fix comments

Author: NHZlX

paddle/fluid/inference/tensorrt/convert/pool2d_op.cc

@@ -30,6 +30,7 @@ class Pool2dOpConverter : public OpConverter {
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs
     auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
+
     std::string pool_type =
         boost::get<std::string>(op_desc.GetAttr("pooling_type"));
     std::vector<int> ksize =
@@ -45,18 +46,18 @@ class Pool2dOpConverter : public OpConverter {
 
     PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
 
-    nvinfer1::PoolingType pool_t = nvinfer1::PoolingType::kMAX;
+    nvinfer1::PoolingType nv_pool_type = nvinfer1::PoolingType::kMAX;
     if (pool_type == "max") {
-      pool_t = nvinfer1::PoolingType::kMAX;
+      nv_pool_type = nvinfer1::PoolingType::kMAX;
     } else if (pool_type == "avg") {
-      pool_t = nvinfer1::PoolingType::kAVERAGE;
+      nv_pool_type = nvinfer1::PoolingType::kAVERAGE;
     } else {
       PADDLE_THROW("TensorRT unsupported pooling type!");
     }
 
     auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
                                        *const_cast<nvinfer1::ITensor*>(input1),
-                                       pool_t, nv_ksize);
+                                       nv_pool_type, nv_ksize);
     PADDLE_ENFORCE_NOT_NULL(layer, "pool layer could not be created.");
     layer->setStride(nv_strides);
     layer->setPadding(nv_paddings);

paddle/fluid/inference/tensorrt/convert/test_activation_op.cc

@@ -23,8 +23,7 @@ namespace tensorrt {
 TEST(ReluOpConverter, main) {
   framework::Scope scope;
   std::unordered_set<std::string> parameters;
-  int runtime_batch = 3;
-  TRTConvertValidation validator(10, parameters, scope, 1000, runtime_batch);
+  TRTConvertValidation validator(10, parameters, scope, 1000);
   validator.DeclInputVar("relu-X", nvinfer1::Dims2(10, 6));
   validator.DeclOutputVar("relu-Out", nvinfer1::Dims2(10, 6));
 
@@ -38,7 +37,7 @@ TEST(ReluOpConverter, main) {
   validator.SetOp(*desc.Proto());
   LOG(INFO) << "execute";
 
-  validator.Execute(runtime_batch);
+  validator.Execute(5);
 }
 
 }  // namespace tensorrt

paddle/fluid/inference/tensorrt/convert/test_fc_op.cc

@@ -23,8 +23,7 @@ namespace tensorrt {
 TEST(fc_op, test) {
   std::unordered_set<std::string> parameters({"mul-Y"});
   framework::Scope scope;
-  int runtime_batch = 2;
-  TRTConvertValidation validator(10, parameters, scope, 1000, runtime_batch);
+  TRTConvertValidation validator(10, parameters, scope, 1000);
   validator.DeclInputVar("mul-X", nvinfer1::Dims3(10, 1, 1));
   validator.DeclParamVar("mul-Y", nvinfer1::Dims2(10, 2));
   validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(1, 2));
@@ -38,7 +37,7 @@ TEST(fc_op, test) {
 
   validator.SetOp(*desc.Proto());
 
-  validator.Execute(runtime_batch);
+  validator.Execute(10);
 }
 
 }  // namespace tensorrt

paddle/fluid/inference/tensorrt/convert/test_mul_op.cc

@@ -23,8 +23,7 @@ namespace tensorrt {
 TEST(MulOpConverter, main) {
   framework::Scope scope;
   std::unordered_set<std::string> parameters;
-  int runtime_batch = 0;
-  TRTConvertValidation validator(10, parameters, scope, 1000, runtime_batch);
+  TRTConvertValidation validator(10, parameters, scope, 1000, false);
   validator.DeclInputVar("mul-X", nvinfer1::Dims2(10, 6));
   validator.DeclInputVar("mul-Y", nvinfer1::Dims2(6, 10));
   validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(10, 10));
@@ -40,7 +39,7 @@ TEST(MulOpConverter, main) {
   validator.SetOp(*desc.Proto());
   LOG(INFO) << "execute";
 
-  validator.Execute(1);
+  validator.Execute(2);
 }
 
 }  // namespace tensorrt

paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc

@@ -23,12 +23,10 @@ namespace tensorrt {
 TEST(Pool2dOpConverter, main) {
   framework::Scope scope;
   std::unordered_set<std::string> parameters;
-  int runtime_batch = 3;
-  TRTConvertValidation validator(5, parameters, scope, 1 << 15, runtime_batch);
+  TRTConvertValidation validator(5, parameters, scope, 1 << 15);
 
-  // We have already set the runtime batchsize, so the
-  // Dims should not contain the batch size.
-  // The ITensor's Dims of input and output should be C * H * W.
+  // The ITensor's Dims should not contain the batch size.
+  // So, the ITensor's Dims of input and output should be C * H * W.
   validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 4, 4));
   validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 2, 2));
 
@@ -52,7 +50,7 @@ TEST(Pool2dOpConverter, main) {
   validator.SetOp(*desc.Proto());
   LOG(INFO) << "execute";
 
-  validator.Execute(runtime_batch);
+  validator.Execute(3);
 }
 
 }  // namespace tensorrt

paddle/fluid/inference/tensorrt/convert/ut_helper.h

@@ -66,10 +66,11 @@ class TRTConvertValidation {
   TRTConvertValidation(int max_batch_size,
                        const std::unordered_set<std::string>& parameters,
                        framework::Scope& scope,  // NOLINT
-                       int workspace_size = 1 << 10, int runtime_batch_size = 1)
+                       int workspace_size = 1 << 10, bool if_add_batch = true)
       : parameters_(parameters),
         scope_(scope),
-        runtime_batch_size_(runtime_batch_size) {
+        if_add_batch_(if_add_batch),
+        max_batch_size_(max_batch_size) {
     // create engine.
     engine_.reset(new TensorRTEngine(max_batch_size, workspace_size, &stream_));
     engine_->InitNetwork();
@@ -102,12 +103,10 @@ class TRTConvertValidation {
     // Init Fluid tensor.
     std::vector<int> dim_vec(dims.d, dims.d + dims.nbDims);
     // There is no batchsize in ITensor's shape, but We should add it to
-    // tensor's
-    // shape of fluid. If the variable is not parameter and the batch size
-    // greater than 0,
-    // add the batchsize to dim_vec.
-    if (is_param != true && runtime_batch_size_ > 0)
-      dim_vec.insert(dim_vec.begin(), runtime_batch_size_);
+    // tensor's shape of fluid. If the variable is not parameter and the
+    // if_add_batch_ flag is true, add the max batchsize to dim_vec.
+    if (is_param != true && if_add_batch_ == true)
+      dim_vec.insert(dim_vec.begin(), max_batch_size_);
     auto* x = scope_.Var(name);
     auto* x_tensor = x->GetMutable<framework::LoDTensor>();
     x_tensor->Resize(framework::make_ddim(dim_vec));
@@ -141,6 +140,7 @@ class TRTConvertValidation {
 
   void Execute(int batch_size) {
     // Execute Fluid Op
+    PADDLE_ENFORCE_LE(batch_size, max_batch_size_);
     platform::CPUPlace place;
     platform::CPUDeviceContext ctx(place);
     op_->Run(scope_, place);
@@ -159,9 +159,14 @@ class TRTConvertValidation {
       auto* var = scope_.FindVar(output);
       auto tensor = var->GetMutable<framework::LoDTensor>();
       framework::TensorToVector(*tensor, ctx, &fluid_out);
+
+      size_t fluid_out_size = fluid_out.size();
+      if (if_add_batch_ == true) {
+        fluid_out_size = batch_size * (tensor->dims().size() / max_batch_size_);
+      }
       // Compare two output
       ASSERT_FALSE(fluid_out.empty());
-      for (size_t i = 0; i < fluid_out.size(); i++) {
+      for (size_t i = 0; i < fluid_out_size; i++) {
         // Loose the threshold for CI in different machine model.
         EXPECT_LT(std::abs(fluid_out[i] - trt_out[i]), 2e-5);
       }
@@ -177,10 +182,12 @@ class TRTConvertValidation {
   std::unique_ptr<framework::OpDesc> op_desc_;
   const std::unordered_set<std::string>& parameters_;
   framework::Scope& scope_;
-  //  It represents the runtime batchsize when we test.
-  //  If the value greater than 0, we add this to
-  //  the first dimension of tensor's shape of fluid.
-  int runtime_batch_size_;
+  // The ITensor of trt does not cotain the batch size,
+  // bug, in most cases, we need to set batch size for
+  // fluid's tensor shape. This variable indicates
+  // whether to add batch size to tensor shape of fluid.
+  bool if_add_batch_;
+  int max_batch_size_;
 };
 
 }  // namespace tensorrt