Paddle/paddle/fluid/inference/tests/api/trt_dynamic_shape_test.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 <glog/logging.h>
#include <gtest/gtest.h>
#include "gflags/gflags.h"

#include "paddle/fluid/inference/tests/api/trt_test_helper.h"

namespace paddle {
namespace inference {

void TestDynamic(bool with_dynamic = true) {
  std::string model_dir =
      FLAGS_infer_model + "/conv_bn_swish_split_gelu/conv_bn_swish_split_gelu";
  AnalysisConfig config;
  config.EnableUseGpu(100, 0);
  config.SetModel(model_dir + "/model", model_dir + "/params");
  config.SwitchUseFeedFetchOps(false);
  // Set the input's min, max, opt shape

  config.EnableTensorRtEngine(1 << 30, 1, 1,
                              AnalysisConfig::Precision::kFloat32, false, true);
  if (with_dynamic) {
    std::map<std::string, std::vector<int>> min_input_shape = {
        {"image", {1, 1, 3, 3}}};
    std::map<std::string, std::vector<int>> max_input_shape = {
        {"image", {1, 1, 10, 10}}};
    std::map<std::string, std::vector<int>> opt_input_shape = {
        {"image", {1, 1, 3, 3}}};

    config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
                                  opt_input_shape);
  }
  auto predictor = CreatePaddlePredictor(config);
  auto input_names = predictor->GetInputNames();
  int channels = 1;
  int height = 3;
  int width = 3;
  int input_num = channels * height * width * 1;

  float *input = new float[input_num];
  memset(input, 0, input_num * sizeof(float));
  auto input_t = predictor->GetInputTensor(input_names[0]);
  input_t->Reshape({1, channels, height, width});
  input_t->copy_from_cpu(input);

  ASSERT_TRUE(predictor->ZeroCopyRun());

  std::vector<float> out_data;
  auto output_names = predictor->GetOutputNames();
  auto output_t = predictor->GetOutputTensor(output_names[0]);
  std::vector<int> output_shape = output_t->shape();
  int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
                                std::multiplies<int>());
  out_data.resize(out_num);
  output_t->copy_to_cpu(out_data.data());
}

void TestDynamic2() {
  std::string model_dir =
      FLAGS_infer_model + "/complex_model_dynamic/complex_model_dynamic2";
  AnalysisConfig config;
  config.EnableUseGpu(100, 0);
  config.SetModel(model_dir + "/model", model_dir + "/params");
  config.SwitchUseFeedFetchOps(false);
  // Set the input's min, max, opt shape
  int batch_size = 1;
  std::map<std::string, std::vector<int>> min_input_shape = {
      {"image", {1, 3, 3, 3}}, {"in1", {1, 2, 1, 1}}, {"in2", {1, 2, 1, 1}}};
  std::map<std::string, std::vector<int>> max_input_shape = {
      {"image", {1, 3, 10, 10}}, {"in1", {1, 2, 1, 1}}, {"in2", {1, 2, 1, 1}}};
  std::map<std::string, std::vector<int>> opt_input_shape = {
      {"image", {1, 3, 5, 5}}, {"in1", {1, 2, 1, 1}}, {"in2", {1, 2, 1, 1}}};
  config.EnableTensorRtEngine(1 << 30, batch_size, 0,
                              AnalysisConfig::Precision::kFloat32, false, true);

  config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
                                opt_input_shape);

  auto predictor = CreatePaddlePredictor(config);
  int channels = 3;
  int height = 5;
  int width = 5;
  int input_num = channels * height * width * 1;

  float *input = new float[input_num];
  memset(input, 0, input_num * sizeof(float));
  auto input_names = predictor->GetInputNames();
  auto input_t = predictor->GetInputTensor(input_names[0]);
  input_t->Reshape({batch_size, channels, height, width});
  input_t->copy_from_cpu(input);

  auto input_t1 = predictor->GetInputTensor(input_names[1]);
  input_t1->Reshape({batch_size, 2, 1, 1});
  std::vector<float> first;
  for (int i = 0; i < batch_size * 2; i++) first.push_back(1.0);
  input_t1->copy_from_cpu(first.data());

  auto input_t2 = predictor->GetInputTensor(input_names[2]);
  input_t2->Reshape({batch_size, 2, 1, 1});
  input_t2->copy_from_cpu(first.data());

  ASSERT_TRUE(predictor->ZeroCopyRun());

  std::vector<float> out_data;
  auto output_names = predictor->GetOutputNames();
  auto output_t = predictor->GetOutputTensor(output_names[0]);
  std::vector<int> output_shape = output_t->shape();
  int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
                                std::multiplies<int>());
  out_data.resize(out_num);
  output_t->copy_to_cpu(out_data.data());
  std::vector<float> result = {0.617728, 1.63504, 2.15771, 0.535556};
  for (size_t i = 0; i < out_data.size(); i++) {
    EXPECT_NEAR(result[i], out_data[i], 1e-5);
  }
}

TEST(AnalysisPredictor, trt_dynamic) { TestDynamic(true); }
TEST(AnalysisPredictor, trt_static) { TestDynamic(false); }
TEST(AnalysisPredictor, trt_dynamic2) { TestDynamic2(); }

}  // namespace inference
}  // namespace paddle