modify the tensorrt engine op to adapt to chage

paddle/fluid/operators/tensorrt_engine_op.cc

@@ -53,13 +53,14 @@ nvinfer1::Dims Vec2TRT_Dims(const std::vector<int64_t> &shape) {
   PADDLE_ENFORCE_LE(shape.size(), 4UL,
                     "TensorRT' tensor input requires at most 4 dimensions");
 
+  // We should delete the batch size here.
   switch (shape.size()) {
     case 2:
-      return nvinfer1::Dims2(shape[0], shape[1]);
+      return nvinfer1::Dims2(1, shape[1]);
     case 3:
-      return nvinfer1::Dims3(shape[0], shape[1], shape[2]);
+      return nvinfer1::Dims3(1, shape[1], shape[2]);
     case 4:
-      return nvinfer1::Dims4(shape[0], shape[1], shape[2], shape[3]);
+      return nvinfer1::Dims4(1, shape[1], shape[2], shape[3]);
     default:
       return nvinfer1::Dims();
   }

paddle/fluid/operators/tensorrt_engine_op.h

@@ -95,16 +95,14 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
       PADDLE_ENFORCE_NOT_NULL(fluid_v, "no output variable called %s", y);
       auto* fluid_t = fluid_v->GetMutable<framework::LoDTensor>();
       fluid_t->Resize(framework::make_ddim(ddim));
-      auto size = inference::analysis::AccuDims(dims.d, dims.nbDims);
+
       if (platform::is_cpu_place(fluid_t->place())) {
         // TODO(Superjomn) change this float to dtype size.
         engine->GetOutputInCPU(
-            y, fluid_t->mutable_data<float>(platform::CPUPlace()),
-            size * sizeof(float));
+            y, fluid_t->mutable_data<float>(platform::CPUPlace()));
       } else {
         engine->GetOutputInGPU(
-            y, fluid_t->mutable_data<float>(platform::CUDAPlace()),
-            size * sizeof(float));
+            y, fluid_t->mutable_data<float>(platform::CUDAPlace()));
       }
     }
 

paddle/fluid/operators/tensorrt_engine_op_test.cc

@@ -64,36 +64,37 @@ TEST(TensorRTEngineOp, manual) {
 
   LOG(INFO) << "create block desc";
   framework::BlockDesc block_desc(&program, block_);
-  LOG(INFO) << "create mul op";
-  auto* mul = block_desc.AppendOp();
-  mul->SetType("mul");
-  mul->SetInput("X", std::vector<std::string>({"x"}));     // 2 x 4
-  mul->SetInput("Y", std::vector<std::string>({"y"}));     // 4 x 6
-  mul->SetOutput("Out", std::vector<std::string>({"z"}));  // 2 x 6
+  LOG(INFO) << "create fc op";
+  auto* fc0 = block_desc.AppendOp();
+  fc0->SetType("mul");
+  fc0->SetInput("X", std::vector<std::string>({"x"}));     // 4 x 1 x 1
+  fc0->SetInput("Y", std::vector<std::string>({"y"}));     // 4 x 6
+  fc0->SetOutput("Out", std::vector<std::string>({"z"}));  // 6 x 1 x 1
 
   LOG(INFO) << "create fc op";
-  auto* fc = block_desc.AppendOp();
-  fc->SetType("mul");
-  fc->SetInput("X", std::vector<std::string>({"z"}));
-  fc->SetInput("Y", std::vector<std::string>({"y0"}));     // 6 x 8
-  fc->SetOutput("Out", std::vector<std::string>({"z0"}));  // 2 x 8
+  auto* fc1 = block_desc.AppendOp();
+  fc1->SetType("mul");
+  fc1->SetInput("X", std::vector<std::string>({"z"}));
+  fc1->SetInput("Y", std::vector<std::string>({"y0"}));     // 6 x 8
+  fc1->SetOutput("Out", std::vector<std::string>({"z0"}));  // 8 x 1 x 1
 
   // Set inputs' variable shape in BlockDesc
-  AddTensorToBlockDesc(block_, "x", std::vector<int64_t>({2, 4}));
+  // the batch size is 2, so the dims of 'x' is {2, 4, 1, 1}
+  AddTensorToBlockDesc(block_, "x", std::vector<int64_t>({2, 4, 1, 1}));
   AddTensorToBlockDesc(block_, "y", std::vector<int64_t>({4, 6}));
   AddTensorToBlockDesc(block_, "y0", std::vector<int64_t>({6, 8}));
   AddTensorToBlockDesc(block_, "z", std::vector<int64_t>({2, 6}));
 
   // It is wired, need to copy manually.
-  *block_->add_ops() = *mul->Proto();
-  *block_->add_ops() = *fc->Proto();
+  *block_->add_ops() = *fc0->Proto();
+  *block_->add_ops() = *fc1->Proto();
 
   ASSERT_EQ(block_->ops_size(), 2);
 
   LOG(INFO) << "create tensorrt desc";
   framework::OpDesc engine_op_desc(nullptr);
   engine_op_desc.SetType("tensorrt_engine");
-  engine_op_desc.SetInput("Xs", std::vector<std::string>({"x", "y", "y0"}));
+  engine_op_desc.SetInput("Xs", std::vector<std::string>({"x"}));
   engine_op_desc.SetOutput("Ys", std::vector<std::string>({"z0"}));
   SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
                        block_->SerializeAsString());
@@ -208,4 +209,3 @@ TEST(TensorRTEngineOp, fc) { Execute(40, 28, 28); }
 }  // namespace paddle
 
 USE_TRT_CONVERTER(mul)
-USE_TRT_CONVERTER(fc)