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https://github.com/huangyxi/aprint
A Python printing package for those who don't care about the actual elements of array-like data but the types and shapes within a complex data structure
https://github.com/huangyxi/aprint
machine-learning machine-learning-library numpy pretty pretty-print print pytorch
Last synced: 13 days ago
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A Python printing package for those who don't care about the actual elements of array-like data but the types and shapes within a complex data structure
- Host: GitHub
- URL: https://github.com/huangyxi/aprint
- Owner: huangyxi
- License: mit
- Created: 2023-09-05T10:00:43.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-01T11:29:31.000Z (about 1 year ago)
- Last Synced: 2024-11-16T01:42:51.183Z (about 1 month ago)
- Topics: machine-learning, machine-learning-library, numpy, pretty, pretty-print, print, pytorch
- Language: Python
- Homepage: https://pypi.org/project/aprint/
- Size: 13.7 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# aprint
A Python print package for those who don't care the actual elements of array-like data (e.g. `numpy.array`, `torch.tensor`) within a complex data structure.
## Background
By default, `print` will display the actual elements in array-like data, such as `numpy.array` or `torch.tensor`, which is not very friendly for those who don't care about the actual elements.
Inspired by the leading scientific computing language [Julia](https://julialang.org/), this package will print the **type and shape** of the data structure, as well as the **type and shape** of the elements within the data structure.
## Installation
```
pip install aprint
```
For the latest version, you can install from github:
```
pip install git+https://github.com/huangyxi/aprint.git
```
## Usage
```python
>>> import numpy as np
>>> import torch
>>> from aprint import aprint
>>> aprint(np.array([1, 2, 3]))
int64[3] numpy.ndarray
>>> aprint({'a': torch.zeros(16, 32).cuda(), 2: [np.array([1, 2]), (torch.zeros(2).to_sparse(), torch.zeros(2,3).to_sparse_csr(), torch.zeros(2,3,4).to_sparse_csc()), {'5', 6, 7.}]}, indent_str='| ')
{ # dict with len=2
| 'a': float32[16×32] torch.Tensor at 'cuda:0' with grad=False,
| 2: [ # list with len=3
| | int64[2] numpy.ndarray,
| | ( # tuple with len=3
| | | float32[2] torch.Tensor at 'cpu' with layout='COO', grad=False,
| | | float32[2×3] torch.Tensor at 'cpu' with layout='CSR', grad=False,
| | | float32[2×3×4] torch.Tensor at 'cpu' with layout='CSC', grad=False,
| | ),
| | { # set with len=3
| | | '5',
| | | 6,
| | | 7.0,
| | },
| ],
}
>>> aprint([1, [2, [3, [4, [5]]]]], max_depth=3, indent_str="⋮ ") # default max_depth=5
[ # list with len=2
⋮ 1,
⋮ [ # list with len=2
⋮ ⋮ 2,
⋮ ⋮ [ # list with len=2
⋮ ⋮ ⋮ 3,
⋮ ⋮ ⋮ [ # list with len=2 ... ],
⋮ ⋮ ],
⋮ ],
]
>>> torch.tensor([1, 2]) | aprint # pipeline print (only for torch.Tensor and built-in iterators)
int64[2] torch.Tensor at 'cpu' with grad=False
```
## Roadmap
- [x] pipeline support for `torch.Tensor`
- [ ] continuous integration
- [ ] support class inheritance
- [x] support iterators' classes
- [ ] support objects' classes
- [ ] support more types
- [ ] `torch.nn.parameter.Parameter`
- [ ] colorful output
- [ ] glance of the data
## License
MIT License