ExternalSource operator

In this example, we will see how to use ExternalSource operator with MXNet DALI iterator, that allows us to use an external data source as an input to the Pipeline.

In order to achieve that, we have to define a Iterator or Generator class which next function will return one or several numpy arrays.

import types
import collections
import numpy as np
from random import shuffle
from nvidia.dali.pipeline import Pipeline
import nvidia.dali.ops as ops
import nvidia.dali.types as types

batch_size = 3
epochs = 3

Defining the iterator

class ExternalInputIterator(object):
    def __init__(self, batch_size, device_id, num_gpus):
        self.images_dir = "../../data/images/"
        self.batch_size = batch_size
        with open(self.images_dir + "file_list.txt", 'r') as f:
            self.files = [line.rstrip() for line in f if line is not '']
        # whole data set size
        self.data_set_len = len(self.files)
        # based on the device_id and total number of GPUs - world size
        # get proper shard
        self.files = self.files[self.data_set_len * device_id // num_gpus:
                                self.data_set_len * (device_id + 1) // num_gpus]
        self.n = len(self.files)

    def __iter__(self):
        self.i = 0
        shuffle(self.files)
        return self

    def __next__(self):
        batch = []
        labels = []

        if self.i >= self.n:
            raise StopIteration

        for _ in range(self.batch_size):
            jpeg_filename, label = self.files[self.i].split(' ')
            f = open(self.images_dir + jpeg_filename, 'rb')
            batch.append(np.frombuffer(f.read(), dtype = np.uint8))
            labels.append(np.array([label], dtype = np.uint8))
            self.i = (self.i + 1) % self.n
        return (batch, labels)

    @property
    def size(self,):
        return self.data_set_len

    next = __next__

Defining the pipeline

Now the pipeline itself will be defined. First of all, a framework iterator will be used so we need to make sure that images and the output of the pipeline are uniforms in size, so resize operator is used. Also, iter_setup will raise the StopIteration exception when the AdvancedExternalInputIterator run of data. Worth notice is that iterator needs to be recreated so next time iter_setup is called it has ready data to consume.

class ExternalSourcePipeline(Pipeline):
    def __init__(self, batch_size, num_threads, device_id, external_data):
        super(ExternalSourcePipeline, self).__init__(batch_size,
                                      num_threads,
                                      device_id,
                                      seed=12)
        self.input = ops.ExternalSource()
        self.input_label = ops.ExternalSource()
        self.decode = ops.ImageDecoder(device = "mixed", output_type = types.RGB)
        self.res = ops.Resize(device="gpu", resize_x=240, resize_y=240)
        self.cast = ops.Cast(device = "gpu",
                             dtype = types.UINT8)
        self.external_data = external_data
        self.iterator = iter(self.external_data)

    def define_graph(self):
        self.jpegs = self.input()
        self.labels = self.input_label()
        images = self.decode(self.jpegs)
        images = self.res(images)
        output = self.cast(images)
        return (output, self.labels)

    def iter_setup(self):
        try:
            (images, labels) = self.iterator.next()
            self.feed_input(self.jpegs, images)
            self.feed_input(self.labels, labels)
        except StopIteration:
            self.iterator = iter(self.external_data)
            raise StopIteration

Using the pipeline

In the end, let us see how it works.

last_batch_padded and last_batch_policy are set here only for the demonstration purposes. The user may write any custom code and change the epoch size epoch to epoch. In that case, it is recommended to set size to -1 and let the iterator just wait for StopIteration exception from iter_setup.

The last_batch_padded here tells the iterator that the difference between dataset size and batch size alignment is padded by real data that could be skipped when provided to the framework (last_batch_policy):

from nvidia.dali.plugin.mxnet import DALIClassificationIterator as MXNetIterator
from nvidia.dali.plugin.mxnet import LastBatchPolicy

eii = ExternalInputIterator(batch_size, 0, 1)
pipe = ExternalSourcePipeline(batch_size=batch_size, num_threads=2, device_id = 0,
                              external_data = eii)
pii = MXNetIterator(pipe, size=eii.size, last_batch_padded=True, last_batch_policy=LastBatchPolicy.PARTIAL)

for e in range(epochs):
    for i, data in enumerate(pii):
        print("epoch: {}, iter {}, real batch size: {}".format(e, i, data[0].data[0].shape[0]))
    pii.reset()

/usr/local/lib/python3.6/dist-packages/nvidia/dali/plugin/base_iterator.py:124: Warning: Please set `reader_name` and don't set last_batch_padded and size manually  whenever possible. This may lead, in some situations, to miss some  samples or return duplicated ones. Check the Sharding section of the documentation for more details.
  _iterator_deprecation_warning()

epoch: 0, iter 0, real batch size: 3
epoch: 0, iter 1, real batch size: 3
epoch: 0, iter 2, real batch size: 3
epoch: 0, iter 3, real batch size: 3
epoch: 0, iter 4, real batch size: 3
epoch: 0, iter 5, real batch size: 3
epoch: 0, iter 6, real batch size: 3
epoch: 1, iter 0, real batch size: 3
epoch: 1, iter 1, real batch size: 3
epoch: 1, iter 2, real batch size: 3
epoch: 1, iter 3, real batch size: 3
epoch: 1, iter 4, real batch size: 3
epoch: 1, iter 5, real batch size: 3
epoch: 1, iter 6, real batch size: 3
epoch: 2, iter 0, real batch size: 3
epoch: 2, iter 1, real batch size: 3
epoch: 2, iter 2, real batch size: 3
epoch: 2, iter 3, real batch size: 3
epoch: 2, iter 4, real batch size: 3
epoch: 2, iter 5, real batch size: 3
epoch: 2, iter 6, real batch size: 3