How to augment data in tensorflow tfrecords?

Question

I am storing my data using tfrecords and I read them as tensors using Dataset API and then I use the Estimator API to perform training. Now, I want to do online data-augmentation on each item in the dataset, but after trying for a while I cannot find a way out to do it. I want randomly flipping, randomly rotation and other manipulators.

I am following the instructions given in this tutorial with a custom estimator which is the my CNN and I am not sure where the data augmentation step occurs.

Answer 1

Using TFRecords doesn't prevent you from doing data augmentation.

Following the tutorial you linked in your comment, here is what roughly happens:

• You create the dataset from the TFRecords files, and parse the file to get an image and a label

dataset = tf.data.TFRecordDataset(filenames=filenames)
dataset = dataset.map(parse)

• You can now apply a new preprocessing function to do some data augmentation during training

# Only do it when we are training
if train:
    dataset = dataset.map(train_preprocess)

• The train_preprocess function can be something like this:

def train_preprocess(image, label):
    flip_image = tf.image.random_flip_left_right(image)
    # Other transformations...
    return flip_image, label