test_paddle_inference_api_impl.cc 5.4 KB
Newer Older
X
Xin Pan 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
/* 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/contrib/inference/paddle_inference_api_impl.h"
#include "paddle/fluid/inference/tests/test_helper.h"

DEFINE_string(dirname, "", "Directory of the inference model.");

namespace paddle {

PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
  PaddleTensor pt;
  pt.data.data = t->data<void>();

  if (t->type() == typeid(int64_t)) {
    pt.data.length = t->numel() * sizeof(int64_t);
    pt.dtype = PaddleDType::INT64;
  } else if (t->type() == typeid(float)) {
    pt.data.length = t->numel() * sizeof(float);
    pt.dtype = PaddleDType::FLOAT32;
  } else {
    LOG(FATAL) << "unsupported type.";
  }
  pt.shape = framework::vectorize2int(t->dims());
  return pt;
}

43 44
ConfigImpl GetConfig() {
  ConfigImpl config;
X
Xin Pan 已提交
45 46
  config.model_dir = FLAGS_dirname + "word2vec.inference.model";
  LOG(INFO) << "dirname  " << config.model_dir;
X
Xin Pan 已提交
47
  config.fraction_of_gpu_memory = 0.15;
X
Xin Pan 已提交
48 49
  config.device = 0;
  config.share_variables = true;
50 51
  return config;
}
X
Xin Pan 已提交
52

53 54 55
TEST(paddle_inference_api_impl, word2vec) {
  ConfigImpl config = GetConfig();
  std::unique_ptr<PaddlePredictor> predictor = CreatePaddlePredictor(config);
X
Xin Pan 已提交
56 57 58 59 60 61 62 63 64 65

  framework::LoDTensor first_word, second_word, third_word, fourth_word;
  framework::LoD lod{{0, 1}};
  int64_t dict_size = 2073;  // The size of dictionary

  SetupLoDTensor(&first_word, lod, static_cast<int64_t>(0), dict_size - 1);
  SetupLoDTensor(&second_word, lod, static_cast<int64_t>(0), dict_size - 1);
  SetupLoDTensor(&third_word, lod, static_cast<int64_t>(0), dict_size - 1);
  SetupLoDTensor(&fourth_word, lod, static_cast<int64_t>(0), dict_size - 1);

66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138
  std::vector<PaddleTensor> paddle_tensor_feeds;
  paddle_tensor_feeds.push_back(LodTensorToPaddleTensor(&first_word));
  paddle_tensor_feeds.push_back(LodTensorToPaddleTensor(&second_word));
  paddle_tensor_feeds.push_back(LodTensorToPaddleTensor(&third_word));
  paddle_tensor_feeds.push_back(LodTensorToPaddleTensor(&fourth_word));

  std::vector<PaddleTensor> outputs;
  ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
  ASSERT_EQ(outputs.size(), 1UL);
  size_t len = outputs[0].data.length;
  float* data = static_cast<float*>(outputs[0].data.data);
  for (int j = 0; j < len / sizeof(float); ++j) {
    ASSERT_LT(data[j], 1.0);
    ASSERT_GT(data[j], -1.0);
  }

  std::vector<paddle::framework::LoDTensor*> cpu_feeds;
  cpu_feeds.push_back(&first_word);
  cpu_feeds.push_back(&second_word);
  cpu_feeds.push_back(&third_word);
  cpu_feeds.push_back(&fourth_word);

  framework::LoDTensor output1;
  std::vector<paddle::framework::LoDTensor*> cpu_fetchs1;
  cpu_fetchs1.push_back(&output1);

  TestInference<platform::CPUPlace>(config.model_dir, cpu_feeds, cpu_fetchs1);

  float* lod_data = output1.data<float>();
  for (size_t i = 0; i < output1.numel(); ++i) {
    EXPECT_LT(lod_data[i] - data[i], 1e-3);
    EXPECT_GT(lod_data[i] - data[i], -1e-3);
  }

  free(outputs[0].data.data);
}

TEST(paddle_inference_api_impl, image_classification) {
  int batch_size = 2;
  bool use_mkldnn = false;
  bool repeat = false;
  ConfigImpl config = GetConfig();
  config.model_dir =
      FLAGS_dirname + "image_classification_resnet.inference.model";

  const bool is_combined = false;
  std::vector<std::vector<int64_t>> feed_target_shapes =
      GetFeedTargetShapes(config.model_dir, is_combined);

  framework::LoDTensor input;
  // Use normilized image pixels as input data,
  // which should be in the range [0.0, 1.0].
  feed_target_shapes[0][0] = batch_size;
  framework::DDim input_dims = framework::make_ddim(feed_target_shapes[0]);
  SetupTensor<float>(
      &input, input_dims, static_cast<float>(0), static_cast<float>(1));
  std::vector<framework::LoDTensor*> cpu_feeds;
  cpu_feeds.push_back(&input);

  framework::LoDTensor output1;
  std::vector<framework::LoDTensor*> cpu_fetchs1;
  cpu_fetchs1.push_back(&output1);

  TestInference<platform::CPUPlace, false, true>(config.model_dir,
                                                 cpu_feeds,
                                                 cpu_fetchs1,
                                                 repeat,
                                                 is_combined,
                                                 use_mkldnn);

  std::unique_ptr<PaddlePredictor> predictor = CreatePaddlePredictor(config);
  std::vector<PaddleTensor> paddle_tensor_feeds;
  paddle_tensor_feeds.push_back(LodTensorToPaddleTensor(&input));
X
Xin Pan 已提交
139 140

  std::vector<PaddleTensor> outputs;
141
  ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
142
  ASSERT_EQ(outputs.size(), 1UL);
143 144 145 146
  size_t len = outputs[0].data.length;
  float* data = static_cast<float*>(outputs[0].data.data);
  float* lod_data = output1.data<float>();
  for (size_t j = 0; j < len / sizeof(float); ++j) {
X
clean  
Xin Pan 已提交
147
    EXPECT_NEAR(lod_data[j], data[j], 1e-3);
X
Xin Pan 已提交
148
  }
149
  free(data);
X
Xin Pan 已提交
150 151 152
}

}  // namespace paddle