Package: dc1d
dc1d (DeformConv1d)A 1D implementation of a deformable convolutional layer implemented in pure Python in PyTorch. The code style is designed to imitate similar classes in PyTorch such astorch.nn.Conv1Dandtorchvision.ops.DeformConv2D.The motivation for creating this toolkit is as of 19/10/2022 there is no native 1D implementation of deformable convolution in the PyTorch library and no alternate library which is simple to install (requiring only a basic PyTorch installation with no additional compilation of c++ or cuda libraries). The implementation here is written entirely in Python and makes use oftorch.autogradfor backpropagation.RequirementsYou must install PyTorch. Follow the details on the website to install properly:https://pytorch.org/get-started/locally/.This package was most thoroughly tested on PyTorch 1.12.1 running CUDA 11.6 on Windows with Python version 3.8. It has also been tested on Ubuntu, Zorin OS and CentOS all running Python 3.8.InstallationPlease use the followingpipcommand to install for now.Most thoroughly tested version (only "reflect" padding mode is implemented on this version, please do not try to use any other):pip install dc1d==0.0.4Alternatively to install the latest version dopip install dc1dThe current version on this branch has not been tested for numerical accuracy but probably works fine. Gradient computation has been tested and seems okay.
To download direct from source dogit clone https://github.com/jwr1995/dc1d.git
cd dc1d
pip install .UsageExample of how to use the deformable convolutional layerDeformConv1d()with timing information.DeformConv1dis the deformable convolution layer designed to imitatetorch.nn.Conv1d.
Note:DeformConv1ddoes not compute the offset values used in itsforward(...)call. These most be computed outside the layer.import time
import torch
from torch import nn

# Import layer
from dc1d.nn import DeformConv1d

# Hyperparameters
batch_size = 16
in_channels = 512
out_channels = 512
kernel_size = 16
stride = 1
padding = "same"
dilation = 3
groups = 1
bias = True
length = 128

# Construct layer
model = DeformConv1d(
    in_channels = in_channels,
    out_channels = out_channels,
    kernel_size = kernel_size,
    stride = stride,
    padding = "same",
    dilation = dilation,
    groups = groups,
    bias = True,
)

# Generate input sequence
x = torch.rand(batch_size, in_channels, length,requires_grad=True)
print(x.shape)

# Generate offsets by first computing the desired output length
offsets = nn.Parameter(torch.ones(batch_size, 1, length, kernel_size, requires_grad=True))

# Process the input sequence and time it
start = time.time()
y = model(x, offsets)
end = time.time()

# Print output shape and time taken
print(y.shape)
print("Deformable runtime =",end-start)For more detailed examples, thennandopsmodules have example usage scripts appended to the bottom of the file inside theirif __name__ == "__main__":clauses. For example one could runpython dc1d/nn.pyto compare the runtime of ourDeformConv1dlayer againsttorch.nn.Conv1d.A class calledPackedConv1dalso exists indc1d.nnwhich computes the offsets using a depthwise-separable convolutional block as detailed in our paper below.PapersPlease cite the following if you use this package@INPROCEEDINGS{dtcn23,
  author={Ravenscroft, William and Goetze, Stefan and Hain, Thomas},
  booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation}, 
  year={2023},
  volume={},
  number={},
  pages={1-5},
  doi={10.1109/ICASSP49357.2023.10095230}}