cd033991fb0ba018d2f9c6f3434f02a99fa91941,models/experimental/resnet_bfloat16/imagenet_input.py,ImageNetInput,input_fn,#ImageNetInput#Any#,125

Before Change

      return images, labels

    if self.is_training:
      dataset = dataset.map(set_shapes)

    dataset = dataset.prefetch(32)  // Prefetch overlaps in-feed with training
    return dataset  // Must return the dataset and not tensors for high perf!

After Change

    // the same image twice by dropping the final batch if it is less than a full
    // batch size. As long as this validation is done with consistent batch size,
    // exactly the same images will be used.
    dataset = dataset.apply(
        tf.contrib.data.map_and_batch(
            self.dataset_parser, batch_size=batch_size,
            num_parallel_batches=self.num_cores, drop_remainder=True))

    if self.use_transpose:
      dataset = dataset.map(
          lambda images, labels: (tf.transpose(images, [1, 2, 3, 0]), labels),

Instances