Simple dataset to dataloader library for pytorch
Project description
This is a simple library for creating readable dataset pipelines and reusing best practices for issues such as imbalanced datasets. There are just two components to keep track of: Dataset and Datastream.
Dataset is a simple mapping between an index and an example. It provides pipelining of functions in a readable syntax originally adapted from tensorflow 2’s tf.data.Dataset.
Datastream combines a Dataset and a sampler into a stream of examples. It provides a simple solution to oversampling / stratification, weighted sampling, and finally converting to a torch.utils.data.DataLoader.
Install
pip install pytorch-datastream
Usage
The list below is meant to showcase functions that are useful in most standard and non-standard cases. It is not meant to be an exhaustive list. See the documentation for a more extensive list on API and usage.
Dataset.from_subscriptable
Dataset.from_dataframe
Dataset
.map
.subset
.split
.cache
.with_columns
Datastream.merge
Datastream.zip
Datastream
.map
.data_loader
.zip_index
.update_weights_
.update_example_weight_
.weight
.state_dict
.load_state_dict
Merge / stratify / oversample datastreams
The fruit datastreams given below repeatedly yields the string of its fruit type.
>>> datastream = Datastream.merge([
... (apple_datastream, 2),
... (pear_datastream, 1),
... (banana_datastream, 1),
... ])
>>> next(iter(datastream.data_loader(batch_size=8)))
['apple', 'apple', 'pear', 'banana', 'apple', 'apple', 'pear', 'banana']
Zip independently sampled datastreams
The fruit datastreams given below repeatedly yields the string of its fruit type.
>>> datastream = Datastream.zip([
... apple_datastream,
... Datastream.merge([pear_datastream, banana_datastream]),
... ])
>>> next(iter(datastream.data_loader(batch_size=4)))
[('apple', 'pear'), ('apple', 'banana'), ('apple', 'pear'), ('apple', 'banana')]
More usage examples
See the documentation for more usage examples.
Install from source
To patch the code locally for Python 3.6 run patch-python3.6.sh.
$ ./patch-python3.6.sh
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
Hashes for pytorch_datastream-0.4.1-py38-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fc408b68a86fd623e6183e5caa1eef613ef8b3e6b7988dd45efead67a06aab8a |
|
MD5 | 2c32b3231db0e3751dda1ccfe7b0f213 |
|
BLAKE2b-256 | 86c60beefef6c515a80d8c89c9ef8a26ca7f2294d390fd22d81cd846cf11d74e |
Hashes for pytorch_datastream-0.4.1-py37-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ad14370e3e9590c8b28ae8894cbfd4f56fbba31aa81cde880404c4a13b989a05 |
|
MD5 | 8b40ff956a17b554c2a62fc341ba483f |
|
BLAKE2b-256 | e046ae79b0bebf090ca70719032141e158fc6e929cf7c219bfed2171ae4f6f42 |
Hashes for pytorch_datastream-0.4.1-py36-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8a2c6e4683bb2192c07c4dd0bc7a2f9d7517b909807b0e05bc4fb8e9d9bf9296 |
|
MD5 | a9a760403db03043f7b3f9cc08af15d9 |
|
BLAKE2b-256 | f6201af79d3dd8180fe99efb8cbaa1b78af5d1fa689fd71ac48e610c08673d3f |