Data Loading API¶

Overview¶

A data iterator reads data batch by batch.

pdl> $data = mx->nd->ones([100,10])
pdl> $nd_iter = mx->io->NDArrayIter($data, batch_size=>25)
pdl> for my $batch (@{ $nd_iter }) { print $batch->data->[0],"\n" }
<AI::MXNet::NDArray 25x10 @cpu(0)>
<AI::MXNet::NDArray 25x10 @cpu(0)>
<AI::MXNet::NDArray 25x10 @cpu(0)>
<AI::MXNet::NDArray 25x10 @cpu(0)>

If $nd_iter->reset() is called, then reads the data again from beginning.

In addition, an iterator provides information about the batch, including the shapes and name.

pdl> $nd_iter = mx->io->NDArrayIter(data=>{data => mx->nd->ones([100,10])}, label=>{softmax_label => mx->nd->ones([100])}, batch_size=>25)
pdl> print($nd_iter->provide_data->[0],"\n")
DataDesc[data,25x10,float32,NCHW]
pdl> print($nd_iter->provide_label->[0],"\n")
DataDesc[softmax_label,25,float32,NCHW]

So this iterator can be used to train a symbol whose input data variable has name data and input label variable has name softmax_label.

pdl> $data  = mx->sym->Variable('data')
pdl> $label = mx->sym->Variable('softmax_label')
pdl> $fullc = mx->sym->FullyConnected(data=>$data, num_hidden=>1)
pdl> $loss  = mx->sym->SoftmaxOutput(data=>$data, label=>$label)
pdl> $mod   = mx->mod->Module($loss)
pdl> print($mod->data_names->[0])
data
pdl> print($mod->label_names->[0])
softmax_label
pdl> $mod->bind(data_shapes=>$nd_iter->provide_data, label_shapes=>$nd_iter->provide_label)

Then we can call $mod->fit($nd_iter, num_epoch=>2) to train loss by 2 epochs.

Predefined Data iterators¶

mx->io->NDArrayIter
mx->io->CSVIter
mx->io->ImageRecordIter
mx->io->ImageRecordUInt8Iter
mx->io->MNISTIter
mx->recordio->MXRecordIO
mx->recordio->MXIndexedRecordIO
mx->image->ImageIter

Helper classes and functions¶

Data structures and other iterators provided in the AI::MXNet::IO package.

AI::MXNet::DataDesc
AI::MXNet::DataBatch
AI::MXNet::DataIter
AI::MXNet::ResizeIter
AI::MXNet::MXDataIter

A list of image modification functions provided by AI::MXNet::Image.

mx->image->imdecode
mx->image->scale_down
mx->image->resize_short
mx->image->fixed_crop
mx->image->random_crop
mx->image->center_crop
mx->image->color_normalize
mx->image->random_size_crop
mx->image->ResizeAug
mx->image->RandomCropAug
mx->image->RandomSizedCropAug
mx->image->CenterCropAug
mx->image->RandomOrderAug
mx->image->ColorJitterAug
mx->image->LightingAug
mx->image->ColorNormalizeAug
mx->image->HorizontalFlipAug
mx->image->CastAug
mx->image->CreateAugmenter

Functions to read and write RecordIO files.

mx->recordio->pack
mx->recordio->unpack
mx->recordio->unpack_img

Develop a new iterator¶

Writing a new data iterator in Perl is straightforward. Most MXNet training/inference programs accept an object with provide_data and provide_label properties. Please refer to AI-MXNet/examples for the examples of custom iterators.