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|
%global _empty_manifest_terminate_build 0
Name: python-torch-intermediate-layer-getter
Version: 0.1.post1
Release: 1
Summary: Simple easy to use module to get the intermediate results from chosen submodules
License: GNU General Public License v3 (GPLv3)
URL: https://github.com/sebamenabar/Pytorch-IntermediateLayerGetter
Source0: https://mirrors.aliyun.com/pypi/web/packages/38/98/8a37ff086257cdc9fd3e62f47b76de7d0091e9a43f3c719521411068449a/torch_intermediate_layer_getter-0.1.post1.tar.gz
BuildArch: noarch
%description
Simple easy to use module to get the intermediate results from chosen submodules. Supports submodule annidation. Inspired in [this](https://github.com/pytorch/vision/blob/f76e598d47879dbd917bf5936bbd11ff41632787/torchvision/models/_utils.py#L7) but does not assume that submodules are executed sequentially.
# Installation
```sh
pip install torch_intermediate_layer_getter
```
# Usage
## Example
```python
import torch
import torch.nn as nn
from torch_intermediate_layer_getter import IntermediateLayerGetter as MidGetter
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(2, 2)
self.fc2 = nn.Linear(2, 2)
self.nested = nn.Sequential(
nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 3)),
nn.Linear(3, 1),
)
self.interaction_idty = nn.Identity() # Simple trick for operations not performed as modules
def forward(self, x):
x1 = self.fc1(x)
x2 = self.fc2(x)
interaction = x1 * x2
self.interaction_idty(interaction)
x_out = self.nested(interaction)
return x_out
model = Model()
return_layers = {
'fc2': 'fc2',
'nested.0.1': 'nested',
'interaction_idty': 'interaction',
}
mid_getter = MidGetter(model, return_layers=return_layers, keep_output=True)
mid_outputs, model_output = mid_getter(torch.randn(1, 2))
print(model_output)
>> tensor([[0.3219]], grad_fn=<AddmmBackward>)
print(mid_outputs)
>> OrderedDict([('fc2', tensor([[-1.5125, 0.9334]], grad_fn=<AddmmBackward>)),
('interaction', tensor([[-0.0687, -0.1462]], grad_fn=<MulBackward0>)),
('nested', tensor([[-0.1697, 0.1432, 0.2959]], grad_fn=<AddmmBackward>))])
# model_output is None if keep_ouput is False
# if keep_output is True the model_output contains the final model's output
```
%package -n python3-torch-intermediate-layer-getter
Summary: Simple easy to use module to get the intermediate results from chosen submodules
Provides: python-torch-intermediate-layer-getter
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-torch-intermediate-layer-getter
Simple easy to use module to get the intermediate results from chosen submodules. Supports submodule annidation. Inspired in [this](https://github.com/pytorch/vision/blob/f76e598d47879dbd917bf5936bbd11ff41632787/torchvision/models/_utils.py#L7) but does not assume that submodules are executed sequentially.
# Installation
```sh
pip install torch_intermediate_layer_getter
```
# Usage
## Example
```python
import torch
import torch.nn as nn
from torch_intermediate_layer_getter import IntermediateLayerGetter as MidGetter
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(2, 2)
self.fc2 = nn.Linear(2, 2)
self.nested = nn.Sequential(
nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 3)),
nn.Linear(3, 1),
)
self.interaction_idty = nn.Identity() # Simple trick for operations not performed as modules
def forward(self, x):
x1 = self.fc1(x)
x2 = self.fc2(x)
interaction = x1 * x2
self.interaction_idty(interaction)
x_out = self.nested(interaction)
return x_out
model = Model()
return_layers = {
'fc2': 'fc2',
'nested.0.1': 'nested',
'interaction_idty': 'interaction',
}
mid_getter = MidGetter(model, return_layers=return_layers, keep_output=True)
mid_outputs, model_output = mid_getter(torch.randn(1, 2))
print(model_output)
>> tensor([[0.3219]], grad_fn=<AddmmBackward>)
print(mid_outputs)
>> OrderedDict([('fc2', tensor([[-1.5125, 0.9334]], grad_fn=<AddmmBackward>)),
('interaction', tensor([[-0.0687, -0.1462]], grad_fn=<MulBackward0>)),
('nested', tensor([[-0.1697, 0.1432, 0.2959]], grad_fn=<AddmmBackward>))])
# model_output is None if keep_ouput is False
# if keep_output is True the model_output contains the final model's output
```
%package help
Summary: Development documents and examples for torch-intermediate-layer-getter
Provides: python3-torch-intermediate-layer-getter-doc
%description help
Simple easy to use module to get the intermediate results from chosen submodules. Supports submodule annidation. Inspired in [this](https://github.com/pytorch/vision/blob/f76e598d47879dbd917bf5936bbd11ff41632787/torchvision/models/_utils.py#L7) but does not assume that submodules are executed sequentially.
# Installation
```sh
pip install torch_intermediate_layer_getter
```
# Usage
## Example
```python
import torch
import torch.nn as nn
from torch_intermediate_layer_getter import IntermediateLayerGetter as MidGetter
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(2, 2)
self.fc2 = nn.Linear(2, 2)
self.nested = nn.Sequential(
nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 3)),
nn.Linear(3, 1),
)
self.interaction_idty = nn.Identity() # Simple trick for operations not performed as modules
def forward(self, x):
x1 = self.fc1(x)
x2 = self.fc2(x)
interaction = x1 * x2
self.interaction_idty(interaction)
x_out = self.nested(interaction)
return x_out
model = Model()
return_layers = {
'fc2': 'fc2',
'nested.0.1': 'nested',
'interaction_idty': 'interaction',
}
mid_getter = MidGetter(model, return_layers=return_layers, keep_output=True)
mid_outputs, model_output = mid_getter(torch.randn(1, 2))
print(model_output)
>> tensor([[0.3219]], grad_fn=<AddmmBackward>)
print(mid_outputs)
>> OrderedDict([('fc2', tensor([[-1.5125, 0.9334]], grad_fn=<AddmmBackward>)),
('interaction', tensor([[-0.0687, -0.1462]], grad_fn=<MulBackward0>)),
('nested', tensor([[-0.1697, 0.1432, 0.2959]], grad_fn=<AddmmBackward>))])
# model_output is None if keep_ouput is False
# if keep_output is True the model_output contains the final model's output
```
%prep
%autosetup -n torch_intermediate_layer_getter-0.1.post1
%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-torch-intermediate-layer-getter -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.post1-1
- Package Spec generated
|
