Loading folders of images in tensorflow

Question

I\'m new to tensorflow, but i already followed and executed the tutorials they promote and many others all over the web. I made a little convolutional neural network over t

Answer 1

The tf.data API (tensorflow 1.4 onwards) is great for things like this. The pipeline will looks something like the following:

• Create an initial tf.data.Dataset object that iterates over all examples
• (if training) shuffle/repeat the dataset;
• map it through some function that makes all images the same size;
• batch;
• (optionall) prefetch to tell your program to collect the preprocess subsequent batches of data while the network is processing the current batch; and
• and get inputs.

There are a number of ways of creating your initial dataset (see here for a more in depth answer)

TFRecords with Tensorflow Datasets

Supporting tensorflow version 1.12 onwards, Tensorflow datasets provides a relatively straight-forward API for creating tfrecord datasets, and also handles data downloading, sharding, statistics generation and other functionality automatically.

See e.g. this image classification dataset implementation. There's a lot of bookeeping stuff in there (download urls, citations etc), but the technical part boils down to specifying features and writing a _generate_examples function

features = tfds.features.FeaturesDict({
            "image": tfds.features.Image(shape=(_TILES_SIZE,) * 2 + (3,)),
            "label": tfds.features.ClassLabel(
                names=_CLASS_NAMES),
            "filename": tfds.features.Text(),
        })

...

def _generate_examples(self, root_dir):
  root_dir = os.path.join(root_dir, _TILES_SUBDIR)
  for i, class_name in enumerate(_CLASS_NAMES):
    class_dir = os.path.join(root_dir, _class_subdir(i, class_name))
    fns = tf.io.gfile.listdir(class_dir)

    for fn in sorted(fns):
      image = _load_tif(os.path.join(class_dir, fn))
      yield {
          "image": image,
          "label": class_name,
          "filename": fn,
      }

---

You can also generate the tfrecords using lower level operations.

Load images via tf.data.Dataset.map and tf.py_func(tion)

Alternatively you can load the image files from filenames inside tf.data.Dataset.map as below.

image_paths, labels = load_base_data(...)
epoch_size = len(image_paths)
image_paths = tf.convert_to_tensor(image_paths, dtype=tf.string)
labels = tf.convert_to_tensor(labels)

dataset = tf.data.Dataset.from_tensor_slices((image_paths, labels))

if mode == 'train':
    dataset = dataset.repeat().shuffle(epoch_size)


def map_fn(path, label):
    # path/label represent values for a single example
    image = tf.image.decode_jpeg(tf.read_file(path))

    # some mapping to constant size - be careful with distorting aspec ratios
    image = tf.image.resize_images(out_shape)
    # color normalization - just an example
    image = tf.to_float(image) * (2. / 255) - 1
    return image, label


# num_parallel_calls > 1 induces intra-batch shuffling
dataset = dataset.map(map_fn, num_parallel_calls=8)
dataset = dataset.batch(batch_size)
# try one of the following
dataset = dataset.prefetch(1)
# dataset = dataset.apply(
#            tf.contrib.data.prefetch_to_device('/gpu:0'))

images, labels = dataset.make_one_shot_iterator().get_next()

I've never worked in a distributed environment, but I've never noticed a performance hit from using this approach over tfrecords. If you need more custom loading functions, also check out tf.py_func.

More general information here, and notes on performance here