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.
[1]:
import types
import collections
import numpy as np
from random import shuffle
from nvidia.dali.pipeline import Pipeline
import nvidia.dali as dali
import nvidia.dali.fn as fn
import mxnet
batch_size = 3
epochs = 3
Defining the iterator¶
[2]:
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:
self.__iter__()
raise StopIteration
for _ in range(self.batch_size):
jpeg_filename, label = self.files[self.i % self.n].split(' ')
batch.append(np.fromfile(self.images_dir + jpeg_filename, dtype = np.uint8)) # we can use numpy
labels.append(mxnet.ndarray.array([int(label)], dtype = 'uint8')) # or MXNet native arrays
self.i += 1
return (batch, labels)
def __len__(self):
return self.data_set_len
next = __next__
Defining the pipeline¶
Now let’s define our pipeline. We need an instance of Pipeline
class and some operators which will define the processing graph. Our external source provides 2 outpus which we can conveniently unpack by specifying num_outputs=2
in the external source operator.
[3]:
def ExternalSourcePipeline(batch_size, num_threads, device_id, external_data):
pipe = Pipeline(batch_size, num_threads, device_id)
with pipe:
jpegs, labels = fn.external_source(source=external_data, num_outputs=2)
images = fn.image_decoder(jpegs, device="mixed")
images = fn.resize(images, resize_x=240, resize_y=240)
output = fn.cast(images, dtype=dali.types.UINT8)
pipe.set_outputs(output, labels)
return pipe
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
):
[4]:
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, 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()
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