Awesome

dataload

local dl = require 'dataload'

A collection of Torch dataset loaders. The library provides the following generic data loader classes :

DataLoader : an abstract class inherited by the following classes;
TensorLoader : for tensor or nested (i.e. tables of) tensor datasets;
ImageClass : for image classification datasets stored in a flat folder structure;
AsyncIterator : decorates a DataLoader for asynchronou multi-threaded iteration;
SequenceLoader : for sequence datasets like language or time-series;
MultiSequence : for shuffled sets of sequence datasets like shuffled sentences;
MultiImageSequence : for suffled sets of sequences of input and target images.

The library also provides functions for downloading specific datasets and preparing them using the above loaders :

loadMNIST : load the MNIST handwritten digit dataset for image classification;
loadCIFAR10 : load the CIFAR10 dataset for image classification;
loadImageNet : load the ILSVRC2014 dataset for image classification;
loadPTB : load the Penn Tree Bank corpus for language modeling;
loadGBW : load the Google Billion Words corpus for language modeling;
loadSentiment140 : load the Twitter data for sentiment analysis/classification (sad, happy).

Also, we try to provide some useful preprocessing functions :

fitImageNormalize : normalize images by channel.

DataLoader

dataloader = dl.DataLoader()

An abstract class inherited by all DataLoader instances. It wraps a data set to provide methods for accessing inputs and targets. The data itself may be loaded from disk or memory.

[n] size()

Returns the number of samples in the dataloader.

[size] isize([excludedim])

Returns the size of inputs. When excludedim is 1 (the default), the batch dimension is excluded from size. When inputs is a tensor, the returned size is a table of numbers. When it is a table of tensors, the returned size is a table of table of numbers.

[size] tsize([excludedim])

Returns the size of targets. When excludedim is 1 (the default), the batch dimension is excluded from size. When targets is a tensor, the returned size is a table of numbers. When it is a table of tensors, the returned size is a table of table of numbers.

[inputs, targets] index(indices, [inputs, targets])

Returns inputs and targets containing samples indexed by indices.

So for example :

indices = torch.LongTensor{1,2,3,4,5}
inputs, targets = dataloader:index(indices)

would return a batch of inputs and targets containing samples 1 through 5. When inputs and targets are provided as arguments, they are used as memory buffers for the returned inputs and targets, i.e. their allocated memory is reused.

[inputs, targets] sample(batchsize, [inputs, targets])

Returns inputs and targets containing batchsize random samples. This method is equivalent to :

indices = torch.LongTensor(batchsize):random(1,dataloader:size())
inputs, targets = dataloader:index(indices)

[inputs, targets] sub(start, stop, [inputs, targets])

Returns inputs and targets containing stop-start+1 samples between start and stop. This method is equivalent to :

indices = torch.LongTensor():range(start, stop)
inputs, targets = dataloader:index(indices)

shuffle()

Internally shuffles the inputs and targets. Note that not all subclasses support this method.

[ds1, ds2] split(ratio)

Splits the dataloader into two new DataLoader instances where ds1 contains the first math.floor(ratio x dataloader:size()) samples, and ds2 contains the remainder. Useful for splitting a training set into a new training set and validation set.

[iterator] subiter([batchsize, epochsize, ...])

Returns an iterator over a validation and test sets. Each iteration returns 3 values :

k : the number of samples processed so far. Each iteration returns a maximum of batchsize samples.
inputs : a tensor (or nested table thereof) containing a maximum of batchsize inputs.
targets : a tensor (or nested table thereof) containing targets for the commensurate inputs.

The iterator will return batches of inputs and targets of size at most batchsize until epochsize samples have been returned.

Note that the default implementation of this iterator is to call sub for each batch. Sub-classes may over-write this behavior.

Example :

local dl = require 'dataload'

inputs, targets = torch.range(1,5), torch.range(1,5)
dataloader = dl.TensorLoader(inputs, targets)

local i = 0
for k, inputs, targets in dataloader:subiter(2,6) do
   i = i + 1
   print(string.format("batch %d, nsampled = %d", i, k))
   print(string.format("inputs:\n%stargets:\n%s", inputs, targets))
end

Output :

batch 1, nsampled = 2	
inputs:
 1
 2
[torch.DoubleTensor of size 2]
targets:
 1
 2
[torch.DoubleTensor of size 2]
	
batch 2, nsampled = 4	
inputs:
 3
 4
[torch.DoubleTensor of size 2]
targets:
 3
 4
[torch.DoubleTensor of size 2]
	
batch 3, nsampled = 5	
inputs:
 5
[torch.DoubleTensor of size 1]
targets:
 5
[torch.DoubleTensor of size 1]
	
batch 4, nsampled = 6	
inputs:
 1
[torch.DoubleTensor of size 1]
targets:
 1
[torch.DoubleTensor of size 1]

Note how the last two batches are of size 1 while those before are of size batchsize = 2. The reason for this is that the dataloader only has 5 samples.
So the last batch is split between the last sample and the first.

[iterator] sampleiter([batchsize, epochsize, ...])

Returns an iterator over a training set. Each iteration returns 3 values :

k : the number of samples processed so far. Each iteration returns a maximum of batchsize samples.
inputs : a tensor (or nested table thereof) containing a maximum of batchsize inputs.
targets : a tensor (or nested table thereof) containing targets for the commensurate inputs.

The iterator will return batches of inputs and targets of size at most batchsize until epochsize samples have been returned.

Note that the default implementation of this iterator is to call sample for each batch. Sub-classes may over-write this behavior.

Example :

local dl = require 'dataload'

inputs, targets = torch.range(1,5), torch.range(1,5)
dataloader = dl.TensorLoader(inputs, targets)

local i = 0
for k, inputs, targets in dataloader:sampleiter(2,6) do
   i = i + 1
   print(string.format("batch %d, nsampled = %d", i, k))
   print(string.format("inputs:\n%stargets:\n%s", inputs, targets))
end

Output :

batch 1, nsampled = 2	
inputs:
 1
 2
[torch.DoubleTensor of size 2]
targets:
 1
 2
[torch.DoubleTensor of size 2]
	
batch 2, nsampled = 4	
inputs:
 4
 2
[torch.DoubleTensor of size 2]
targets:
 4
 2
[torch.DoubleTensor of size 2]
	
batch 3, nsampled = 6	
inputs:
 4
 1
[torch.DoubleTensor of size 2]
targets:
 4
 1
[torch.DoubleTensor of size 2]

reset()

Resets all internal counters such as those used for iterators. Called by AsyncIterator before serializing the DataLoader to threads.

collectgarbage()

Collect garbage every self.gccdelay times this method is called.

[copy] clone()

Returns a deep copy clone of self.

TensorLoader

dataloader = dl.TensorLoader(inputs, targets)

The TensorLoader can be used to encapsulate tensors of inputs and targets. As an example, consider a dummy 3 x 8 x 8 image classification dataset consisting of 1000 samples and 10 classes:

inputs = torch.randn(1000, 3, 8, 8)
targets = torch.LongTensor(1000):random(1,10)
dataloader = dl.TensorLoader(inputs, targets)

The TensorLoader can also be used to encapsulate nested tensors of inputs and targets. It uses recursive functions to handle nestings of arbitrary depth. As an example, let us modify the above example to include x,y GPS coordinates in the inputs and a parallel set of classification targets (7 classes):

inputs = {torch.randn(1000, 3, 8, 8), torch.randn(1000, 2)}
targets = {torch.LongTensor(1000):random(1,10), torch.LongTensor(1000):random(1,7)}
dataloader = dl.TensorLoader(inputs, targets)

ImageClass

dataloader = dl.ImageClass(datapath, loadsize, [samplesize, samplefunc, sortfunc, verbose])

For loading an image classification data set stored in a flat folder structure :

(datapath)/(classdir)/(imagefile).(jpg|png|etc)

So directory classdir is expected to contain the all images belonging to that class. All image files are indexed into an efficient CharTensor during initialization. Images are only loaded into inputs and targets tensors upon calling batch sampling methods like index, sample and sub.

Note that for asynchronous loading of images (i.e. loading batches of images in different threads), the ImageClass loader can be decorated with an AsyncIterator. Images on disk can have different height, width and number of channels.

Constructor arguments are as follows :

datapath : one or many paths to directories of images;
loadsize : initialize size to load the images to. Example : {3, 256, 256};
samplesize : consistent sample size to resize the images to. Defaults to loadsize;
samplefunc : function f(self, dst, path) used to create a sample(s) from an image path. Stores them in CharTensor dst. Strings "sampleDefault" (the default), "sampleTrain" or "sampleTest" can also be provided as they refer to existing functions
verbose : display verbose message (default is true);
sortfunc : comparison operator used for sorting classdir to get class indices. Defaults to the < operator.

AsyncIterator

dataloader = dl.AsyncIterator(dataloader, [nthread, verbose, serialmode])

This DataLoader subclass overwrites the subiter and sampleiter iterator methods. The implementation uses the threads package to build a pool of nthread worker threads. The main thread delegates the tasks of building inputs and targets tensors to the workers. The workers each have a deep copy of the decorated dataloader. The optional parameter serialmode can be specified as 'ascii' (default) or 'binary'. If large amounts of data need to be processed, 'binary' can prevent the dataloader from allocating too much RAM. When a task is received from the main thread through the Queue, they call sample or sub to build the batch and return the inputs and targets to the main thread. The iteration is asynchronous as the first iteration will fill the Queue with nthread tasks. Note that when nthread > 1 the order of tensors is not deterministic. This loader is well suited for decorating a dl.ImageClass instance and other such I/O and CPU bound loaders.

SequenceLoader

dataloader = dl.SequenceLoader(sequence, batchsize, [bidirectional])

This DataLoader subclass can be used to encapsulate a sequence for training time-series or language models. The sequence is a tensor where the first dimension indexes time. Internally, the loader will split the sequence into batchsize subsequences. Calling the sub(start, stop, inputs, targets) method will return inputs and targets of size seqlen x batchsize [x inputsize] where stop - start + 1 <= seqlen. See RNNLM training script for an example.

The bidirectional argument should be set to true for bidirectional models like BRNN/BLSTMs. In which case, the returned inputs and targets will be aligned. For example, using batchsize = 3 and seqlen = 5 :

print(inputs:t(), targets:t())
   36  1516   853    94  1376
 3193   433   553   805   521
  512   434    57  1029  1962
[torch.IntTensor of size 3x5]

   36  1516   853    94  1376
 3193   433   553   805   521
  512   434    57  1029  1962
[torch.IntTensor of size 3x5]

When bidirectional is false (the default), the targets will be one step in the future with respect to the inputs : For example, using batchsize = 3 and seqlen = 5 :

print(inputs:t(), targets:t())
   36  1516   853    94  1376
 3193   433   553   805   521
  512   434    57  1029  1962
[torch.IntTensor of size 3x5]

 1516   853    94  1376   719
  433   553   805   521    27
  434    57  1029  1962    49
[torch.IntTensor of size 3x5]

MultiSequence

dataloader = dl.MultiSequence(sequences, batchsize)

This DataLoader subclass is used by the Billion Words dataset to encapsulate unordered sentences. The sequences arguments is a table or tds.Vec of tensors. Each such tensors is a single sequence independent of the others. The tensor can be multi-dimensional as long as the non-sequence dimension sizes are consistent from sequence to sequence.

When calling sub(start, stop) or subiter(seqlen) methods, a column of the returned inputs and targets tensors (of size seqlen x batchsize) could contain multiple sequences. For example, a character-level language model could look like:

target : [ ] E L L O [ ] C R E E N ...
input  : [ ] H E L L [ ] S C R E E ...

where HELLO and SCREEN would be two independent sequences. Note that [ ] is a zero mask used to seperate independent sequences. For most cases, the [ ] token is a 0. Except for 1D targets, where it is a 1 (so that it works with ClassNLLCriterion).

MultiImageSequence

ds = dl.MultiImageSequence(datapath, batchsize, loadsize, samplesize, [samplefunc, verbose])

This DataLoader is used to load datasets consisting of independent sequences of input and target images. So basically, each independent sequence consists of two sequences of the same size, one for inputs, one for targets.

As a concrete example, this DataLoader could be used to wrap a dataset where each input is a sequence of video frames, and its commensurate targets are binary masks.

Like the ImageClass loader, MultiImageSequence expects images to be stored on disk. Each directory is organized as :

[datapath]/[seqid]/[input|target][1,2,3,...,T].jpg

where the datapath (first constructor argument) specifies the file system path to the data. That directory is expected to contain a folder for each sequence, here represented by the seqid variable. The seqid folder can have any name, but by default its contents are expected to contain the pattern input%d.jpg and target%d.jpg for input and target images, respectively. Internally, the %d is replaced with integers starting at 1 until no more images are found. These patterns can be replaced after construction via the inputpattern and targetpattern.

Variable length sequences are natively supported.

Images will be only be loaded when requested.

Like the MultiSequence loader, the batchsize must be specified during construction. Like the ImageClass, the loadsize argument specifies that size of to which the images are to be loaded initially. These are specified as two tables in c x h x w format, for inputs and targets respectively (e.g. {{3,28,28},{1,8,8}}).

The samplesize specifies the returned input image size (e.g. {3,24,24}). The actual sample size of the targets cannot be provided as it will be forced to be proportional to the input's load to sample size.

The samplefunc specifies the function to use for sampling input and target images. The default value of sampleDefault simply resizes the images to the given input samplesize and the proportional target sample size. When sampleTrain is provided, a random location will be chosen for each sampled sequence.

When calling sub(start, stop) the returned input and target are tensors of size seqlen x batchsize x samplesize. Since variable length sequences are natively supported, the returned inputs and targets will be separated by mask tokens (here represented by [ ]):

[ ] target11, target12, target13, ..., target1T [ ] target21, ...
[ ] input11,  input12,  input13,  ..., input1T  [ ] input21, ...

The mask tokens [ ] represent images with nothing but zeros.

For large datasets use Lua5.2 instead of LuaJIT to avoid memory errors (see torch.ch).

The following are attributes that can be set to true to modify the behavior of the loader:

cropeverystep: samples a random uniform crop location every time-step (instead of once per sequence)
varyloadsize: random-uniformly samples a loadsize between samplesize and loadsize (this effectively scales the cropped location)
scaleeverystep: varies loadsize every step instead of once per sequence
randseq: each new sequence is chosen random uniformly

loadMNIST

train, valid, test = dl.loadMNIST([datapath, validratio, scale, srcurl])

Returns the training, validation and testing sets as 3 TensorLoader instances. Each such loader encapsulates a part of the MNIST dataset which is located in datapath (defaults to dl.DATA_PATH/mnist). The validratio argument, a number between 0 and 1, specifies the ratio of the 60000 training samples that will be allocated to the validation set. The scale argument specifies range within which pixel values will be scaled (defaults to {0,1}). The srcurl specifies the URL from where the raw data can be downloaded from if not located on disk.

loadCIFAR10

train, valid, test = dl.loadCIFAR10([datapath, validratio, scale, srcurl])

Returns the training, validation and testing sets as 3 TensorLoader instances. Each such loader encapsulates a part of the CIFAR10 dataset which is located in datapath (defaults to dl.DATA_PATH/cifar-10-batches-t7). The validratio argument, a number between 0 and 1, specifies the ratio of the 50000 training samples that will be allocated to the validation set. The scale argument specifies range within which pixel values will be scaled (defaults to {0,1}). The srcurl specifies the URL from where the raw data can be downloaded from if not located on disk.

loadPTB

train, valid, test = dl.loadPTB(batchsize, [datapath, srcurl])

Returns the training, validation and testing sets as 3 SequenceLoader instance Each such loader encapsulates a part of the Penn Tree Bank dataset which is located in datapath (defaults to dl.DATA_PATH/PennTreeBank). If the files aren't found in the datapath, they will be automatically downloaded from the srcurl URL. The batchsize specifies the number of samples that will be returned when iterating through the dataset. If specified as a table, its elements specify the batchsize of commensurate train, valid and test tables. We recommend a batchsize of 1 for evaluation sets (e.g. {50,1,1}).

See RNNLM training script for an example.

loadImageNet

Ref.: A. http://image-net.org/challenges/LSVRC/2014/download-images-5jj5.php

train, valid = dl.loadImageNet(datapath, [nthread, loadsize, samplesize, verbose])

Returns the training and validation sets of the Large Scale Visual Recognition Challenge 2014 (ILSVRC2014) image classification dataset (commonly known as ImageNet). The dataset hasn't changed from 2012-2014.

The returned train and valid loaders do not read all images into memory when first loaded. Each dataset is implemented using an ImageClass loader decorated by an AsyncIterator.

The datapath should point to a directory containing the outputs of the downloadimagenet.lua and harmonizeimagenet.lua scripts (see bellow).

Requirements

Due to its size, the data first needs to be prepared offline. Use downloadimagenet.lua to download and extract the data :

th downloadimagenet.lua --savePath '/path/to/diskspace/ImageNet'

The entire process requires about 360 GB of disk space to complete the download and extraction process. This can be reduced to about 150 GB if the training set is downloaded and extracted first, and all the .tar files are manually deleted. Repeat for the validation set, devkit and metadata. If you still don't have enough space in one partition, you can divide the data among different partitions. We recommend a good internet connection (>60Mbs download) and a Solid-State Drives (SSD).

Use harmonizeimagenet.lua to harmonize the train and validation sets:

th harmonizeimagenet.lua --dataPath /path/to/diskspace/ImageNet --progress --forReal

Each set will then contain a directory of images for each class with name class[id] where [id] is a class index, between 1 and 1000, used for the ILVRC2014 competition.

Then we need to install graphicsmagick :

luarocks install graphicsmagick

Inference

As in the famous (Krizhevsky et al. 2012) paper, the ImageNet training dataset samples images cropped from random 224x224 patches from the images resizes so that the smallest dimension has size 256. As for the validation set, ten 224x224 patches are cropped per image, i.e. center, four corners and their horizontal flips, and their predictions are averaged.

loadGBW

train, valid, test = dl.loadGBW(batchsize, [trainfile, datapath, srcurl, verbose])

Loads the Google Billion Words corpus as MultiSequence loaders. The preprocessing specified in Google Billion Words language modeling benchmark was applied to training-monolingual.tokenized/news.20??.en.shuffled.tokenized to generate the different subsets. These subsets are automatically downloaded when not found on disk. The task consists in predicting the next word given the previous ones. The corpus contains approximately 30 million sentences of an average length of about 25 words. In total, there are about 800 thousand (unique) words in the vocabulary, which makes it a very memory intensive problem.

loadSentiment140

train, valid, test = dl.loadSentiment140([datapath, minfreq, seqlen, validratio, srcurl, progress])

Load & processing training data.	
Number of tweets: 1600000	
Vocabulary size: 155723	
Number of occurences replaced with <OOV> token: 750575	
Tweet corpus size (in number of tokens): 20061241	
trainset set processed in 28.306740999222s

Load the Sentiment140 dataset. This dataset can be used for sentiment analysis for microblogging websites like Twitter. The task is to predict the sentiment of a tweet. The input is a sequence of tokenized words with a default maximum sequence length of 50 (i.e. seqlen=50). Targets can be one of three classes that map to the sentiment of the tweet: 1 = negative, 2 = neutral, 3 = positive. The neutral tweets are not present in the training data hence we ignore them from all (train, valid & test) datasets. This results in a 2-class (1=Negative, 2=Positive) dataset. Tweets are tokenized using the twitter/twokenize.py script. By default, only words with at least 3 occurrences (i.e. minfreq=3) in the training set are kept. The dataset is automatically downloaded from srcurl, tokenized and parsed into a tensor the first time the loader is used. The returned training, validation and test sets are encapsulated using the TensorLoader. The input is padded with zeros before the tweet when it is shorted than seqlen.

The above is only printed when progress=true (the default) the first time the loader is invoked. The processed data is subsequently cached to speedup future loadings. To overwrite any cached data use dl.overwrite=true.

fitImageNormalize

ppf = dl.fitImageNormalize(trainset, [nsample, cachepath, verbose])

Returns a ppf preprocessing function that can be used to in-place normalize a batch of images (inputs) channel-wise :

ppf(inputs)

The trainset argument is a DataLoader instance containing image inputs. The mean and standard deviation will be measured on nsample images (default 10000). When cachepath is provided, the mean and standard deviation are saved for the next function call.