7d209b46e2f6402e44c69f6fe00384aa14286b08,models/experimental/resnet_bfloat16/imagenet_input.py,ImageNetInput,input_fn,#ImageNetInput#Any#,119

Before Change

            fetch_dataset, cycle_length=self.num_cores, sloppy=True))
    dataset = dataset.shuffle(1024)

    dataset = dataset.map(
        self.dataset_parser,
        num_parallel_calls=self.num_parallel_calls)
    dataset = dataset.prefetch(batch_size)

    // For training, batch as usual. When evaluating, prevent accidentally
    // evaluating 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

After Change

    // than a full batch size. As long as this validation is done with
    // consistent batch size, exactly the same images will be used.
    if not self.is_training:
      dataset = dataset.apply(batching.filter_irregular_batches(batch_size))

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

Instances