b3ef7ae9d3c1f6cfc9a202255ed2aa6b9d2aa791,research/delf/delf/python/feature_extractor.py,,PostProcessDescriptors,#Any#Any#Any#,334

Before Change

    if use_pca:
      // Load PCA parameters.
      pca_mean = tf.constant(
          datum_io.ReadFromFile(pca_parameters.mean_path), dtype=tf.float32)
      pca_matrix = tf.constant(
          datum_io.ReadFromFile(pca_parameters.projection_matrix_path),
          dtype=tf.float32)
      pca_dim = pca_parameters.pca_dim
      pca_variances = None
      if pca_parameters.use_whitening:
        pca_variances = tf.squeeze(
            tf.constant(
                datum_io.ReadFromFile(pca_parameters.pca_variances_path),
                dtype=tf.float32))

      // Apply PCA, and whitening if desired.
      final_descriptors = ApplyPcaAndWhitening(final_descriptors, pca_matrix,
                                               pca_mean, pca_dim,
                                               pca_parameters.use_whitening,
                                               pca_variances)

      // Re-normalize.
      final_descriptors = tf.nn.l2_normalize(
          final_descriptors, axis=1, name="pca_l2_normalization")

After Change

    if use_pca:
      // Apply PCA, and whitening if desired.
      final_descriptors = ApplyPcaAndWhitening(final_descriptors,
                                               pca_parameters["matrix"],
                                               pca_parameters["mean"],
                                               pca_parameters["dim"],
                                               pca_parameters["use_whitening"],

Instances