PyTorch implementation of Depthwise Separable Convolution
Project description
Unofficial PyTorch Module - Depthwise Separable Convolution
An illustration of Depthwise Separable Convolution. Credit: Depthwise Convolution Is All You Need for Learning Multiple Visual Domains.
PyTorch (unofficial) implementation of Depthwise Separable Convolution. This type of convolution is introduced by Chollet in Xception: Deep Learning With Depthwise Separable Convolutions. This package provides SeparableConv1d, SeparableConv2d, SeparableConv3d, LazySeparableConv1d, LazySeparableConv2d, and LazySeparableConv3d.
Installation
Install separableconv-torch using pip (require: Python >=3.7).
pip install separableconv-torch
Parameters
| Parameter | Description | Type |
|---|---|---|
| in_channels | Number of channels in the input image | int |
| out_channels | Number of channels produced by the convolution | int |
| kernel_size | Size of the convolving kernel | int or tuple |
| stride | Stride of the convolution. Default: 1 | int or tuple, optional |
| padding | Padding added to all four sides of the input. Default: 0 | int, tuple or str, optional |
| padding_mode | 'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros' |
string, optional |
| dilation | Spacing between kernel elements. Default: 1 | int or tuple, optional |
| depth_multiplier | The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to in_channels * depth_multiplier. Default: 1 |
int, optional |
| normalization_dw | depthwise convolution normalization. Default: 'bn' | str, optional |
| normalization_pw | pointwise convolution normalization. Default: 'bn' | str, optional |
| activation_dw | depthwise convolution activation. Default: torch.nn.ReLU |
Callable[..., torch.nn.Module], optional |
| activation_pw | pointwise convolution activation. Default: torch.nn.ReLU |
Callable[..., torch.nn.Module], optional |
| bias | If True, adds a learnable bias to the output. Default: True |
bool, optional |
Example Usage
For 1-dimensional case.
import torch
import separableconv.nn as nn
# set input
input = torch.randn(4, 10, 100)
# define model
m = nn.SeparableConv1d(10, 30, 3)
# process input through model
output = m(input)
For 2-dimensional case.
import torch
import separableconv.nn as nn
# set input
input = torch.randn(4, 10, 100, 100)
# define model
m = nn.SeparableConv2d(10, 30, 3)
# process input through model
output = m(input)
For 3-dimensional case.
import torch
import separableconv.nn as nn
# set input
input = torch.randn(4, 10, 100, 100, 100)
# define model
m = nn.SeparableConv3d(10, 30, 3)
# process input through model
output = m(input)
Stacked SeparableConv2d.
import torch
import separableconv.nn as nn
# set input
input = torch.randn(4, 3, 100, 100)
# define model
m = nn.Sequential(
nn.SeparableConv2d(3, 32, 3),
nn.SeparableConv2d(32, 64, 3),
nn.SeparableConv2d(64, 96, 3))
# process input through model
output = m(input)
For lazy 2-dimensional case.
import torch
import separableconv.nn as nn
# set input
input = torch.randn(4, 10, 100, 100)
# define model
m = nn.LazySeparableConv2d(30, 3)
# process input through model
output = m(input)
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