How to create tf.data.dataset from directories of tfrecords?

Question

My dataset has different directories and each directory is corresponding to one class. There are different numbers of .tfrecords in each directory. My question is that how can I sample 5 images (each .tfrecord file corresponds to one image) from each directory? My other question is that how can I sample 5 of these directories and then sample 5 images from each.

I just want to do it with tf.data.dataset. So I want to have a dataset from which I get an iterator and that iterator.next() gives me a batch of 25 images containing 5 samples from 5 classes.

Answer 1

EDIT: If the number of classes is greater than 5, then you can use the new tf.contrib.data.sample_from_datasets() API (currently available in tf-nightly and will be available in TensorFlow 1.9).

directories = ["class_0/*", "class_1/*", "class_2/*", "class_3/*", ...]

CLASSES_PER_BATCH = 5
EXAMPLES_PER_CLASS_PER_BATCH = 5
BATCH_SIZE = CLASSES_PER_BATCH * EXAMPLES_PER_CLASS_PER_BATCH
NUM_CLASSES = len(directories)


# Build one dataset per class.
per_class_datasets = [
    tf.data.TFRecordDataset(tf.data.Dataset.list_files(d)) for d in directories]

# Next, build a dataset where each element is a vector of 5 classes to be chosen
# for a particular batch.
classes_per_batch_dataset = tf.contrib.data.Counter().map(
    lambda _: tf.random_shuffle(tf.range(NUM_CLASSES))[:CLASSES_PER_BATCH]))

# Transform the dataset of per-batch class vectors into a dataset with one
# one-hot element per example (i.e. 25 examples per batch).
class_dataset = classes_per_batch_dataset.flat_map(
    lambda classes: tf.data.Dataset.from_tensor_slices(
        tf.one_hot(classes, num_classes)).repeat(EXAMPLES_PER_CLASS_PER_BATCH))

# Use `tf.contrib.data.sample_from_datasets()` to select an example from the
# appropriate dataset in `per_class_datasets`.
example_dataset = tf.contrib.data.sample_from_datasets(per_class_datasets,
                                 class_dataset)

# Finally, combine 25 consecutive examples into a batch.
result = example_dataset.batch(BATCH_SIZE)

---

If you have exactly 5 classes, you can define a nested dataset for each directory and combine them using Dataset.interleave():

# NOTE: We're assuming that the 0th directory contains elements from class 0, etc.
directories = ["class_0/*", "class_1/*", "class_2/*", "class_3/*", "class_4/*"]
directories = tf.data.Dataset.from_tensor_slices(directories)
directories = directories.apply(tf.contrib.data.enumerate_dataset())    

# Define a function that maps each (class, directory) pair to the (shuffled)
# records in those files.
def per_directory_dataset(class_label, directory_glob):
  files = tf.data.Dataset.list_files(directory_glob, shuffle=True)
  records = tf.data.TFRecordDataset(records)
  # Zip the records with their class. 
  # NOTE: This part might not be necessary if the records contain information about
  # their class that can be parsed from them.
  return tf.data.Dataset.zip(
      (records, tf.data.Dataset.from_tensors(class_label).repeat(None)))

# NOTE: The `cycle_length` and `block_length` here aren't strictly necessary,
# because the batch size is exactly `number of classes * images per class`.
# However, these arguments may be useful if you want to decouple these numbers.
merged_records = directories.interleave(per_directory_dataset,
                                        cycle_length=5, block_length=5)
merged_records = merged_records.batch(25)

Answer 2

Please find below a potential solution.

For the sake of the demonstration, I am using a python generator instead of TFRecords as input (I am supposing you know how to use TF Dataset to read and parse the files in each folder. Other threads are otherwise covering this, e.g. here).

import tensorflow as tf
import numpy as np

def get_class_generator(class_id, num_el, el_shape=(32, 32), el_dtype=np.int32):
    """ Returns a dummy generator, 
        outputting "num_el" elements of a single class (input data & class label) 
    """
    def class_generator():
        x = 0
        for x in range(num_el):
            element = np.ones(el_shape, dtype=el_dtype) * x
            yield element, class_id
    return class_generator


def concatenate_datasets(datasets):
    """ Concatenate a list of datasets together.
        Snippet by user2781994 (https://stackoverflow.com/a/49069420/624547)
    """
    ds0 = tf.data.Dataset.from_tensors(datasets[0])
    for ds1 in datasets[1:]:
        ds0 = ds0.concatenate(tf.data.Dataset.from_tensors(ds1))
    return ds0


num_classes = 11
class_batch_size = 3
num_classes_per_batch = 5
# note: using 3 instead of 5 for class_batch_size in this example 
#       just to distinguish between the 2 vars.

# Initializing per-class datasets:
# (note: replace tf.data.Dataset.from_generator(...) to suit your use-case
#        e.g. tf.contrib.data.TFRecordDataset(glob.glob(perclass_tfrecords_path))
#                            .map(your_parsing_function)
class_datasets = [tf.data.Dataset
                 .from_generator(get_class_generator(
                      class_id, num_el=np.random.randint(1, 60) 
                      # ^ simulating unequal number of samples per class
                      ), (tf.int32, tf.int32), ([32, 32], []))
                 .repeat(-1)
                 .batch(class_batch_size)
                  for class_id in range(num_classes)]

# Initializing complete dataset:
dataset = (tf.data.Dataset
           # Concatenating all the class datasets together:
           .zip(tuple(class_datasets))
           .flat_map(lambda *args: concatenate_datasets(args))
           # Shuffling the class datasets:
           .shuffle(buffer_size=num_classes)
           # Flattening batches from shape (num_classes_per_batch, class_batch_size, ...)
           # into (num_classes_per_batch * class_batch_size, ...):
           .flat_map(lambda *args: tf.data.Dataset.from_tensor_slices(args))
           # Returning correct number of el. (num_classes_per_batch * class_batch_size):
           .batch(num_classes_per_batch * class_batch_size))

# Visualizing results:
next_batch = dataset.make_one_shot_iterator().get_next()
with tf.Session() as sess:
    for i in range(10):
        batch = sess.run(next_batch)
        print(">> batch {}".format(i))
        print("- inputs shape: {} ; label shape: {}".format(batch[0].shape,batch[1].shape))
        print("- class values: {}".format(batch[1]))

Outputs:

>> batch 0
- inputs shape: (15, 32, 32) ; label shape: (15,)
- class values: [ 1  1  1  0  0  0 10 10 10  2  2  2  9  9  9]
>> batch 1
- inputs shape: (15, 32, 32) ; label shape: (15,)
- class values: [0 0 0 2 2 2 3 3 3 5 5 5 6 6 6]
>> batch 2
- inputs shape: (15, 32, 32) ; label shape: (15,)
- class values: [ 9  9  9  8  8  8  4  4  4  3  3  3 10 10 10]
>> batch 3
- inputs shape: (15, 32, 32) ; label shape: (15,)
- class values: [7 7 7 8 8 8 6 6 6 6 6 6 2 2 2]
>> batch 4
- inputs shape: (15, 32, 32) ; label shape: (15,)
- class values: [1 1 1 0 0 0 1 1 1 8 8 8 5 5 5]
>> batch 5
- inputs shape: (15, 32, 32) ; label shape: (15,)
- class values: [2 2 2 4 4 4 9 9 9 5 5 5 5 5 5]
>> batch 6
- inputs shape: (15, 32, 32) ; label shape: (15,)
- class values: [0 0 0 7 7 7 3 3 3 9 9 9 7 7 7]
>> batch 7
- inputs shape: (15, 32, 32) ; label shape: (15,)
- class values: [10 10 10 10 10 10  1  1  1  6  6  6  7  7  7]
>> batch 8
- inputs shape: (15, 32, 32) ; label shape: (15,)
- class values: [4 4 4 3 3 3 5 5 5 6 6 6 3 3 3]
>> batch 9
- inputs shape: (15, 32, 32) ; label shape: (15,)
- class values: [8 8 8 9 9 9 2 2 2 8 8 8 0 0 0]