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Updates for version 0.7.1

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This commit is contained in:
Jeremy Francis Reizenstein
2022-10-23 09:41:42 -07:00
parent 27dc9dc21d
commit 45d1b39f1c
14 changed files with 481 additions and 545 deletions
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21
tutorials/implicitron_config_system.html
View File

@@ -384,9 +384,9 @@ Note that we indicate configurable classes using a special base class <code>Conf
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># expand_args_fields must be called on an object before it is instantiated.</span>
<span class="c1"># A warning is raised if this is missed, but it is possible to not notice the warning.</span>
<span class="c1"># It modifies the class like @dataclass</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># The expand_args_fields function modifies the class like @dataclasses.dataclass.</span>
<span class="c1"># If it has not been called on a Configurable object before it has been instantiated, it will</span>
<span class="c1"># be called automatically.</span>
<span class="n">expand_args_fields</span><span class="p">(</span><span class="n">MyConfigurable</span><span class="p">)</span>
<span class="n">my_configurable_instance</span> <span class="o">=</span> <span class="n">MyConfigurable</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">my_configurable_instance</span><span class="o">.</span><span class="n">d</span> <span class="o">==</span> <span class="mi">16</span>
@@ -400,7 +400,7 @@ Note that we indicate configurable classes using a special base class <code>Conf
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># get_default_args calls expand_args_fields automatically</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># get_default_args also calls expand_args_fields automatically</span>
<span class="n">our_structured</span> <span class="o">=</span> <span class="n">get_default_args</span><span class="p">(</span><span class="n">MyConfigurable</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">our_structured</span><span class="p">,</span> <span class="n">DictConfig</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">OmegaConf</span><span class="o">.</span><span class="n">to_yaml</span><span class="p">(</span><span class="n">our_structured</span><span class="p">))</span>
@@ -642,8 +642,7 @@ Note in this case it is necessary to call <code>Module.__init__</code> explicitl
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">expand_args_fields</span><span class="p">(</span><span class="n">MyLinear</span><span class="p">)</span>
<span class="n">my_linear</span> <span class="o">=</span> <span class="n">MyLinear</span><span class="p">()</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="n">my_linear</span> <span class="o">=</span> <span class="n">MyLinear</span><span class="p">()</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">my_linear</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"output shape:"</span><span class="p">,</span> <span class="n">output</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
@@ -736,8 +735,7 @@ before you <em>use</em> the library classes.</p>
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">expand_args_fields</span><span class="p">(</span><span class="n">Out</span><span class="p">)</span>
<span class="n">out2</span> <span class="o">=</span> <span class="n">Out</span><span class="p">(</span><span class="n">inner_class_type</span><span class="o">=</span><span class="s2">"UserImplementedInner"</span><span class="p">)</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="n">out2</span> <span class="o">=</span> <span class="n">Out</span><span class="p">(</span><span class="n">inner_class_type</span><span class="o">=</span><span class="s2">"UserImplementedInner"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">out2</span><span class="o">.</span><span class="n">inner</span><span class="p">)</span>
</pre></div>
</div>
@@ -785,8 +783,7 @@ before you <em>use</em> the library classes.</p>
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">expand_args_fields</span><span class="p">(</span><span class="n">LargeComponent</span><span class="p">)</span>
<span class="n">large_component</span> <span class="o">=</span> <span class="n">LargeComponent</span><span class="p">()</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="n">large_component</span> <span class="o">=</span> <span class="n">LargeComponent</span><span class="p">()</span>
<span class="k">assert</span> <span class="n">large_component</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span>
<span class="nb">print</span><span class="p">(</span><span class="n">OmegaConf</span><span class="o">.</span><span class="n">to_yaml</span><span class="p">(</span><span class="n">LargeComponent</span><span class="p">))</span>
</pre></div>
@@ -842,8 +839,7 @@ before you <em>use</em> the library classes.</p>
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">expand_args_fields</span><span class="p">(</span><span class="n">LargeComponent</span><span class="p">)</span>
<span class="n">large_component</span> <span class="o">=</span> <span class="n">LargeComponent</span><span class="p">()</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="n">large_component</span> <span class="o">=</span> <span class="n">LargeComponent</span><span class="p">()</span>
<span class="k">assert</span> <span class="n">large_component</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span>
<span class="nb">print</span><span class="p">(</span><span class="n">OmegaConf</span><span class="o">.</span><span class="n">to_yaml</span><span class="p">(</span><span class="n">LargeComponent</span><span class="p">))</span>
</pre></div>
@@ -871,7 +867,6 @@ before you <em>use</em> the library classes.</p>
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Appendix:-gotchas-⚠️">Appendix: gotchas ⚠️<a class="anchor-link" href="#Appendix:-gotchas-⚠️"></a></h2><ul>
<li>Omitting to define <code>__post_init__</code> or not calling <code>run_auto_creation</code> in it.</li>
<li>Using a configurable class without calling get_default_args or expand_args_fields on it.</li>
<li>Omitting a type annotation on a field. For example, writing
<pre><code> subcomponent_class_type = "SubComponent"</code></pre>
instead of

21
tutorials/implicitron_config_system/index.html
View File

@@ -384,9 +384,9 @@ Note that we indicate configurable classes using a special base class <code>Conf
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># expand_args_fields must be called on an object before it is instantiated.</span>
<span class="c1"># A warning is raised if this is missed, but it is possible to not notice the warning.</span>
<span class="c1"># It modifies the class like @dataclass</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># The expand_args_fields function modifies the class like @dataclasses.dataclass.</span>
<span class="c1"># If it has not been called on a Configurable object before it has been instantiated, it will</span>
<span class="c1"># be called automatically.</span>
<span class="n">expand_args_fields</span><span class="p">(</span><span class="n">MyConfigurable</span><span class="p">)</span>
<span class="n">my_configurable_instance</span> <span class="o">=</span> <span class="n">MyConfigurable</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">my_configurable_instance</span><span class="o">.</span><span class="n">d</span> <span class="o">==</span> <span class="mi">16</span>
@@ -400,7 +400,7 @@ Note that we indicate configurable classes using a special base class <code>Conf
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># get_default_args calls expand_args_fields automatically</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># get_default_args also calls expand_args_fields automatically</span>
<span class="n">our_structured</span> <span class="o">=</span> <span class="n">get_default_args</span><span class="p">(</span><span class="n">MyConfigurable</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">our_structured</span><span class="p">,</span> <span class="n">DictConfig</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">OmegaConf</span><span class="o">.</span><span class="n">to_yaml</span><span class="p">(</span><span class="n">our_structured</span><span class="p">))</span>
@@ -642,8 +642,7 @@ Note in this case it is necessary to call <code>Module.__init__</code> explicitl
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">expand_args_fields</span><span class="p">(</span><span class="n">MyLinear</span><span class="p">)</span>
<span class="n">my_linear</span> <span class="o">=</span> <span class="n">MyLinear</span><span class="p">()</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="n">my_linear</span> <span class="o">=</span> <span class="n">MyLinear</span><span class="p">()</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">my_linear</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"output shape:"</span><span class="p">,</span> <span class="n">output</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
@@ -736,8 +735,7 @@ before you <em>use</em> the library classes.</p>
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">expand_args_fields</span><span class="p">(</span><span class="n">Out</span><span class="p">)</span>
<span class="n">out2</span> <span class="o">=</span> <span class="n">Out</span><span class="p">(</span><span class="n">inner_class_type</span><span class="o">=</span><span class="s2">"UserImplementedInner"</span><span class="p">)</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="n">out2</span> <span class="o">=</span> <span class="n">Out</span><span class="p">(</span><span class="n">inner_class_type</span><span class="o">=</span><span class="s2">"UserImplementedInner"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">out2</span><span class="o">.</span><span class="n">inner</span><span class="p">)</span>
</pre></div>
</div>
@@ -785,8 +783,7 @@ before you <em>use</em> the library classes.</p>
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">expand_args_fields</span><span class="p">(</span><span class="n">LargeComponent</span><span class="p">)</span>
<span class="n">large_component</span> <span class="o">=</span> <span class="n">LargeComponent</span><span class="p">()</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="n">large_component</span> <span class="o">=</span> <span class="n">LargeComponent</span><span class="p">()</span>
<span class="k">assert</span> <span class="n">large_component</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span>
<span class="nb">print</span><span class="p">(</span><span class="n">OmegaConf</span><span class="o">.</span><span class="n">to_yaml</span><span class="p">(</span><span class="n">LargeComponent</span><span class="p">))</span>
</pre></div>
@@ -842,8 +839,7 @@ before you <em>use</em> the library classes.</p>
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">expand_args_fields</span><span class="p">(</span><span class="n">LargeComponent</span><span class="p">)</span>
<span class="n">large_component</span> <span class="o">=</span> <span class="n">LargeComponent</span><span class="p">()</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="n">large_component</span> <span class="o">=</span> <span class="n">LargeComponent</span><span class="p">()</span>
<span class="k">assert</span> <span class="n">large_component</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span>
<span class="nb">print</span><span class="p">(</span><span class="n">OmegaConf</span><span class="o">.</span><span class="n">to_yaml</span><span class="p">(</span><span class="n">LargeComponent</span><span class="p">))</span>
</pre></div>
@@ -871,7 +867,6 @@ before you <em>use</em> the library classes.</p>
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Appendix:-gotchas-⚠️">Appendix: gotchas ⚠️<a class="anchor-link" href="#Appendix:-gotchas-⚠️"></a></h2><ul>
<li>Omitting to define <code>__post_init__</code> or not calling <code>run_auto_creation</code> in it.</li>
<li>Using a configurable class without calling get_default_args or expand_args_fields on it.</li>
<li>Omitting a type annotation on a field. For example, writing
<pre><code> subcomponent_class_type = "SubComponent"</code></pre>
instead of

19
tutorials/implicitron_volumes.html
View File

@@ -174,10 +174,9 @@
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.dataset.dataset_base</span> <span class="kn">import</span> <span class="n">FrameData</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.dataset.rendered_mesh_dataset_map_provider</span> <span class="kn">import</span> <span class="n">RenderedMeshDatasetMapProvider</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.models.generic_model</span> <span class="kn">import</span> <span class="n">GenericModel</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.models.implicit_function.base</span> <span class="kn">import</span> <span class="n">ImplicitFunctionBase</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.models.implicit_function.base</span> <span class="kn">import</span> <span class="n">ImplicitFunctionBase</span><span class="p">,</span> <span class="n">ImplicitronRayBundle</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.models.renderer.base</span> <span class="kn">import</span> <span class="n">EvaluationMode</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.tools.config</span> <span class="kn">import</span> <span class="n">expand_args_fields</span><span class="p">,</span> <span class="n">get_default_args</span><span class="p">,</span> <span class="n">registry</span><span class="p">,</span> <span class="n">remove_unused_components</span>
<span class="kn">from</span> <span class="nn">pytorch3d.renderer</span> <span class="kn">import</span> <span class="n">RayBundle</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.tools.config</span> <span class="kn">import</span> <span class="n">get_default_args</span><span class="p">,</span> <span class="n">registry</span><span class="p">,</span> <span class="n">remove_unused_components</span>
<span class="kn">from</span> <span class="nn">pytorch3d.renderer.implicit.renderer</span> <span class="kn">import</span> <span class="n">VolumeSampler</span>
<span class="kn">from</span> <span class="nn">pytorch3d.structures</span> <span class="kn">import</span> <span class="n">Volumes</span>
<span class="kn">from</span> <span class="nn">pytorch3d.vis.plotly_vis</span> <span class="kn">import</span> <span class="n">plot_batch_individually</span><span class="p">,</span> <span class="n">plot_scene</span>
@@ -241,21 +240,12 @@ If running locally, the data is already available at the correct path.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>If we want to instantiate one of Implicitron's configurable objects, such as <code>RenderedMeshDatasetMapProvider</code>, without using the OmegaConf initialisation (get_default_args), we need to call <code>expand_args_fields</code> on the class first.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">expand_args_fields</span><span class="p">(</span><span class="n">RenderedMeshDatasetMapProvider</span><span class="p">)</span>
<span class="n">cow_provider</span> <span class="o">=</span> <span class="n">RenderedMeshDatasetMapProvider</span><span class="p">(</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="n">cow_provider</span> <span class="o">=</span> <span class="n">RenderedMeshDatasetMapProvider</span><span class="p">(</span>
<span class="n">data_file</span><span class="o">=</span><span class="s2">"data/cow_mesh/cow.obj"</span><span class="p">,</span>
<span class="n">use_point_light</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">resolution</span><span class="o">=</span><span class="n">output_resolution</span><span class="p">,</span>
@@ -344,7 +334,7 @@ We use Python's dataclass annotations for configuring the module.</p>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">ray_bundle</span><span class="p">:</span> <span class="n">RayBundle</span><span class="p">,</span>
<span class="n">ray_bundle</span><span class="p">:</span> <span class="n">ImplicitronRayBundle</span><span class="p">,</span>
<span class="n">fun_viewpool</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">global_code</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="p">):</span>
@@ -394,7 +384,6 @@ There are two ways to construct it which are equivalent here.</p>
<span class="n">gm</span> <span class="o">=</span> <span class="n">GenericModel</span><span class="p">(</span><span class="o">**</span><span class="n">cfg</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># constructing GenericModel directly</span>
<span class="n">expand_args_fields</span><span class="p">(</span><span class="n">GenericModel</span><span class="p">)</span>
<span class="n">gm</span> <span class="o">=</span> <span class="n">GenericModel</span><span class="p">(</span>
<span class="n">implicit_function_class_type</span><span class="o">=</span><span class="s2">"MyVolumes"</span><span class="p">,</span>
<span class="n">render_image_height</span><span class="o">=</span><span class="n">output_resolution</span><span class="p">,</span>

19
tutorials/implicitron_volumes/index.html
View File

@@ -174,10 +174,9 @@
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.dataset.dataset_base</span> <span class="kn">import</span> <span class="n">FrameData</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.dataset.rendered_mesh_dataset_map_provider</span> <span class="kn">import</span> <span class="n">RenderedMeshDatasetMapProvider</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.models.generic_model</span> <span class="kn">import</span> <span class="n">GenericModel</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.models.implicit_function.base</span> <span class="kn">import</span> <span class="n">ImplicitFunctionBase</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.models.implicit_function.base</span> <span class="kn">import</span> <span class="n">ImplicitFunctionBase</span><span class="p">,</span> <span class="n">ImplicitronRayBundle</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.models.renderer.base</span> <span class="kn">import</span> <span class="n">EvaluationMode</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.tools.config</span> <span class="kn">import</span> <span class="n">expand_args_fields</span><span class="p">,</span> <span class="n">get_default_args</span><span class="p">,</span> <span class="n">registry</span><span class="p">,</span> <span class="n">remove_unused_components</span>
<span class="kn">from</span> <span class="nn">pytorch3d.renderer</span> <span class="kn">import</span> <span class="n">RayBundle</span>
<span class="kn">from</span> <span class="nn">pytorch3d.implicitron.tools.config</span> <span class="kn">import</span> <span class="n">get_default_args</span><span class="p">,</span> <span class="n">registry</span><span class="p">,</span> <span class="n">remove_unused_components</span>
<span class="kn">from</span> <span class="nn">pytorch3d.renderer.implicit.renderer</span> <span class="kn">import</span> <span class="n">VolumeSampler</span>
<span class="kn">from</span> <span class="nn">pytorch3d.structures</span> <span class="kn">import</span> <span class="n">Volumes</span>
<span class="kn">from</span> <span class="nn">pytorch3d.vis.plotly_vis</span> <span class="kn">import</span> <span class="n">plot_batch_individually</span><span class="p">,</span> <span class="n">plot_scene</span>
@@ -241,21 +240,12 @@ If running locally, the data is already available at the correct path.</p>
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<p>If we want to instantiate one of Implicitron's configurable objects, such as <code>RenderedMeshDatasetMapProvider</code>, without using the OmegaConf initialisation (get_default_args), we need to call <code>expand_args_fields</code> on the class first.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">expand_args_fields</span><span class="p">(</span><span class="n">RenderedMeshDatasetMapProvider</span><span class="p">)</span>
<span class="n">cow_provider</span> <span class="o">=</span> <span class="n">RenderedMeshDatasetMapProvider</span><span class="p">(</span>
<div class="highlight hl-ipython3"><pre><span></span><span class="n">cow_provider</span> <span class="o">=</span> <span class="n">RenderedMeshDatasetMapProvider</span><span class="p">(</span>
<span class="n">data_file</span><span class="o">=</span><span class="s2">"data/cow_mesh/cow.obj"</span><span class="p">,</span>
<span class="n">use_point_light</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">resolution</span><span class="o">=</span><span class="n">output_resolution</span><span class="p">,</span>
@@ -344,7 +334,7 @@ We use Python's dataclass annotations for configuring the module.</p>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">ray_bundle</span><span class="p">:</span> <span class="n">RayBundle</span><span class="p">,</span>
<span class="n">ray_bundle</span><span class="p">:</span> <span class="n">ImplicitronRayBundle</span><span class="p">,</span>
<span class="n">fun_viewpool</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">global_code</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="p">):</span>
@@ -394,7 +384,6 @@ There are two ways to construct it which are equivalent here.</p>
<span class="n">gm</span> <span class="o">=</span> <span class="n">GenericModel</span><span class="p">(</span><span class="o">**</span><span class="n">cfg</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># constructing GenericModel directly</span>
<span class="n">expand_args_fields</span><span class="p">(</span><span class="n">GenericModel</span><span class="p">)</span>
<span class="n">gm</span> <span class="o">=</span> <span class="n">GenericModel</span><span class="p">(</span>
<span class="n">implicit_function_class_type</span><span class="o">=</span><span class="s2">"MyVolumes"</span><span class="p">,</span>
<span class="n">render_image_height</span><span class="o">=</span><span class="n">output_resolution</span><span class="p">,</span>

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tutorials/render_densepose.html
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@@ -204,7 +204,7 @@ In this tutorial, we provide an example of using DensePose data in PyTorch3D.</p
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<h2 id="Load-the-SMPL-model">Load the SMPL model<a class="anchor-link" href="#Load-the-SMPL-model"></a></h2><h4 id="Download-the-SMPL-model">Download the SMPL model<a class="anchor-link" href="#Download-the-SMPL-model"></a></h4><ul>
<li>Go to <a href="http://smpl.is.tue.mpg.de/downloads">http://smpl.is.tue.mpg.de/downloads</a> and sign up.</li>
<li>Go to <a href="https://smpl.is.tue.mpg.de/download.php">https://smpl.is.tue.mpg.de/download.php</a> and sign up.</li>
<li>Download SMPL for Python Users and unzip.</li>
<li>Copy the file male template file <strong>'models/basicModel_m_lbs_10_207_0_v1.0.0.pkl'</strong> to the data/DensePose/ folder.<ul>
<li>rename the file to <strong>'smpl_model.pkl'</strong> or rename the string where it's commented below</li>

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@@ -204,7 +204,7 @@ In this tutorial, we provide an example of using DensePose data in PyTorch3D.</p
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<h2 id="Load-the-SMPL-model">Load the SMPL model<a class="anchor-link" href="#Load-the-SMPL-model"></a></h2><h4 id="Download-the-SMPL-model">Download the SMPL model<a class="anchor-link" href="#Download-the-SMPL-model"></a></h4><ul>
<li>Go to <a href="http://smpl.is.tue.mpg.de/downloads">http://smpl.is.tue.mpg.de/downloads</a> and sign up.</li>
<li>Go to <a href="https://smpl.is.tue.mpg.de/download.php">https://smpl.is.tue.mpg.de/download.php</a> and sign up.</li>
<li>Download SMPL for Python Users and unzip.</li>
<li>Copy the file male template file <strong>'models/basicModel_m_lbs_10_207_0_v1.0.0.pkl'</strong> to the data/DensePose/ folder.<ul>
<li>rename the file to <strong>'smpl_model.pkl'</strong> or rename the string where it's commented below</li>