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ACSConv-0.1.1
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[IEEE JBHI] Reinventing 2D Convolutions for 3D Images
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ACSConv-0.1.1 |
| نام | ACSConv |
| نسخه کتابخانه | 0.1.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jiancheng Yang and Xiaoyang Huang |
| ایمیل نویسنده | jekyll4168@sjtu.edu.cn |
| آدرس صفحه اصلی | https://github.com/M3DV/ACSConv |
| آدرس اینترنتی | https://pypi.org/project/ACSConv/ |
| مجوز | Apache-2.0 License |
# ACSConv
Reinventing 2D Convolutions for 3D Images ([arXiv](https://arxiv.org/abs/1911.10477))
IEEE Journal of Biomedical and Health Informatics (IEEE JBHI), 2021 ([DOI](http://doi.org/10.1109/JBHI.2021.3049452))
**News**:
- 2022.01.26 - ACS [ConvNeXt](acsconv/models/convnext.py) supported.
- 2021.12.17 - torch 1.10 supported & pip installation supported.
- 2021.4.19 - torch 1.8 supported.
## Key contributions
* ACS convolution aims at a **plug-and-play replacement** of standard 3D convolution, for 3D medical images.
* ACS convolution enables **2D-to-3D transfer learning**, which consistently provides significant performance boost in our experiments.
* Even without pretraining, ACS convolution is **comparable to or even better than** 3D convolution, with **smaller model size** and **less computation**.
## Package Installation
If you want to use this class, you have two options:
A) Install ACSConv as a standard Python package from PyPI:
```bash
pip install ACSConv
```
B) Simply copy and paste it in your project;
You could run the `test.py` to validate the installation. (If you want to test the validity of pip installation, please move this `test.py` file outside of this git project directory, otherwise it is testing the code inside the project instead of pip installation.)
## Requirements
### PyTorch requirements
```python
torch>=1.0.0 and torch<=1.10.0
```
You can install it on the [official homepage](https://pytorch.org/docs/stable/index.html).
### Other requirements
All libraries needed to run the included experiments (base requirements included).
```python
fire
jupyterlab
matplotlib
pandas
tqdm
sklearn
tensorboardx
```
## Code structure
* ``acsconv``
the core implementation of ACS convolution, including the operators, models, and 2D-to-3D/ACS model converters.
* ``operators``: include ACSConv, SoftACSConv and Conv2_5d.
* ``converters``: include converters which convert 2D models to 3d/ACS/Conv2_5d counterparts.
* ``models``: Native ACS models.
* ``experiments``
the scripts to run experiments.
* ``mylib``: the lib for running the experiments.
* ``poc``: the scripts to run proof-of-concept experiments.
* ``lidc``: the scripts to run LIDC-IDRI experiments.
## Convert a 2D model into 3D with a single line of code
```python
import torch
from torchvision.models import resnet18
from acsconv.converters import ACSConverter
# model_2d is a standard pytorch 2D model
model_2d = resnet18(pretrained=True)
B, C_in, H, W = (1, 3, 64, 64)
input_2d = torch.rand(B, C_in, H, W)
output_2d = model_2d(input_2d)
model_3d = ACSConverter(model_2d)
# once converted, model_3d is using ACSConv and capable of processing 3D volumes.
B, C_in, D, H, W = (1, 3, 64, 64, 64)
input_3d = torch.rand(B, C_in, D, H, W)
output_3d = model_3d(input_3d)
```
## Usage of ACS operators
```python
import torch
from acsconv.operators import ACSConv, SoftACSConv
B, C_in, D, H, W = (1, 3, 64, 64, 64)
x = torch.rand(B, C_in, D, H, W)
# ACSConv to process 3D volumnes
conv = ACSConv(in_channels=3, out_channels=10, kernel_size=3, padding=1)
out = conv(x)
# SoftACSConv to process 3D volumnes
conv = SoftACSConv(in_channels=3, out_channels=10, kernel_size=3, padding=1)
out = conv(x)
```
## Usage of native ACS models
```python
import torch
from acsconv.models.acsunet import ACSUNet
unet_3d = ACSUNet(num_classes=3)
B, C_in, D, H, W = (1, 1, 64, 64, 64)
input_3d = torch.rand(B, C_in, D, H, W)
output_3d = unet_3d(input_3d)
```
## How to run the experiments
* [Proof-of-Concept Segmentation](./experiments/poc/README.md)
* [Lung Nodule Classification and Segmentation](./experiments/lidc/README.md)
* ...
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6.0 | Python |
نحوه نصب
نصب پکیج whl ACSConv-0.1.1:
pip install ACSConv-0.1.1.whl
نصب پکیج tar.gz ACSConv-0.1.1:
pip install ACSConv-0.1.1.tar.gz