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torchlayers is a library based on PyTorch
providing automatic shape and dimensionality inference of torch.nn layers + additional
building blocks featured in current SOTA architectures (e.g. Efficient-Net).
Above requires no user intervention (except single call to torchlayers.build)
similarly to the one seen in Keras.
Main functionalities:
- Shape inference for most of
torch.nnmodule (convolutional, recurrent, transformer, attention and linear layers) - Dimensionality inference (e.g.
torchlayers.Convworking astorch.nn.Conv1d/2d/3dbased oninput shape) - Shape inference of custom modules (see examples section)
- Additional Keras-like layers (e.g.
torchlayers.Reshapeortorchlayers.StandardNormalNoise) - Additional SOTA layers mostly from ImageNet competitions (e.g. PolyNet, Squeeze-And-Excitation, StochasticDepth)
- Useful defaults (
"same"padding and defaultkernel_size=3forConv, dropout rates etc.) - Zero overhead and torchscript 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. 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.
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)
# New layer, more on that in the next example
self.pooling = tl.GlobalMaxPool()
self.dense = tl.Linear(10)
def forward(self, x):
x = torch.relu(self.conv1(x))
x = torch.relu(self.conv2(x))
x = torch.relu(self.conv3(x))
return self.dense(self.pooling(x))
# Pass model and any example inputs afterwards
clf = tl.build(Classifier(), torch.randn(1, 3, 32, 32))
Above torchlayers.Linear(out_features=10) is used. It is "equivalent" to
original PyTorch's torch.nn.Linear(in_features=?, out_features=10) where in_features
will be inferred from example input input during torchlayers.build call.
Same thing happens with torch.nn.Conv2d(in_channels, out_channels, kernel_size, ...)
which can be replaced directly by tl.Conv2d(out_channels, kernel_size, ...).
Just remember to pass example input through the network!
Simple image and text classifier in one!
- We will use single "model" for both tasks.
Firstly let's define it using
torch.nnandtorchlayers:
import torch
import torchlayers as tl
# torch.nn and torchlayers can be mixed easily
model = torch.nn.Sequential(
tl.Conv(64), # specify ONLY out_channels
torch.nn.ReLU(), # use torch.nn wherever you wish
tl.BatchNorm(), # BatchNormNd inferred from input
tl.Conv(128), # Default kernel_size equal to 3
tl.ReLU(),
tl.Conv(256, kernel_size=11), # "same" padding as default
tl.GlobalMaxPool(), # Known from Keras
tl.Linear(10), # Output for 10 classes
)
print(model)
Above would give you model's summary like this (notice question marks for not yet inferred values):
Sequential(
(0): Conv(in_channels=?, out_channels=64, kernel_size=3, stride=1, padding=same, dilation=1, groups=1, bias=True, padding_mode=zeros)
(1): ReLU()
(2): BatchNorm(num_features=?, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): Conv(in_channels=?, out_channels=128, kernel_size=3, stride=1, padding=same, dilation=1, groups=1, bias=True, padding_mode=zeros)
(4): ReLU()
(5): Conv(in_channels=?, out_channels=256, kernel_size=11, stride=1, padding=same, dilation=1, groups=1, bias=True, padding_mode=zeros)
(6): GlobalMaxPool()
(7): Linear(in_features=?, out_features=10, bias=True)
)
- Now you can build/instantiate your model with example input (in this case MNIST-like):
mnist_model = tl.build(model, torch.randn(1, 3, 28, 28))
- Or if it's text classification you are after, same model could be built with different
input shape(e.g. for text classification using300dimensional pretrained embedding):
# [batch, embedding, timesteps], first dimension > 1 for BatchNorm1d to work
text_model = tl.build(model, torch.randn(2, 300, 1))
- Finally, you can
printboth models after instantiation, provided below side by-side for readability (notice different dimenstionality, e.g.Conv2dvsConv1daftertorchlayers.build):
# TEXT CLASSIFIER MNIST CLASSIFIER
Sequential( Sequential(
(0): Conv1d(300, 64) (0): Conv2d(3, 64)
(1): ReLU() (1): ReLU()
(2): BatchNorm1d(64) (2): BatchNorm2d(64)
(3): Conv1d(64, 128) (3): Conv2d(64, 128)
(4): ReLU() (4): ReLU()
(5): Conv1d(128, 256) (5): Conv2d(128, 256)
(6): GlobalMaxPool() (6): GlobalMaxPool()
(7): Linear(256, 10) (7): Linear(256, 10)
) )
As you can see both modules "compiled" into original pytorch layers.
Custom modules with shape inference capabilities
User can define any module and make it shape inferable with torchlayers.infer
function:
# Class defined with in_features
# It might be a good practice to use _ prefix and Impl as postfix
# to differentiate from shape inferable version
class _MyLinearImpl(torch.nn.Module):
def __init__(self, in_features: int, out_features: int):
super().__init__()
self.weight = torch.nn.Parameter(torch.randn(out_features, in_features))
self.bias = torch.nn.Parameter(torch.randn(out_features))
def forward(self, inputs):
return torch.nn.functional.linear(inputs, self.weight, self.bias)
MyLinear = tl.infer(_MyLinearImpl)
# Build and use just like any other layer in this library
layer =tl.build(MyLinear(out_features=32), torch.randn(1, 64))
layer(torch.randn(1, 64))
By default inputs.shape[1] will be used as in_features value
during initial forward pass. If you wish to use different index (e.g. to infer using
inputs.shape[3]) use MyLayer = tl.infer(_MyLayerImpl, index=3) as a decorator.
Autoencoder with inverted residual bottleneck and pixel shuffle
Please check code comments and documentation if needed. If you are unsure what autoencoder is you could see this example blog post.
Below is a convolutional denoising autoencoder example for ImageNet-like images.
Think of it like a demonstration of capabilities of different layers
and building blocks provided by torchlayers.
# Input - 3 x 256 x 256 for ImageNet reconstruction
class AutoEncoder(torch.nn.Module):
def __init__(self):
super().__init__()
self.encoder = tl.Sequential(
tl.StandardNormalNoise(), # Apply noise to input images
tl.Conv(64, kernel_size=7),
tl.activations.Swish(), # Direct access to module .activations
tl.InvertedResidualBottleneck(squeeze_excitation=False),
tl.AvgPool(), # shape 64 x 128 x 128, kernel_size=2 by default
tl.HardSwish(), # Access simply through tl
tl.SeparableConv(128), # Up number of channels to 128
tl.InvertedResidualBottleneck(), # Default with squeeze excitation
torch.nn.ReLU(),
tl.AvgPool(), # shape 128 x 64 x 64, kernel_size=2 by default
tl.DepthwiseConv(256), # DepthwiseConv easier to use
# Pass input thrice through the same weights like in PolyNet
tl.Poly(tl.InvertedResidualBottleneck(), order=3),
tl.ReLU(), # all torch.nn can be accessed via tl
tl.MaxPool(), # shape 256 x 32 x 32
tl.Fire(out_channels=512), # shape 512 x 32 x 32
tl.SqueezeExcitation(hidden=64),
tl.InvertedResidualBottleneck(),
tl.MaxPool(), # shape 512 x 16 x 16
tl.InvertedResidualBottleneck(squeeze_excitation=False),
# Randomly switch off the last two layers with 0.5 probability
tl.StochasticDepth(
torch.nn.Sequential(
tl.InvertedResidualBottleneck(squeeze_excitation=False),
tl.InvertedResidualBottleneck(squeeze_excitation=False),
),
p=0.5,
),
tl.AvgPool(), # shape 512 x 8 x 8
)
# This one is more "standard"
self.decoder = tl.Sequential(
tl.Poly(tl.InvertedResidualBottleneck(), order=2),
# Has ICNR initialization by default after calling `build`
tl.ConvPixelShuffle(out_channels=512, upscale_factor=2),
# Shape 512 x 16 x 16 after PixelShuffle
tl.Poly(tl.InvertedResidualBottleneck(), order=3),
tl.ConvPixelShuffle(out_channels=256, upscale_factor=2),
# Shape 256 x 32 x 32
tl.Poly(tl.InvertedResidualBottleneck(), order=3),
tl.ConvPixelShuffle(out_channels=128, upscale_factor=2),
# Shape 128 x 64 x 64
tl.Poly(tl.InvertedResidualBottleneck(), order=4),
tl.ConvPixelShuffle(out_channels=64, upscale_factor=2),
# Shape 64 x 128 x 128
tl.InvertedResidualBottleneck(),
tl.Conv(256),
tl.Dropout(), # Defaults to 0.5 and Dropout2d for images
tl.Swish(),
tl.InstanceNorm(),
tl.ConvPixelShuffle(out_channels=32, upscale_factor=2),
# Shape 32 x 256 x 256
tl.Conv(16),
tl.Swish(),
tl.Conv(3),
# Shape 3 x 256 x 256
)
def forward(self, inputs):
return self.decoder(self.encoder(inputs))
Now one can instantiate the module and use it with torch.nn.MSELoss as per usual.
autoencoder = tl.build(AutoEncoder(), torch.randn(1, 3, 256, 256))
Installation
pip
Latest release:
pip install --user torchlayers
Nightly:
pip install --user torchlayers-nightly
Docker
CPU standalone and various versions of GPU enabled images are available at dockerhub.
For CPU quickstart, issue:
docker pull szymonmaszke/torchlayers:18.04
Nightly builds are also available, just prefix tag with nightly_. If you are going for GPU image make sure you have
nvidia/docker installed and it's runtime set.
Contributing
If you find issue or would like to see some functionality (or implement one), please open new Issue or create Pull Request.
1 maintainer
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