deploy: SimplexLab/TorchJD@6c02f3b

Author: ValerianRey

latest/docs/aggregation/cagrad/index.html

@@ -324,14 +324,14 @@ <h1>CAGrad<a class="headerlink" href="#cagrad" title="Link to this heading">¶</
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.CAGrad.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGrad.__call__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">matrix</span></span></em>, <em class="sig-param"><span class="positional-only-separator o"><abbr title="Positional-only parameter separator (PEP 570)"><span class="pre">/</span></abbr></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_aggregator_bases.py#L32-L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGrad.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the aggregation from the input matrix and applies all registered hooks.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>matrix</strong> – The Jacobian to aggregate.</p>
+<dd class="field-odd"><p><strong>matrix</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a></span>) – The Jacobian to aggregate.</p>
 </dd>
 <dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.14)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a></span></p>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a></span></p>
 </dd>
 </dl>
 </dd></dl>
@@ -343,11 +343,14 @@ <h1>CAGrad<a class="headerlink" href="#cagrad" title="Link to this heading">¶</
 <span class="property"><span class="k"><span class="pre">class</span></span><span class="w"> </span></span><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.</span></span><span class="sig-name descname"><span class="pre">CAGradWeighting</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">c</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_cagrad.py#L21-L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGradWeighting" title="Link to this definition">¶</a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.CAGradWeighting.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGradWeighting.__call__" title="Link to this definition">¶</a></dt>
-<dd><p>Call self as a function.</p>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gramian</span></span></em>, <em class="sig-param"><span class="positional-only-separator o"><abbr title="Positional-only parameter separator (PEP 570)"><span class="pre">/</span></abbr></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_weighting_bases.py#L72-L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGradWeighting.__call__" title="Link to this definition">¶</a></dt>
+<dd><p>Computes the vector of weights from the input Gramian and applies all registered hooks.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.14)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a></span></p>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>gramian</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a></span>) – The Gramian from which the weights must be extracted.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a></span></p>
 </dd>
 </dl>
 </dd></dl>

latest/docs/aggregation/config/index.html

@@ -320,14 +320,14 @@ <h1>ConFIG<a class="headerlink" href="#config" title="Link to this heading">¶</
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.ConFIG.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.ConFIG.__call__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">matrix</span></span></em>, <em class="sig-param"><span class="positional-only-separator o"><abbr title="Positional-only parameter separator (PEP 570)"><span class="pre">/</span></abbr></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_aggregator_bases.py#L32-L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.ConFIG.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the aggregation from the input matrix and applies all registered hooks.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>matrix</strong> – The Jacobian to aggregate.</p>
+<dd class="field-odd"><p><strong>matrix</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a></span>) – The Jacobian to aggregate.</p>
 </dd>
 <dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.14)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a></span></p>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a></span></p>
 </dd>
 </dl>
 </dd></dl>

latest/docs/aggregation/dualproj/index.html

@@ -320,14 +320,14 @@ <h1>DualProj<a class="headerlink" href="#dualproj" title="Link to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.DualProj.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.DualProj.__call__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">matrix</span></span></em>, <em class="sig-param"><span class="positional-only-separator o"><abbr title="Positional-only parameter separator (PEP 570)"><span class="pre">/</span></abbr></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_aggregator_bases.py#L32-L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.DualProj.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the aggregation from the input matrix and applies all registered hooks.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>matrix</strong> – The Jacobian to aggregate.</p>
+<dd class="field-odd"><p><strong>matrix</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a></span>) – The Jacobian to aggregate.</p>
 </dd>
 <dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.14)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a></span></p>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a></span></p>
 </dd>
 </dl>
 </dd></dl>
@@ -350,14 +350,14 @@ <h1>DualProj<a class="headerlink" href="#dualproj" title="Link to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.DualProjWeighting.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.DualProjWeighting.__call__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gramian</span></span></em>, <em class="sig-param"><span class="positional-only-separator o"><abbr title="Positional-only parameter separator (PEP 570)"><span class="pre">/</span></abbr></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_weighting_bases.py#L72-L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.DualProjWeighting.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the vector of weights from the input Gramian and applies all registered hooks.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>gramian</strong> – The Gramian from which the weights must be extracted.</p>
+<dd class="field-odd"><p><strong>gramian</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a></span>) – The Gramian from which the weights must be extracted.</p>
 </dd>
 <dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.14)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a></span></p>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a></span></p>
 </dd>
 </dl>
 </dd></dl>