add conv2d trt converter

paddle/fluid/inference/tensorrt/convert/CMakeLists.txt

@@ -13,3 +13,6 @@ nv_test(test_trt_fc_op SRCS test_fc_op.cc fc_op.cc
         DEPS ${FLUID_CORE_MODULES} tensorrt_engine mul_op SERIAL)
 nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc
         DEPS ${FLUID_CORE_MODULES} tensorrt_engine activation_op SERIAL)
+
+nv_test(test_trt_conv_op SRCS test_conv2d_op.cc conv2d_op.cc
+        DEPS ${FLUID_CORE_MODULES} tensorrt_engine conv_op SERIAL)

paddle/fluid/inference/tensorrt/convert/conv2d_op.cc

@@ -20,11 +20,59 @@ namespace tensorrt {
 
 class Conv2dOpConverter : public OpConverter {
  public:
-  Conv2dOpConverter() {}
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
     LOG(INFO)
         << "convert a fluid conv2d op to tensorrt conv layer without bias";
+
+    framework::OpDesc op_desc(op, nullptr);
+    PADDLE_ENFORCE_EQ(op_desc.Input("Input").size(), 1);
+    PADDLE_ENFORCE_EQ(op_desc.Input("Filter").size(), 1);  // Y is a weight
+    PADDLE_ENFORCE_EQ(op_desc.Output("Output").size(), 1);
+
+    auto* X = engine_->GetITensor(op_desc.Input("Input").front());
+    // Declare weights
+    auto* Y_v = scope.FindVar(op_desc.Input("Filter").front());
+    PADDLE_ENFORCE_NOT_NULL(Y_v);
+    auto* Y_t = Y_v->GetMutable<framework::LoDTensor>();
+    auto* weight_data = Y_t->mutable_data<float>(platform::CPUPlace());
+
+    const int n_output = Y_t->dims()[0];
+    const int filter_h = Y_t->dims()[2];
+    const int filter_w = Y_t->dims()[3];
+
+    const int groups = boost::get<int>(op_desc.GetAttr("groups"));
+    const std::vector<int> dilations =
+        boost::get<std::vector<int>>(op_desc.GetAttr("dilations"));
+    const std::vector<int> strides =
+        boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
+    const std::vector<int> paddings =
+        boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
+
+    nvinfer1::DimsHW nv_ksize(filter_h, filter_w);
+    nvinfer1::DimsHW nv_dilations(dilations[0], dilations[1]);
+    nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
+    nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
+
+    TensorRTEngine::Weight weight{nvinfer1::DataType::kFLOAT,
+                                  static_cast<void*>(weight_data),
+                                  Y_t->memory_size() / sizeof(float)};
+
+    TensorRTEngine::Weight bias{nvinfer1::DataType::kFLOAT, nullptr, 0};
+    auto* layer = TRT_ENGINE_ADD_LAYER(
+        engine_, Convolution, *const_cast<nvinfer1::ITensor*>(X), n_output,
+        nv_ksize, weight.get(), bias.get());
+    PADDLE_ENFORCE(layer != nullptr);
+    layer->setStride(nv_strides);
+    layer->setPadding(nv_paddings);
+    layer->setDilation(nv_dilations);
+    layer->setNbGroups(groups);
+
+    auto output_name = op_desc.Output("Output").front();
+    engine_->SetITensor(output_name, layer->getOutput(0));
+    if (test_mode) {
+      engine_->DeclareOutput(output_name);
+    }
   }
 };
 

paddle/fluid/inference/tensorrt/convert/test_conv2d_op.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <gtest/gtest.h>
+#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+TEST(conv2d_op, test) {
+  std::unordered_set<std::string> parameters({"conv2d-Y"});
+  framework::Scope scope;
+  TRTConvertValidation validator(2, parameters, scope, 1 << 15);
+
+  validator.DeclInputVar("conv2d-X", nvinfer1::Dims4(1, 2, 5, 5));
+  validator.DeclParamVar("conv2d-Y", nvinfer1::Dims4(3, 2, 3, 3));
+  validator.DeclOutputVar("conv2d-Out", nvinfer1::Dims4(1, 3, 5, 5));
+
+  // Prepare Op description
+  framework::OpDesc desc;
+  desc.SetType("conv2d");
+  desc.SetInput("Input", {"conv2d-X"});
+  desc.SetInput("Filter", {"conv2d-Y"});
+  desc.SetOutput("Output", {"conv2d-Out"});
+
+  const std::vector<int> strides({1, 1});
+  const std::vector<int> paddings({1, 1});
+  const std::vector<int> dilations({1, 1});
+  const int groups = 1;
+
+  desc.SetAttr("strides", strides);
+  desc.SetAttr("paddings", paddings);
+  desc.SetAttr("dilations", dilations);
+  desc.SetAttr("groups", groups);
+
+  validator.SetOp(*desc.Proto());
+
+  validator.Execute(1);
+}
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+USE_OP(conv2d);