diff options
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-torchtuples.spec | 342 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 344 insertions, 0 deletions
@@ -0,0 +1 @@ +/torchtuples-0.2.2.tar.gz diff --git a/python-torchtuples.spec b/python-torchtuples.spec new file mode 100644 index 0000000..a769afd --- /dev/null +++ b/python-torchtuples.spec @@ -0,0 +1,342 @@ +%global _empty_manifest_terminate_build 0 +Name: python-torchtuples +Version: 0.2.2 +Release: 1 +Summary: Training neural networks in PyTorch +License: BSD license +URL: https://github.com/havakv/torchtuples +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/eb/46/4b8dd8b12eeb24b10492d7d36011063c12c07a6848bbe21d9a973bf98b8e/torchtuples-0.2.2.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-matplotlib + +%description +# torchtuples + +[](https://github.com/havakv/torchtuples/actions) +[](https://pypi.org/project/torchtuples/) +[](https://anaconda.org/conda-forge/torchtuples) + +[](https://github.com/havakv/torchtuples/blob/master/LICENSE) + +**torchtuples** is a small python package for training PyTorch models. +It works equally well for `numpy arrays` and `torch tensors`. +One of the main benefits of **torchtuples** is that it handles data in the form of nested tuples (see [example below](#example)). + + +## Installation + +**torchtuples** depends on [PyTorch](https://pytorch.org/get-started/locally/) which should be installed from [HERE](https://pytorch.org/get-started/locally/). + +Next, **torchtuples** can be installed with pip: +```bash +pip install torchtuples +``` +Or, via conda: +```bash +conda install -c conda-forge torchtuples +``` +For the bleeding edge version, install directly from github (consider adding `--force-reinstall`): +```bash +pip install git+git://github.com/havakv/torchtuples.git +``` +or by cloning the repo: +```bash +git clone https://github.com/havakv/torchtuples.git +cd torchtuples +python setup.py install +``` + +## Example + +```python +import torch +from torch import nn +from torchtuples import Model, optim +``` +Make a data set with three sets of covariates `x0`, `x1` and `x2`, and a target `y`. +The covariates are structured in a nested tuple `x`. +```python +n = 500 +x0, x1, x2 = [torch.randn(n, 3) for _ in range(3)] +y = torch.randn(n, 1) +x = (x0, (x0, x1, x2)) +``` +Create a simple ReLU net that takes as input the tensor `x_tensor` and the tuple `x_tuple`. Note that `x_tuple` can be of arbitrary length. The tensors in `x_tuple` are passed through a layer `lin_tuple`, averaged, and concatenated with `x_tensor`. +We then pass our new tensor through the layer `lin_cat`. +```python +class Net(nn.Module): + def __init__(self): + super().__init__() + self.lin_tuple = nn.Linear(3, 2) + self.lin_cat = nn.Linear(5, 1) + self.relu = nn.ReLU() + + def forward(self, x_tensor, x_tuple): + x = [self.relu(self.lin_tuple(xi)) for xi in x_tuple] + x = torch.stack(x).mean(0) + x = torch.cat([x, x_tensor], dim=1) + return self.lin_cat(x) + + def predict(self, x_tensor, x_tuple): + x = self.forward(x_tensor, x_tuple) + return torch.sigmoid(x) +``` + +We can now fit the model with +```python +model = Model(Net(), nn.MSELoss(), optim.SGD(0.01)) +log = model.fit(x, y, batch_size=64, epochs=5) +``` +and make predictions with either the `Net.predict` method +```python +preds = model.predict(x) +``` +or with the `Net.forward` method +```python +preds = model.predict_net(x) +``` + +For more examples, see the [examples folder](https://github.com/havakv/torchtuples/tree/master/examples). + + + + +%package -n python3-torchtuples +Summary: Training neural networks in PyTorch +Provides: python-torchtuples +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-torchtuples +# torchtuples + +[](https://github.com/havakv/torchtuples/actions) +[](https://pypi.org/project/torchtuples/) +[](https://anaconda.org/conda-forge/torchtuples) + +[](https://github.com/havakv/torchtuples/blob/master/LICENSE) + +**torchtuples** is a small python package for training PyTorch models. +It works equally well for `numpy arrays` and `torch tensors`. +One of the main benefits of **torchtuples** is that it handles data in the form of nested tuples (see [example below](#example)). + + +## Installation + +**torchtuples** depends on [PyTorch](https://pytorch.org/get-started/locally/) which should be installed from [HERE](https://pytorch.org/get-started/locally/). + +Next, **torchtuples** can be installed with pip: +```bash +pip install torchtuples +``` +Or, via conda: +```bash +conda install -c conda-forge torchtuples +``` +For the bleeding edge version, install directly from github (consider adding `--force-reinstall`): +```bash +pip install git+git://github.com/havakv/torchtuples.git +``` +or by cloning the repo: +```bash +git clone https://github.com/havakv/torchtuples.git +cd torchtuples +python setup.py install +``` + +## Example + +```python +import torch +from torch import nn +from torchtuples import Model, optim +``` +Make a data set with three sets of covariates `x0`, `x1` and `x2`, and a target `y`. +The covariates are structured in a nested tuple `x`. +```python +n = 500 +x0, x1, x2 = [torch.randn(n, 3) for _ in range(3)] +y = torch.randn(n, 1) +x = (x0, (x0, x1, x2)) +``` +Create a simple ReLU net that takes as input the tensor `x_tensor` and the tuple `x_tuple`. Note that `x_tuple` can be of arbitrary length. The tensors in `x_tuple` are passed through a layer `lin_tuple`, averaged, and concatenated with `x_tensor`. +We then pass our new tensor through the layer `lin_cat`. +```python +class Net(nn.Module): + def __init__(self): + super().__init__() + self.lin_tuple = nn.Linear(3, 2) + self.lin_cat = nn.Linear(5, 1) + self.relu = nn.ReLU() + + def forward(self, x_tensor, x_tuple): + x = [self.relu(self.lin_tuple(xi)) for xi in x_tuple] + x = torch.stack(x).mean(0) + x = torch.cat([x, x_tensor], dim=1) + return self.lin_cat(x) + + def predict(self, x_tensor, x_tuple): + x = self.forward(x_tensor, x_tuple) + return torch.sigmoid(x) +``` + +We can now fit the model with +```python +model = Model(Net(), nn.MSELoss(), optim.SGD(0.01)) +log = model.fit(x, y, batch_size=64, epochs=5) +``` +and make predictions with either the `Net.predict` method +```python +preds = model.predict(x) +``` +or with the `Net.forward` method +```python +preds = model.predict_net(x) +``` + +For more examples, see the [examples folder](https://github.com/havakv/torchtuples/tree/master/examples). + + + + +%package help +Summary: Development documents and examples for torchtuples +Provides: python3-torchtuples-doc +%description help +# torchtuples + +[](https://github.com/havakv/torchtuples/actions) +[](https://pypi.org/project/torchtuples/) +[](https://anaconda.org/conda-forge/torchtuples) + +[](https://github.com/havakv/torchtuples/blob/master/LICENSE) + +**torchtuples** is a small python package for training PyTorch models. +It works equally well for `numpy arrays` and `torch tensors`. +One of the main benefits of **torchtuples** is that it handles data in the form of nested tuples (see [example below](#example)). + + +## Installation + +**torchtuples** depends on [PyTorch](https://pytorch.org/get-started/locally/) which should be installed from [HERE](https://pytorch.org/get-started/locally/). + +Next, **torchtuples** can be installed with pip: +```bash +pip install torchtuples +``` +Or, via conda: +```bash +conda install -c conda-forge torchtuples +``` +For the bleeding edge version, install directly from github (consider adding `--force-reinstall`): +```bash +pip install git+git://github.com/havakv/torchtuples.git +``` +or by cloning the repo: +```bash +git clone https://github.com/havakv/torchtuples.git +cd torchtuples +python setup.py install +``` + +## Example + +```python +import torch +from torch import nn +from torchtuples import Model, optim +``` +Make a data set with three sets of covariates `x0`, `x1` and `x2`, and a target `y`. +The covariates are structured in a nested tuple `x`. +```python +n = 500 +x0, x1, x2 = [torch.randn(n, 3) for _ in range(3)] +y = torch.randn(n, 1) +x = (x0, (x0, x1, x2)) +``` +Create a simple ReLU net that takes as input the tensor `x_tensor` and the tuple `x_tuple`. Note that `x_tuple` can be of arbitrary length. The tensors in `x_tuple` are passed through a layer `lin_tuple`, averaged, and concatenated with `x_tensor`. +We then pass our new tensor through the layer `lin_cat`. +```python +class Net(nn.Module): + def __init__(self): + super().__init__() + self.lin_tuple = nn.Linear(3, 2) + self.lin_cat = nn.Linear(5, 1) + self.relu = nn.ReLU() + + def forward(self, x_tensor, x_tuple): + x = [self.relu(self.lin_tuple(xi)) for xi in x_tuple] + x = torch.stack(x).mean(0) + x = torch.cat([x, x_tensor], dim=1) + return self.lin_cat(x) + + def predict(self, x_tensor, x_tuple): + x = self.forward(x_tensor, x_tuple) + return torch.sigmoid(x) +``` + +We can now fit the model with +```python +model = Model(Net(), nn.MSELoss(), optim.SGD(0.01)) +log = model.fit(x, y, batch_size=64, epochs=5) +``` +and make predictions with either the `Net.predict` method +```python +preds = model.predict(x) +``` +or with the `Net.forward` method +```python +preds = model.predict_net(x) +``` + +For more examples, see the [examples folder](https://github.com/havakv/torchtuples/tree/master/examples). + + + + +%prep +%autosetup -n torchtuples-0.2.2 + +%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-torchtuples -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.2-1 +- Package Spec generated @@ -0,0 +1 @@ +504ddd77fa9a57ebe411d06e56450afb torchtuples-0.2.2.tar.gz |