0cbbbf8468781d1558431a22fd244ed143115588,models/official/detection/modeling/architecture/nn_ops.py,,pyramid_feature_fusion,#Any#Any#,536

Before Change

    if l == target_level:
      resampled_feats.append(pyramid_feats[l])
    else:
      target_size = pyramid_feats[l].shape.as_list()[1:3]
      target_size[0] *= 2**(l - target_level)
      target_size[1] *= 2**(l - target_level)
      resampled_feat = tf.image.resize_bilinear(

After Change

      target_size[0] *= 2**(l - target_level)
      target_size[1] *= 2**(l - target_level)
      // Casts feat to float32 so the resize_bilinear op can be run on TPU.
      feat = tf.cast(feat, tf.float32)
      feat = tf.image.resize_bilinear(
          feat, size=target_size, align_corners=False)
      // Casts it back to be compatible with the rest opetations.
      feat = tf.cast(feat, pyramid_feats[l].dtype)

Instances