%global _empty_manifest_terminate_build 0
Name:		python-ultimate-anatome
Version:	0.1.1
Release:	1
Summary:	Ἀνατομή (Anatome) is a PyTorch library to analyze representation of neural networks
License:	MIT
URL:		https://github.com/brando90/ultimate-anatome
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/d7/83/41e194e19878eea5cbd9647c5832fb94499307895365de0b2fba5e839f73/ultimate-anatome-0.1.1.tar.gz
BuildArch:	noarch

Requires:	python3-torch
Requires:	python3-torchvision
Requires:	python3-tqdm

%description
# anatome ![](https://github.com/moskomule/anatome/workflows/pytest/badge.svg)

Ἀνατομή is a PyTorch library to analyze internal representation of neural networks

This project is under active development and the codebase is subject to change.

Note the name of the pypi package is `ultimate-anatome` but the python import is done with
the original name `import anatome`.
Test via:
```angular2html
python -c "import anatome.my_utils as my_utils;my_utils.hello()"
python -c "import anatome.my_utils as my_utils;my_utils.my_anatome_test()"
```

Credit to original library: https://github.com/moskomule/anatome and consider citing both of us! Citation at the end of readme.

## Installation

If you are going to use a gpu the do this first before continuing 
(or check the offical website: https://pytorch.org/get-started/locally/):
```angular2html
pip3 install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
```
Otherwise, just doing the follwoing should work.
```
pip install ultimate-anatome
```

## Manual installation [for Development]

To use code first get the code from this repo (e.g. fork it on github):

```
git clone git@github.com:brando90/ultimate-anatome.git
```

Then install it in development mode in your python env with python >=3.9.
E.g. create your env with conda:

```
conda create -n ultimate_anatome_env python=3.9
conda activate ultimate_anatome_env
```

Then install code in edibable mode and all it's depedencies with pip in the currently activated conda environment:

```
pip install -e ~/ultimate-anatome
```

## Available Tools

### Representation Similarity

To measure the similarity of learned representation, `anatome.SimilarityHook` is a useful tool. Currently, the following
methods are implemented.

- [Raghu et al. NIPS2017 SVCCA](https://papers.nips.cc/paper/7188-svcca-singular-vector-canonical-correlation-analysis-for-deep-learning-dynamics-and-interpretability)
- [Marcos et al. NeurIPS2018 PWCCA](https://papers.nips.cc/paper/7815-insights-on-representational-similarity-in-neural-networks-with-canonical-correlation)
- [Kornblith et al. ICML2019 Linear CKA](http://proceedings.mlr.press/v97/kornblith19a.html)
- [Ding et al. arXiv Orthogonal Procrustes distance](https://arxiv.org/abs/2108.01661)

```python
import torch
from torchvision.models import resnet18
from anatome import DistanceHook
from anatome.my_utils import remove_hook

model = resnet18()
hook1 = DistanceHook(model, "layer3.0.conv1")
hook2 = DistanceHook(model, "layer3.0.conv2")
model.eval()
with torch.no_grad():
    model(torch.randn(128, 3, 224, 224))
# downsampling to (size, size) may be helpful
hook1.distance(hook2, size=8)
hook1.clear()
hook2.clear()
remove_hook(model, hook1)
remove_hook(model, hook2)
```

or to test do:
```python
from anatome.my_utils import my_anatome_test
my_anatome_test()
```

### Loss Landscape Visualization

- [Li et al. NeurIPS2018](https://papers.nips.cc/paper/7875-visualizing-the-loss-landscape-of-neural-nets)

```python
from anatome import landscape2d

x, y, z = landscape2d(resnet18(),
                      data,
                      F.cross_entropy,
                      x_range=(-1, 1),
                      y_range=(-1, 1),
                      step_size=0.1)
imshow(z)
```

![](assets/landscape2d.svg)
![](assets/landscape3d.svg)

### Fourier Analysis

- Yin et al. NeurIPS 2019 etc.,

```python
from anatome import fourier_map

map = fourier_map(resnet18(),
                  data,
                  F.cross_entropy,
                  norm=4)
imshow(map)
```

![](assets/fourier.svg)

## Citation

If you use this implementation in your research, please conser citing my version of anatome:
```
@software{miranda2021ultimate_anatome,
    author={Brando Miranda},
    title={Ultimate Anatome, the ultimate PyTorch library to analyze internal representation of neural networks},
    url={https://github.com/brando90/ultimate-anatome},
    year={2021}
}
```
and the original version:

```
@software{hataya2020anatome,
    author={Ryuichiro Hataya},
    title={anatome, a PyTorch library to analyze internal representation of neural networks},
    url={https://github.com/moskomule/anatome},
    year={2020}
}
```


# Related libraries:

- The original anatome of course https://github.com/moskomule/anatome
- https://github.com/AntixK/PyTorch-Model-Compare From the developers "A tiny package to compare two neural networks in PyTorch. There are many ways to compare two neural networks, but one robust and scalable way is using the Centered Kernel Alignment (CKA) metric, where the features of the networks are compared."
- Captum is trying to add support. Check it out here: https://github.com/pytorch/captum/issues/572#issuecomment-957306989
- The original svcca tutorials by Maithra et al https://github.com/google/svcca/tree/master/tutorials 
- if you know more create a pull request and add it here!




%package -n python3-ultimate-anatome
Summary:	Ἀνατομή (Anatome) is a PyTorch library to analyze representation of neural networks
Provides:	python-ultimate-anatome
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-ultimate-anatome
# anatome ![](https://github.com/moskomule/anatome/workflows/pytest/badge.svg)

Ἀνατομή is a PyTorch library to analyze internal representation of neural networks

This project is under active development and the codebase is subject to change.

Note the name of the pypi package is `ultimate-anatome` but the python import is done with
the original name `import anatome`.
Test via:
```angular2html
python -c "import anatome.my_utils as my_utils;my_utils.hello()"
python -c "import anatome.my_utils as my_utils;my_utils.my_anatome_test()"
```

Credit to original library: https://github.com/moskomule/anatome and consider citing both of us! Citation at the end of readme.

## Installation

If you are going to use a gpu the do this first before continuing 
(or check the offical website: https://pytorch.org/get-started/locally/):
```angular2html
pip3 install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
```
Otherwise, just doing the follwoing should work.
```
pip install ultimate-anatome
```

## Manual installation [for Development]

To use code first get the code from this repo (e.g. fork it on github):

```
git clone git@github.com:brando90/ultimate-anatome.git
```

Then install it in development mode in your python env with python >=3.9.
E.g. create your env with conda:

```
conda create -n ultimate_anatome_env python=3.9
conda activate ultimate_anatome_env
```

Then install code in edibable mode and all it's depedencies with pip in the currently activated conda environment:

```
pip install -e ~/ultimate-anatome
```

## Available Tools

### Representation Similarity

To measure the similarity of learned representation, `anatome.SimilarityHook` is a useful tool. Currently, the following
methods are implemented.

- [Raghu et al. NIPS2017 SVCCA](https://papers.nips.cc/paper/7188-svcca-singular-vector-canonical-correlation-analysis-for-deep-learning-dynamics-and-interpretability)
- [Marcos et al. NeurIPS2018 PWCCA](https://papers.nips.cc/paper/7815-insights-on-representational-similarity-in-neural-networks-with-canonical-correlation)
- [Kornblith et al. ICML2019 Linear CKA](http://proceedings.mlr.press/v97/kornblith19a.html)
- [Ding et al. arXiv Orthogonal Procrustes distance](https://arxiv.org/abs/2108.01661)

```python
import torch
from torchvision.models import resnet18
from anatome import DistanceHook
from anatome.my_utils import remove_hook

model = resnet18()
hook1 = DistanceHook(model, "layer3.0.conv1")
hook2 = DistanceHook(model, "layer3.0.conv2")
model.eval()
with torch.no_grad():
    model(torch.randn(128, 3, 224, 224))
# downsampling to (size, size) may be helpful
hook1.distance(hook2, size=8)
hook1.clear()
hook2.clear()
remove_hook(model, hook1)
remove_hook(model, hook2)
```

or to test do:
```python
from anatome.my_utils import my_anatome_test
my_anatome_test()
```

### Loss Landscape Visualization

- [Li et al. NeurIPS2018](https://papers.nips.cc/paper/7875-visualizing-the-loss-landscape-of-neural-nets)

```python
from anatome import landscape2d

x, y, z = landscape2d(resnet18(),
                      data,
                      F.cross_entropy,
                      x_range=(-1, 1),
                      y_range=(-1, 1),
                      step_size=0.1)
imshow(z)
```

![](assets/landscape2d.svg)
![](assets/landscape3d.svg)

### Fourier Analysis

- Yin et al. NeurIPS 2019 etc.,

```python
from anatome import fourier_map

map = fourier_map(resnet18(),
                  data,
                  F.cross_entropy,
                  norm=4)
imshow(map)
```

![](assets/fourier.svg)

## Citation

If you use this implementation in your research, please conser citing my version of anatome:
```
@software{miranda2021ultimate_anatome,
    author={Brando Miranda},
    title={Ultimate Anatome, the ultimate PyTorch library to analyze internal representation of neural networks},
    url={https://github.com/brando90/ultimate-anatome},
    year={2021}
}
```
and the original version:

```
@software{hataya2020anatome,
    author={Ryuichiro Hataya},
    title={anatome, a PyTorch library to analyze internal representation of neural networks},
    url={https://github.com/moskomule/anatome},
    year={2020}
}
```


# Related libraries:

- The original anatome of course https://github.com/moskomule/anatome
- https://github.com/AntixK/PyTorch-Model-Compare From the developers "A tiny package to compare two neural networks in PyTorch. There are many ways to compare two neural networks, but one robust and scalable way is using the Centered Kernel Alignment (CKA) metric, where the features of the networks are compared."
- Captum is trying to add support. Check it out here: https://github.com/pytorch/captum/issues/572#issuecomment-957306989
- The original svcca tutorials by Maithra et al https://github.com/google/svcca/tree/master/tutorials 
- if you know more create a pull request and add it here!




%package help
Summary:	Development documents and examples for ultimate-anatome
Provides:	python3-ultimate-anatome-doc
%description help
# anatome ![](https://github.com/moskomule/anatome/workflows/pytest/badge.svg)

Ἀνατομή is a PyTorch library to analyze internal representation of neural networks

This project is under active development and the codebase is subject to change.

Note the name of the pypi package is `ultimate-anatome` but the python import is done with
the original name `import anatome`.
Test via:
```angular2html
python -c "import anatome.my_utils as my_utils;my_utils.hello()"
python -c "import anatome.my_utils as my_utils;my_utils.my_anatome_test()"
```

Credit to original library: https://github.com/moskomule/anatome and consider citing both of us! Citation at the end of readme.

## Installation

If you are going to use a gpu the do this first before continuing 
(or check the offical website: https://pytorch.org/get-started/locally/):
```angular2html
pip3 install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
```
Otherwise, just doing the follwoing should work.
```
pip install ultimate-anatome
```

## Manual installation [for Development]

To use code first get the code from this repo (e.g. fork it on github):

```
git clone git@github.com:brando90/ultimate-anatome.git
```

Then install it in development mode in your python env with python >=3.9.
E.g. create your env with conda:

```
conda create -n ultimate_anatome_env python=3.9
conda activate ultimate_anatome_env
```

Then install code in edibable mode and all it's depedencies with pip in the currently activated conda environment:

```
pip install -e ~/ultimate-anatome
```

## Available Tools

### Representation Similarity

To measure the similarity of learned representation, `anatome.SimilarityHook` is a useful tool. Currently, the following
methods are implemented.

- [Raghu et al. NIPS2017 SVCCA](https://papers.nips.cc/paper/7188-svcca-singular-vector-canonical-correlation-analysis-for-deep-learning-dynamics-and-interpretability)
- [Marcos et al. NeurIPS2018 PWCCA](https://papers.nips.cc/paper/7815-insights-on-representational-similarity-in-neural-networks-with-canonical-correlation)
- [Kornblith et al. ICML2019 Linear CKA](http://proceedings.mlr.press/v97/kornblith19a.html)
- [Ding et al. arXiv Orthogonal Procrustes distance](https://arxiv.org/abs/2108.01661)

```python
import torch
from torchvision.models import resnet18
from anatome import DistanceHook
from anatome.my_utils import remove_hook

model = resnet18()
hook1 = DistanceHook(model, "layer3.0.conv1")
hook2 = DistanceHook(model, "layer3.0.conv2")
model.eval()
with torch.no_grad():
    model(torch.randn(128, 3, 224, 224))
# downsampling to (size, size) may be helpful
hook1.distance(hook2, size=8)
hook1.clear()
hook2.clear()
remove_hook(model, hook1)
remove_hook(model, hook2)
```

or to test do:
```python
from anatome.my_utils import my_anatome_test
my_anatome_test()
```

### Loss Landscape Visualization

- [Li et al. NeurIPS2018](https://papers.nips.cc/paper/7875-visualizing-the-loss-landscape-of-neural-nets)

```python
from anatome import landscape2d

x, y, z = landscape2d(resnet18(),
                      data,
                      F.cross_entropy,
                      x_range=(-1, 1),
                      y_range=(-1, 1),
                      step_size=0.1)
imshow(z)
```

![](assets/landscape2d.svg)
![](assets/landscape3d.svg)

### Fourier Analysis

- Yin et al. NeurIPS 2019 etc.,

```python
from anatome import fourier_map

map = fourier_map(resnet18(),
                  data,
                  F.cross_entropy,
                  norm=4)
imshow(map)
```

![](assets/fourier.svg)

## Citation

If you use this implementation in your research, please conser citing my version of anatome:
```
@software{miranda2021ultimate_anatome,
    author={Brando Miranda},
    title={Ultimate Anatome, the ultimate PyTorch library to analyze internal representation of neural networks},
    url={https://github.com/brando90/ultimate-anatome},
    year={2021}
}
```
and the original version:

```
@software{hataya2020anatome,
    author={Ryuichiro Hataya},
    title={anatome, a PyTorch library to analyze internal representation of neural networks},
    url={https://github.com/moskomule/anatome},
    year={2020}
}
```


# Related libraries:

- The original anatome of course https://github.com/moskomule/anatome
- https://github.com/AntixK/PyTorch-Model-Compare From the developers "A tiny package to compare two neural networks in PyTorch. There are many ways to compare two neural networks, but one robust and scalable way is using the Centered Kernel Alignment (CKA) metric, where the features of the networks are compared."
- Captum is trying to add support. Check it out here: https://github.com/pytorch/captum/issues/572#issuecomment-957306989
- The original svcca tutorials by Maithra et al https://github.com/google/svcca/tree/master/tutorials 
- if you know more create a pull request and add it here!




%prep
%autosetup -n ultimate-anatome-0.1.1

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-ultimate-anatome -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.1-1
- Package Spec generated