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Shape and dimension inference (Keras-like) for PyTorch layers and neural networks
| Date | Stars |
|---|---|
| 2026-07-31 | 570 |
| 2026-08-06 | 570 |
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growth rate 0.00%/day
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| Version | Docs | Tests | Coverage | Style | PyPI | Python | PyTorch | Docker |
|---------|------|-------|----------|-------|------|--------|---------|--------|
| [](https://github.com/szymonmaszke/torchlayers/releases) | [](https://szymonmaszke.github.io/torchlayers/) |  | [](https://codecov.io/gh/szymonmaszke/torchlayers) | [](https://codebeat.co/projects/github-com-szymonmaszke-torchlayers-master) | [](https://pypi.org/project/torchlayers/) | [](https://www.python.org/) | [](https://pytorch.org/) | [](https://hub.docker.com/r/szymonmaszke/torchlayers) |
[__torchlayers__](https://szymonmaszke.github.io/torchlayers/) is a library based on [__PyTorch__](https://pytorch.org/)
providing __automatic shape and dimensionality inference of `torch.nn` layers__ + additional
building blocks featured in current SOTA architectures (e.g. [Efficient-Net](https://arxiv.org/abs/1905.11946)).
Above requires no user intervention (except single call to `torchlayers.build`)
similarly to the one seen in [__Keras__](https://www.tensorflow.org/guide/keras).
### Main functionalities:
* __Shape inference__ for most of `torch.nn` module (__convolutional, recurrent, transformer, attention and linear layers__)
* __Dimensionality inference__ (e.g. `torchlayers.Conv` working as `torch.nn.Conv1d/2d/3d` based on `input shape`)
* __Shape inference of custom modules__ (see examples section)
* __Additional [Keras-like](https://www.tensorflow.org/guide/keras) layers__ (e.g. `torchlayers.Reshape` or `torchlayers.StandardNormalNoise`)
* __Additional SOTA layers__ mostly from ImageNet competitions
(e.g. [PolyNet](https://arxiv.org/abs/1608.06993),
[Squeeze-And-Excitation](https://arxiv.org/abs/1709.01507),
[StochasticDepth](www.arxiv.org/abs/1512.03385>))
* __Useful defaults__ (`"same"` padding and default `kernel_size=3` for `Conv`, dropout rates etc.)
* __Zero overhead and [torchscript](https://pytorch.org/docs/stable/jit.html) support__
__Keep in mind this library works almost exactly like PyTorch originally__.
What that means is you can use `Sequential`, __define your own networks of any complexity using
`torch.nn.Module`__, create new layers with shape inference etc.
_See below to get some intuition about library_.
# Examples
For full functionality please check [__torchlayers documentation__](https://szymonmaszke.github.io/torchlayers/).
Below examples should introduce all necessary concepts you should know.
## Basic classifier
__All__ `torch.nn` modules can be used through `torchlayers` and __each module with input shape__
will be appropriately modified with it's input inferable counterpart.
```python
import torchlayers as tl
class Classifier(tl.Module):
def __init__(self):
super().__init__()
self.conv1 = tl.Conv2d(64, kernel_size=6)
self.conv2 = tl.Conv2d(128, kernel_size=3)
self.conv3 = tl.Conv2d(256, kernel_size=3, padding=1)Excerpt of 13,812 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0840463260e32817, topic:deep-learning, topic:pytorch
matched fp:0840463260e32817, topic:inference