automatic import of python-torch-intermediate-layer-getteropeneuler20.03

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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