C++ DataTransformer::Transform方法代码示例

TYPED_TEST(DataTransformTest, TestMeanValues) {
  TransformationParameter transform_param;
  const bool unique_pixels = false;  // pixels are equal to label
  const int_tp label = 0;
  const int_tp channels = 3;
  const int_tp height = 4;
  const int_tp width = 5;

  transform_param.add_mean_value(0);
  transform_param.add_mean_value(1);
  transform_param.add_mean_value(2);
  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
  DataTransformer<TypeParam>* transformer =
      new DataTransformer<TypeParam>(transform_param, TEST,
                                     Caffe::GetDefaultDevice());
  transformer->InitRand();
  transformer->Transform(datum, blob);
  for (int_tp c = 0; c < channels; ++c) {
    for (int_tp j = 0; j < height * width; ++j) {
      EXPECT_EQ(blob->cpu_data()[blob->offset(0, c) + j], label - c);
    }
  }
}

  int NumSequenceMatches(const TransformationParameter transform_param,
      const Datum& datum, Phase phase) {
    // Get crop sequence with Caffe seed 1701.
    DataTransformer<Dtype>* transformer =
        new DataTransformer<Dtype>(transform_param, phase);
    const int crop_size = transform_param.crop_size();
    int crop_h = transform_param.crop_h();
    int crop_w = transform_param.crop_w();
    if (crop_size > 0) {
      crop_h = crop_w = crop_size;
    }
    Caffe::set_random_seed(seed_);
    transformer->InitRand();
    Blob<Dtype>* blob =
        new Blob<Dtype>(1, datum.channels(), datum.height(), datum.width());
    if (crop_h > 0 || crop_w > 0) {
      blob->Reshape(1, datum.channels(), crop_h, crop_w);
    }

    vector<vector<Dtype> > crop_sequence;
    for (int iter = 0; iter < this->num_iter_; ++iter) {
      vector<Dtype> iter_crop_sequence;
      transformer->Transform(datum, blob);
      for (int j = 0; j < blob->count(); ++j) {
        iter_crop_sequence.push_back(blob->cpu_data()[j]);
      }
      crop_sequence.push_back(iter_crop_sequence);
    }
    // Check if the sequence differs from the previous
    int num_sequence_matches = 0;
    for (int iter = 0; iter < this->num_iter_; ++iter) {
      vector<Dtype> iter_crop_sequence = crop_sequence[iter];
      transformer->Transform(datum, blob);
      for (int j = 0; j < blob->count(); ++j) {
        num_sequence_matches +=
            (crop_sequence[iter][j] == blob->cpu_data()[j]);
      }
    }
    return num_sequence_matches;
  }

TYPED_TEST(DataTransformTest, TestMeanValue) {
  TransformationParameter transform_param;
  const bool unique_pixels = false;  // pixels are equal to label
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;
  const int mean_value = 2;

  transform_param.add_mean_value(mean_value);
  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
  DataTransformer<TypeParam>* transformer =
      new DataTransformer<TypeParam>(transform_param, TEST);
  transformer->InitRand();
  transformer->Transform(datum, blob);
  for (int j = 0; j < blob->count(); ++j) {
    EXPECT_EQ(blob->cpu_data()[j], label - mean_value);
  }
}

TYPED_TEST(DataTransformTest, TestMeanFile) {
  TransformationParameter transform_param;
  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
  const int_tp label = 0;
  const int_tp channels = 3;
  const int_tp height = 4;
  const int_tp width = 5;
  const int_tp size = channels * height * width;

  // Create a mean file
  string* mean_file = new string();
  MakeTempFilename(mean_file);
  BlobProto blob_mean;
  blob_mean.set_num(1);
  blob_mean.set_channels(channels);
  blob_mean.set_height(height);
  blob_mean.set_width(width);

  for (int_tp j = 0; j < size; ++j) {
      blob_mean.add_data(j);
  }

  LOG(INFO) << "Using temporary mean_file " << *mean_file;
  WriteProtoToBinaryFile(blob_mean, *mean_file);

  transform_param.set_mean_file(*mean_file);
  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
  DataTransformer<TypeParam>* transformer =
      new DataTransformer<TypeParam>(transform_param, TEST,
                                     Caffe::GetDefaultDevice());
  transformer->InitRand();
  transformer->Transform(datum, blob);
  for (int_tp j = 0; j < blob->count(); ++j) {
      EXPECT_EQ(blob->cpu_data()[j], 0);
  }
}