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bcos-0.0.4
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مشاهده بیشترتوضیحات
B-cos models.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | bcos-0.0.4 |
| نام | bcos |
| نسخه کتابخانه | 0.0.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | Moritz Böhle <mboehle@mpi-inf.mpg.de>, Navdeeppal Singh <npsingh0181@gmail.com> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/bcos/ |
| مجوز | Apache License 2.0 |
# B-cos Networks v2
M. Böhle, N. Singh, M. Fritz, B. Schiele.
Improved B-cos Networks.
# Table of Contents
- [Introduction](#introduction)
- [Quick Start](#quick-start)
- [Installation](#installation)
- [`bcos` Package](#bcos-package)
- [Training Environment Setup](#training-environment-setup)
- [Setting Data Paths](#setting-data-paths)
- [Usage](#usage)
- [Evaluation](#evaluation)
- [Training](#training)
- [Model Zoo](#model-zoo)
- [License](#license)
# Introduction
This repository contains the code for the B-cos v2 models. (_Paper coming soon!_)
These models are more efficient and easier to train than the original B-cos models. Furthermore, we make a
large number of pretrained B-cos models available for use.
If you want to take a quick look at the explanations the models generate,
you can try out the Gradio web demo on [](https://huggingface.co/spaces/nps1ngh/B-cos).
If you prefer a more hands-on approach,
you can take a look at the [demo notebook on `Colab`](https://colab.research.google.com/drive/1bdc1zdIVvv7XUJj8B8Toe6VMPYAsIT9w?usp=sharing)
or load the models directly via `torch.hub` as explained below.
If you simply want to copy the model definitions, we provide a minimal, single-file reference implementation including
explanation mode in [`extra/minimal_bcos_resnet.py`](extra/minimal_bcos_resnet.py)!
# Quick Start
You only need to make sure you have `torch` and `torchvision` installed.
Then, loading the models via `torch.hub` is as easy as:
```python
import torch
# list all available models
torch.hub.list('B-cos/B-cos-v2')
# load a pretrained model
model = torch.hub.load('B-cos/B-cos-v2', 'resnet50', pretrained=True)
```
Inference and explanation visualization is as simple as:
```python
from PIL import Image
import matplotlib.pyplot as plt
# load image
img = model.transform(Image.open('cat.jpg'))
img = img[None].requires_grad_()
# predict and explain
model.eval()
expl_out = model.explain(img)
print("Prediction:", expl_out["prediction"]) # predicted class idx
plt.imshow(expl_out["explanation"])
plt.show()
```
Each of the models has its inference transform attached to it, accessible via `model.transform`.
Furthermore, each model has a `.explain()` method that takes an image tensor and returns a dictionary
containing the prediction and the explanation, and some extras.
<!-- TODO: add link to docs if end up making a site for them -->
See the [demo notebook](https://colab.research.google.com/drive/1bdc1zdIVvv7XUJj8B8Toe6VMPYAsIT9w?usp=sharing)
for more details on the `.explain()` method.
Furthermore, each model has a `get_classifier` and `get_feature_extractor` method that return the
classifier and feature extractor modules respectively. These can useful for fine-tuning the models!
<!-- =============================================================================================================== -->
# Installation
Depending on your use case, you can either install the `bcos` package
or set up the development environment for training the models (for your custom models or for reproducing the results).
## `bcos` Package
If you are simply interested in using the models (pretrained or otherwise),
then we provide a `bcos` package that can be installed via `pip`:
```bash
pip install bcos
```
This contains the models, their modules, transforms, and other utilities
making it easy to use and build B-cos models.
Take a look at the public API [here](bcos/__init__.py).
(I'll add a proper docs site if I have time or there's enough interest.
Nonetheless, I have tried to keep the code well-documented, so it should be easy to follow.)
## Training Environment Setup
If you want to train your own B-cos models using this repository or are interested in reproducing the results,
you can set up the development environment as follows:
Using `conda` (recommended, especially if you want to reproduce the results):
```bash
conda env create -f environment.yml
conda activate bcos
```
Using `pip`
```bash
pip install -r requirements-train.txt
```
### Setting Data Paths
You can either set the paths in [`bcos/settings.py`](bcos/settings.py) or set the environment variables
1. `DATA_ROOT`
2. `IMAGENET_PATH`
to the paths of the data directories.
The `DATA_ROOT` environment variable should point to the data root directory for CIFAR-10
(will be automatically downloaded).
For ImageNet, the `IMAGENET_PATH` environment variable should point to the directory containing
the `train` and `val` directories.
<!-- =============================================================================================================== -->
# Usage
For the `bcos` package, as mentioned earlier, take a look at the public API [here](bcos/__init__.py).
For evaluating or training the models, you can use the `evaluate.py` and `train.py` scripts, as follows:
## Evaluation
You can use evaluate the accuracy of the models on the ImageNet validation set using:
```bash
python evaluate.py --dataset ImageNet --hubconf resnet18
```
This will download the model from `torch.hub` and evaluate it on the ImageNet validation set.
The default batch size is 1, but you can change it using the `--batch-size` argument.
Replace `resnet18` with any of the other models listed in [Model Zoo](#model-zoo) that you wish to evaluate.
## Training
Short version:
```bash
python train.py \
--dataset ImageNet \
--base_network bcos_final \
--experiment_name resnet18
```
Long version: See [TRAINING.md](TRAINING.md) for more details on
how the setup works and how to train your own models.
# Model Zoo
Here are the ImageNet pre-trained models available in the model zoo.
You can find the links to the model weights below
(uploaded to the [`Weights` GitHub release](https://github.com/B-cos/B-cos-v2/releases/tag/v0.0.1-weights)).
| Model/Entrypoint | Top-1 Accuracy | Top-5 Accuracy | #Params | Download |
|---------------------|----------------|----------------|---------|--------------------------------------------------------------------------------------------------------------|
| `resnet18` | 68.736% | 87.430% | 11.69M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/resnet_18-68b4160fff.pth) |
| `resnet34` | 72.284% | 90.052% | 21.80M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/resnet_34-a63425a03e.pth) |
| `resnet50` | 75.882% | 92.528% | 25.52M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/resnet_50-ead259efe4.pth) |
| `resnet101` | 76.532% | 92.538% | 44.50M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/resnet_101-84c3658278.pth) |
| `resnet152` | 76.484% | 92.398% | 60.13M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/resnet_152-42051a77c1.pth) |
| `resnext50_32x4d` | 75.820% | 91.810% | 25.00M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/resnext_50_32x4d-57af241ab9.pth) |
| `densenet121` | 73.612% | 91.106% | 7.95M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/densenet_121-b8daf96afb.pth) |
| `densenet161` | 76.622% | 92.554% | 28.58M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/densenet_161-9e9ea51353.pth) |
| `densenet169` | 75.186% | 91.786% | 14.08M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/densenet_169-7037ee0604.pth) |
| `densenet201` | 75.480% | 91.992% | 19.91M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/densenet_201-00ac87066f.pth) |
| `vgg11_bnu` | 69.310% | 88.388% | 132.86M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/vgg_11_bnu-34036029f0.pth) |
| | | | | |
| `convnext_tiny` | 77.488% | 93.192% | 28.54M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/convnext_tiny_pn-539b1bfb37.pth) |
| `convnext_base` | 79.650% | 94.614% | 88.47M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/convnext_base_pn-b0495852c6.pth) |
| `convnext_tiny_bnu` | 76.826% | 93.090% | 28.54M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/convnext_tiny_bnu-dbd7f5ef9d.pth) |
| `convnext_base_bnu` | 80.142% | 94.834% | 88.47M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/convnext_base_bnu-7c32a704b3.pth) |
| `densenet121_long` | 77.302% | 93.234% | 7.95M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/densenet_121_long-5175461597.pth) |
| `resnet50_long` | 79.468% | 94.452% | 25.52M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/resnet_50_long-ef38a88533.pth) |
| `resnet152_long` | 80.144% | 94.116% | 60.13M | [link](https://github.com/B-cos/B-cos-v2/releases/download/v0.0.1-weights/resnet_152_long-0b4b434939.pth) |
We'll add ViT models to the model zoo soon, stay tuned!
You can find these entrypoints in [`bcos/models/pretrained.py`](bcos/models/pretrained.py).
# License
This repository's code is licensed under the Apache License 2.0
which you can find in the [LICENSE](./LICENSE) file.
The pre-trained models are trained on ImageNet (and are hence derived from it), which is
licensed under the [ImageNet Terms of access](https://image-net.org/download),
which among others things, only allows non-commercial use of the dataset.
It is therefore your responsibility to check whether you have permission to use the
pre-trained models for *your* use case.
نحوه نصب
نصب پکیج whl bcos-0.0.4:
pip install bcos-0.0.4.whl
نصب پکیج tar.gz bcos-0.0.4:
pip install bcos-0.0.4.tar.gz