paddlepaddle--paddle/paddle/fluid/inference/tensorrt/convert/pool3d_op.cc

/* Copyright (c) 2021 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 "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/plugin/pool3d_op_plugin.h"

namespace paddle::inference::tensorrt {

inline void DealCeilMode(const nvinfer1::Dims &input_shape,
                         std::vector<int> ksize,
                         std::vector<int> strides,
                         std::vector<int> paddings,
                         nvinfer1::Dims3 *pre_pad,
                         nvinfer1::Dims3 *post_pad,
                         int input_dims) {
  int input_depth = input_shape.d[input_dims - 3];
  int input_height = input_shape.d[input_dims - 2];
  int input_width = input_shape.d[input_dims - 1];

  int floor_d_output_size =
      (input_depth - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
  int ceil_d_output_size =
      (input_depth - ksize[0] + 2 * paddings[0] + strides[0] - 1) / strides[0] +
      1;

  int floor_h_output_size =
      (input_height - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
  int ceil_h_output_size =
      (input_height - ksize[1] + 2 * paddings[1] + strides[1] - 1) /
          strides[1] +
      1;

  int floor_w_output_size =
      (input_width - ksize[2] + 2 * paddings[2]) / strides[2] + 1;
  int ceil_w_output_size =
      (input_width - ksize[2] + 2 * paddings[2] + strides[2] - 1) / strides[2] +
      1;

  if (floor_d_output_size != ceil_d_output_size) {
    post_pad->d[0] = strides[0] - 1;
  }

  if (floor_h_output_size != ceil_h_output_size) {
    post_pad->d[1] = strides[1] - 1;
  }

  if (floor_w_output_size != ceil_w_output_size) {
    post_pad->d[2] = strides[2] - 1;
  }
}

class Pool3dOpConverter : public OpConverter {
 public:
  void operator()(const framework::proto::OpDesc &op,
                  const framework::Scope &scope,
                  bool test_mode) override {
    VLOG(3) << "convert a pool3d op to tensorrt pool3d layer without bias";
    framework::OpDesc op_desc(op, nullptr);
    auto *input1 = engine_->GetITensor(op_desc.Input("X")[0]);

    bool global_pooling =
        PADDLE_GET_CONST(bool, op_desc.GetAttr("global_pooling"));
    std::string pool_type =
        PADDLE_GET_CONST(std::string, op_desc.GetAttr("pooling_type"));
    std::vector<int> ksize =
        PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("ksize"));
    std::vector<int> strides =
        PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("strides"));
    std::vector<int> paddings =
        PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("paddings"));
    bool exclusive = op_desc.HasAttr("exclusive")
                         ? PADDLE_GET_CONST(bool, op_desc.GetAttr("exclusive"))
                         : true;
    bool ceil_mode = PADDLE_GET_CONST(bool, op_desc.GetAttr("ceil_mode"));
    bool adaptive = false;
    if (op_desc.HasAttr("adaptive"))
      adaptive = PADDLE_GET_CONST(bool, op_desc.GetAttr("adaptive"));
    std::string padding_algorithm = "EXPLICIT";
    if (op_desc.HasAttr("padding_algorithm"))
      padding_algorithm =
          PADDLE_GET_CONST(std::string, op_desc.GetAttr("padding_algorithm"));
    if (padding_algorithm == "VALID" || padding_algorithm == "SAME") {
      std::fill(paddings.begin(), paddings.end(), 0);
    }

    nvinfer1::PoolingType nv_pool_type = nvinfer1::PoolingType::kMAX;
    nvinfer1::ReduceOperation reduce_operation =
        nvinfer1::ReduceOperation::kMAX;
    if (pool_type == "max") {
      nv_pool_type = nvinfer1::PoolingType::kMAX;
      reduce_operation = nvinfer1::ReduceOperation::kMAX;
    } else if (pool_type == "avg") {
      nv_pool_type = nvinfer1::PoolingType::kAVERAGE;
      reduce_operation = nvinfer1::ReduceOperation::kAVG;
    }
    nvinfer1::Dims3 nv_ksize(ksize[0], ksize[1], ksize[2]);
    nvinfer1::Dims3 nv_strides(strides[0], strides[1], strides[2]);
    nvinfer1::Dims3 nv_paddings(paddings[0], paddings[1], paddings[2]);
    nvinfer1::ILayer *layer = nullptr;
    if (op_desc.HasAttr("enable_int8")) {
      PADDLE_ENFORCE_EQ(op_desc.HasAttr("Input_scale"),
                        true,
                        common::errors::InvalidArgument(
                            "Expected attribute 'Input_scale' to be "
                            "present when 'enable_int8' is set."));

      float input_scale =
          PADDLE_GET_CONST(float, op_desc.GetAttr("Input_scale"));
      engine_->SetTensorDynamicRange(input1, input_scale);
    }

    if (!adaptive && !global_pooling && !ceil_mode) {
      auto *pool_layer = TRT_ENGINE_ADD_LAYER(
          engine_, PoolingNd, *input1, nv_pool_type, nv_ksize);
      pool_layer->setStrideNd(nv_strides);
      pool_layer->setPaddingNd(nv_paddings);
      pool_layer->setAverageCountExcludesPadding(exclusive);
      layer = pool_layer;
    } else if (global_pooling) {
      auto *reduce_layer = TRT_ENGINE_ADD_LAYER(
          engine_, Reduce, *input1, reduce_operation, 28, true);
      layer = reduce_layer;
    } else {
      plugin::Pool3DPluginDynamic *plugin =
          new plugin::Pool3DPluginDynamic(ceil_mode,
                                          pool_type,
                                          adaptive,
                                          ksize,
                                          strides,
                                          paddings,
                                          global_pooling);
      layer = engine_->AddDynamicPlugin(&input1, 1, plugin);
    }
    auto output_name = op_desc.Output("Out")[0];
    layer->setName(("pool3d (Output: " + output_name + ")").c_str());
    layer->getOutput(0)->setName(output_name.c_str());
    engine_->SetITensor(output_name, layer->getOutput(0));
    if (test_mode) {
      engine_->DeclareOutput(output_name);
    }
  }
};

}  // namespace paddle::inference::tensorrt

USE_OP_ITSELF(pool3d);
REGISTER_TRT_OP_CONVERTER(pool3d, Pool3dOpConverter);