datatia 0.4.1

Opinionated declarative utility library for writing PyTorch dataset classes

What is it?

Opinionated declarative utility library for writing dataset classes. Intended for small pytorch experiments.

Rationale

Pytorch dataset loading often involves certain common tasks:

• Load tensors or values from a filelist
• Truncate sequence/spatial dims to a maximum length
• Drop items that don't satisfy particular requirements
• Pad sequence/spatial dims to a multiple of a number or a maximum per-batch length
• Pad sequence/spatial dims in groups across multiple data fields in a batch
• Or (on training datasets only) randomly subsample sequence/spatial dims to meet a maximum length constraint, and add a "length" field for the pre-padding lengths
• Apply data augmentations

Implementing these tasks is often highly repetitive and error prone.

Dataset loading code can be further simplified by making certain assumptions:

• All data to be loaded takes the form of either a literal or a single file tensor which can be loaded from disk.
• Each dataset class takes only one filelist.
• Filelists contain only paths to tensors or python literals (such as class IDs).

Example usage

We use an example a 3-column dataset specified as a filelist:

# filelist.txt
test/test_files/tensor1_0.pt|test/test_files/tensor2_0.pt|0
test/test_files/tensor1_1.pt|test/test_files/tensor2_1.pt|1
test/test_files/tensor1_2.pt|test/test_files/tensor2_2.pt|2

When creating a dt.Dataset we specify names and datatypes for the columns in order.

import datatia as dt
dataset = dt.Dataset(filelist='filelist.txt',
    field_specs=[
        dt.FieldSpec(name='tensor1', datatype=torch.Tensor),
        dt.FieldSpec(name='tensor2', datatype=torch.Tensor),
        dt.FieldSpec(name='id', datatype=int),
    ],
    actions=[dt.PadGroup(fields=['tensor1', 'tensor2'], 
        dims=[0, 1], values=[0, 0], to_multiple=[4, 5])])
loader = dataset.loader(batch_size=4)
batch = next(iter(loader))

The PadGroup action pads dimensions within a group of tensor columns for a batch to either the next largest common multiple of a number (to_multiple), to a fixed length (to_length), or to the maximum size of the dimensions within the batch.

See datatia/actions.py for other actions (Truncate, Drop, PreMap, LiveMapRow, RandomSubsample, PadGroup) and datatia/datatia.py for FieldSpec and dt.Dataset API.