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dc1d-0.0.4
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A PyTorch implementation of 1D deformable convolution
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dc1d-0.0.4 |
| نام | dc1d |
| نسخه کتابخانه | 0.0.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | William Ravenscroft <jwravenscroft1@sheffield.ac.uk> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/dc1d/ |
| مجوز | MIT License Copyright (c) 2022 William Ravenscroft Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
# dc1d (DeformConv1d)
An 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 as ```torch.nn.Conv1D``` and ```torchvision.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 of ```torch.autograd``` for backpropagation.
# Requirements
You 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.
# Installation
```
pip install dc1d
```
or
```
git clone https://github.com/jwr1995/dc1d.git
cd dc1d
pip install .
```
# Usage
## Example of how to use the deformable convolutional layer ```DeformConv1d()``` with timing information.
```DeformConv1d``` is the deformable convolution layer designed to imitate ```torch.nn.Conv1d```.
Note: ```DeformConv1d``` does not compute the offset values used in its ```forward(...)``` call. These most be computed outside the layer.
```
import time
# 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,
device="cuda"
)
# Generate input sequence
x = torch.rand(batch_size, in_channels, length,requires_grad=True).cuda()
print(x.shape)
# Generate offsets by first computing the desired output length
output_length = x.shape[-1]-dilation*(kernel_size-1)
offsets = nn.Parameter(torch.ones(batch_size, 1, output_length, kernel_size, requires_grad=True, device="cuda"))
# 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, the ```nn``` and ```ops``` modules have example usage scripts appended to the bottom of the file inside their ```if __name__ == "__main__":``` clauses. For example one could run
```
python dc1d/nn.py
```
to compare the runtime of our ```DeformConv1d``` layer against ```torch.nn.Conv1d```.
A class called ```PackedConv1d``` also exists in ```dc1d.nn``` which computes the offsets using a depthwise-separable convolutional block as detailed in our paper below.
# Papers
Please cite the following if you use this package
```
@misc{ravenscroft2022dtcn,
doi = {10.48550/ARXIV.2210.15305},
url = {https://arxiv.org/abs/2210.15305},
author = {Ravenscroft, William and Goetze, Stefan and Hain, Thomas},
title = {Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl dc1d-0.0.4:
pip install dc1d-0.0.4.whl
نصب پکیج tar.gz dc1d-0.0.4:
pip install dc1d-0.0.4.tar.gz