argument_type_display_pass",     //
+          "io_copy_kernel_pick_pass",       //
+          "variable_place_inference_pass",  //
+#endif
           "runtime_context_assign_pass",  //
       }});
     } else {

paddle/fluid/lite/operators/batch_norm_op.cc

@@ -82,8 +82,7 @@ bool BatchNormOp::AttachImpl(const cpp::OpDesc &op_desc, lite::Scope *scope) {
   param_.variance =
       scope->FindVar(op_desc.Input("Variance").front())->GetMutable<Tensor>();
   param_.y = scope->FindVar(op_desc.Output("Y").front())->GetMutable<Tensor>();
-  param_.is_test = true;  // TODO(hong19860320) param_.is_test =
-                          // op_desc.GetAttr<int>("is_test");
+  param_.is_test = op_desc.GetAttr<int>("is_test");
   param_.use_global_stats = op_desc.GetAttr<bool>("use_global_stats");
   if (!param_.is_test) {
     param_.mean_out =

paddle/fluid/lite/operators/batch_norm_op_test.cc

@@ -46,7 +46,7 @@ TEST(batch_norm_op_lite, test) {
   desc.SetInput("Mean", {"mean"});
   desc.SetInput("Variance", {"variance"});
   desc.SetOutput("Y", {"y"});
-  desc.SetAttr("is_test", true);
+  desc.SetAttr("is_test", static_cast<int>(1));
   desc.SetAttr("use_global_stats", false);
   desc.SetAttr("epsilon", 1e-5f);
   desc.SetAttr("momentum", 0.9f);
@@ -67,72 +67,72 @@ TEST(batch_norm_op_lite, test) {
   }
 }
 
-// TEST(batch_norm_op_lite, test_enable_is_test) {
-//   // prepare variables
-//   Scope scope;
-//   auto* x = scope.Var("x")->GetMutable<Tensor>();
-//   auto* scale = scope.Var("scale")->GetMutable<Tensor>();
-//   auto* bias = scope.Var("bias")->GetMutable<Tensor>();
-//   auto* mean = scope.Var("mean")->GetMutable<Tensor>();
-//   auto* variance = scope.Var("variance")->GetMutable<Tensor>();
-//   auto* y = scope.Var("y")->GetMutable<Tensor>();
-//   auto* mean_out = scope.Var("mean_out")->GetMutable<Tensor>();
-//   auto* variance_out = scope.Var("variance_out")->GetMutable<Tensor>();
-//   auto* saved_mean = scope.Var("saved_mean")->GetMutable<Tensor>();
-//   auto* saved_variance = scope.Var("saved_variance")->GetMutable<Tensor>();
-//   x->Resize({2, 32, 10, 20});
-//   auto x_dims = x->dims();
-//   const int64_t channel_size = x_dims[1];  // NCHW
-//   scale->Resize({channel_size});
-//   bias->Resize({channel_size});
-//   mean->Resize({channel_size});
-//   variance->Resize({channel_size});
+TEST(batch_norm_op_lite, test_enable_is_test) {
+  // prepare variables
+  Scope scope;
+  auto* x = scope.Var("x")->GetMutable<Tensor>();
+  auto* scale = scope.Var("scale")->GetMutable<Tensor>();
+  auto* bias = scope.Var("bias")->GetMutable<Tensor>();
+  auto* mean = scope.Var("mean")->GetMutable<Tensor>();
+  auto* variance = scope.Var("variance")->GetMutable<Tensor>();
+  auto* y = scope.Var("y")->GetMutable<Tensor>();
+  auto* mean_out = scope.Var("mean_out")->GetMutable<Tensor>();
+  auto* variance_out = scope.Var("variance_out")->GetMutable<Tensor>();
+  auto* saved_mean = scope.Var("saved_mean")->GetMutable<Tensor>();
+  auto* saved_variance = scope.Var("saved_variance")->GetMutable<Tensor>();
+  x->Resize({2, 32, 10, 20});
+  auto x_dims = x->dims();
+  const int64_t channel_size = x_dims[1];  // NCHW
+  scale->Resize({channel_size});
+  bias->Resize({channel_size});
+  mean->Resize({channel_size});
+  variance->Resize({channel_size});
 
-//   // prepare op desc
-//   cpp::OpDesc desc;
-//   desc.SetType("batch_norm");
-//   desc.SetInput("X", {"x"});
-//   desc.SetInput("Scale", {"scale"});
-//   desc.SetInput("Bias", {"bias"});
-//   desc.SetInput("Mean", {"mean"});
-//   desc.SetInput("Variance", {"variance"});
-//   desc.SetOutput("Y", {"y"});
-//   desc.SetOutput("MeanOut", {"mean_out"});
-//   desc.SetOutput("VarianceOut", {"variance_out"});
-//   desc.SetOutput("SavedMean", {"saved_mean"});
-//   desc.SetOutput("SavedVariance", {"saved_variance"});
-//   desc.SetAttr("is_test", false);
-//   desc.SetAttr("use_global_stats", false);
-//   desc.SetAttr("epsilon", 1e-5f);
-//   desc.SetAttr("momentum", 0.9f);
-//   desc.SetAttr("data_layout", std::string("NCHW"));
+  // prepare op desc
+  cpp::OpDesc desc;
+  desc.SetType("batch_norm");
+  desc.SetInput("X", {"x"});
+  desc.SetInput("Scale", {"scale"});
+  desc.SetInput("Bias", {"bias"});
+  desc.SetInput("Mean", {"mean"});
+  desc.SetInput("Variance", {"variance"});
+  desc.SetOutput("Y", {"y"});
+  desc.SetOutput("MeanOut", {"mean_out"});
+  desc.SetOutput("VarianceOut", {"variance_out"});
+  desc.SetOutput("SavedMean", {"saved_mean"});
+  desc.SetOutput("SavedVariance", {"saved_variance"});
+  desc.SetAttr("is_test", static_cast<int>(0));
+  desc.SetAttr("use_global_stats", false);
+  desc.SetAttr("epsilon", 1e-5f);
+  desc.SetAttr("momentum", 0.9f);
+  desc.SetAttr("data_layout", std::string("NCHW"));
 
-//   BatchNormOp batch_norm("batch_norm");
+  BatchNormOp batch_norm("batch_norm");
 
-//   batch_norm.SetValidPlaces({Place{TARGET(kHost), PRECISION(kFloat)}});
-//   batch_norm.Attach(desc, &scope);
-//   batch_norm.CheckShape();
-//   batch_norm.InferShape();
+  batch_norm.SetValidPlaces({Place{TARGET(kHost), PRECISION(kFloat)}});
+  batch_norm.Attach(desc, &scope);
+  batch_norm.CheckShape();
+  batch_norm.InferShape();
 
-//   // check output dims
-//   auto y_dims = y->dims();
-//   CHECK_EQ(y_dims.size(), x_dims.size());
-//   for (size_t i = 0; i < y_dims.size(); i++) {
-//     CHECK_EQ(y_dims[i], x_dims[i]);
-//   }
-//   auto mean_out_dims = mean_out->dims();
-//   auto variance_out_dims = variance_out->dims();
-//   auto saved_mean_dims = saved_mean->dims();
-//   auto saved_variance_dims = saved_variance->dims();
-//   CHECK_EQ(mean_out_dims.size(), 1UL);
-//   CHECK_EQ(variance_out_dims.size(), 1UL);
-//   CHECK_EQ(saved_mean_dims.size(), 1UL);
-//   CHECK_EQ(saved_variance_dims.size(), 1UL);
-//   CHECK_EQ(mean_out_dims[0], channel_size);
-//   CHECK_EQ(variance_out_dims[0], channel_size);
-//   CHECK_EQ(saved_mean_dims[0], channel_size);
-//   CHECK_EQ(saved_variance_dims[0], channel_size);
-// }
+  // check output dims
+  auto y_dims = y->dims();
+  CHECK_EQ(y_dims.size(), x_dims.size());
+  for (size_t i = 0; i < y_dims.size(); i++) {
+    CHECK_EQ(y_dims[i], x_dims[i]);
+  }
+  auto mean_out_dims = mean_out->dims();
+  auto variance_out_dims = variance_out->dims();
+  auto saved_mean_dims = saved_mean->dims();
+  auto saved_variance_dims = saved_variance->dims();
+  CHECK_EQ(mean_out_dims.size(), 1UL);
+  CHECK_EQ(variance_out_dims.size(), 1UL);
+  CHECK_EQ(saved_mean_dims.size(), 1UL);
+  CHECK_EQ(saved_variance_dims.size(), 1UL);
+  CHECK_EQ(mean_out_dims[0], channel_size);
+  CHECK_EQ(variance_out_dims[0], channel_size);
+  CHECK_EQ(saved_mean_dims[0], channel_size);
+  CHECK_EQ(saved_variance_dims[0], channel_size);
+}
 
 }  // namespace operators
 }  // namespace lite

paddle/fluid/lite/operators/pool_op.h

@@ -71,7 +71,7 @@ class PoolOpLite : public OpLite {
 
   void AttachKernel(KernelBase *kernel) override { kernel->SetParam(param_); }
 
-  std::string DebugString() const override { return "pool"; }
+  std::string DebugString() const override { return "pool2d"; }
 
  private:
   mutable PoolParam param_;

paddle/fluid/lite/operators/pool_op_test.cc

@@ -38,7 +38,7 @@ TEST(pool_op_lite, test) {
 
   // prepare op desc
   cpp::OpDesc desc;
-  desc.SetType("pool");
+  desc.SetType("pool2d");
   desc.SetInput("X", {"x"});
   desc.SetOutput("Out", {"output"});