mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-12-21 14:50:36 +08:00
updates
This commit is contained in:
Jeremy Francis Reizenstein
@@ -127,7 +127,7 @@
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<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
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<span class="n">need_pytorch3d</span><span class="o">=</span><span class="kc">True</span>
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<span class="k">if</span> <span class="n">need_pytorch3d</span><span class="p">:</span>
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<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">"1.13."</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">"linux"</span><span class="p">):</span>
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<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">startswith</span><span class="p">((</span><span class="s2">"1.13."</span><span class="p">,</span> <span class="s2">"2.0."</span><span class="p">))</span> <span class="ow">and</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">"linux"</span><span class="p">):</span>
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<span class="c1"># We try to install PyTorch3D via a released wheel.</span>
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<span class="n">pyt_version_str</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"+"</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"."</span><span class="p">,</span> <span class="s2">""</span><span class="p">)</span>
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<span class="n">version_str</span><span class="o">=</span><span class="s2">""</span><span class="o">.</span><span class="n">join</span><span class="p">([</span>
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@@ -135,14 +135,11 @@
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<span class="n">torch</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"."</span><span class="p">,</span><span class="s2">""</span><span class="p">),</span>
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<span class="sa">f</span><span class="s2">"_pyt</span><span class="si">{</span><span class="n">pyt_version_str</span><span class="si">}</span><span class="s2">"</span>
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<span class="p">])</span>
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<span class="o">!</span>pip install fvcore iopath
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<span class="o">!</span>pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/<span class="o">{</span>version_str<span class="o">}</span>/download.html
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<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>fvcore<span class="w"> </span>iopath
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<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>--no-index<span class="w"> </span>--no-cache-dir<span class="w"> </span>pytorch3d<span class="w"> </span>-f<span class="w"> </span>https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/<span class="o">{</span>version_str<span class="o">}</span>/download.html
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<span class="k">else</span><span class="p">:</span>
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<span class="c1"># We try to install PyTorch3D from source.</span>
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<span class="o">!</span>curl -LO https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz
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<span class="o">!</span>tar xzf <span class="m">1</span>.10.0.tar.gz
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<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"CUB_HOME"</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span> <span class="o">+</span> <span class="s2">"/cub-1.10.0"</span>
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<span class="o">!</span>pip install <span class="s1">'git+https://github.com/facebookresearch/pytorch3d.git@stable'</span>
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<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="s1">'git+https://github.com/facebookresearch/pytorch3d.git@stable'</span>
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</pre></div>
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</div>
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</div>
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@@ -203,8 +200,8 @@
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<div class="prompt input_prompt">In [ ]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class="highlight hl-ipython3"><pre><span></span><span class="o">!</span>wget https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/docs/tutorials/utils/plot_image_grid.py
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<span class="o">!</span>wget https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/docs/tutorials/utils/generate_cow_renders.py
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<div class="highlight hl-ipython3"><pre><span></span><span class="o">!</span>wget<span class="w"> </span>https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/docs/tutorials/utils/plot_image_grid.py
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<span class="o">!</span>wget<span class="w"> </span>https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/docs/tutorials/utils/generate_cow_renders.py
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<span class="kn">from</span> <span class="nn">plot_image_grid</span> <span class="kn">import</span> <span class="n">image_grid</span>
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<span class="kn">from</span> <span class="nn">generate_cow_renders</span> <span class="kn">import</span> <span class="n">generate_cow_renders</span>
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</pre></div>
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@@ -356,7 +353,7 @@ It renders the cow mesh from the <code>fit_textured_mesh.ipynb</code> tutorial f
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<div class="input_area">
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">HarmonicEmbedding</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
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<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_harmonic_functions</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span> <span class="n">omega0</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> Given an input tensor `x` of shape [minibatch, ... , dim],</span>
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<span class="sd"> the harmonic embedding layer converts each feature</span>
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<span class="sd"> in `x` into a series of harmonic features `embedding`</span>
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@@ -383,7 +380,7 @@ It renders the cow mesh from the <code>fit_textured_mesh.ipynb</code> tutorial f
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<span class="n">omega0</span> <span class="o">*</span> <span class="p">(</span><span class="mf">2.0</span> <span class="o">**</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_harmonic_functions</span><span class="p">)),</span>
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<span class="p">)</span>
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<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">x</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> Args:</span>
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<span class="sd"> x: tensor of shape [..., dim]</span>
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<span class="sd"> Returns:</span>
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@@ -397,7 +394,7 @@ It renders the cow mesh from the <code>fit_textured_mesh.ipynb</code> tutorial f
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<span class="k">class</span> <span class="nc">NeuralRadianceField</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
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<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_harmonic_functions</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span> <span class="n">n_hidden_neurons</span><span class="o">=</span><span class="mi">256</span><span class="p">):</span>
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<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
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<span class="sd">"""</span>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> Args:</span>
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<span class="sd"> n_harmonic_functions: The number of harmonic functions</span>
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<span class="sd"> used to form the harmonic embedding of each point.</span>
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@@ -454,7 +451,7 @@ It renders the cow mesh from the <code>fit_textured_mesh.ipynb</code> tutorial f
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<span class="bp">self</span><span class="o">.</span><span class="n">density_layer</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.5</span>
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<span class="k">def</span> <span class="nf">_get_densities</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">features</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> This function takes `features` predicted by `self.mlp`</span>
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<span class="sd"> and converts them to `raw_densities` with `self.density_layer`.</span>
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<span class="sd"> `raw_densities` are later mapped to [0-1] range with</span>
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@@ -464,7 +461,7 @@ It renders the cow mesh from the <code>fit_textured_mesh.ipynb</code> tutorial f
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<span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="o">-</span><span class="n">raw_densities</span><span class="p">)</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span>
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<span class="k">def</span> <span class="nf">_get_colors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">rays_directions</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> This function takes per-point `features` predicted by `self.mlp`</span>
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<span class="sd"> and evaluates the color model in order to attach to each</span>
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<span class="sd"> point a 3D vector of its RGB color.</span>
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@@ -508,7 +505,7 @@ It renders the cow mesh from the <code>fit_textured_mesh.ipynb</code> tutorial f
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<span class="n">ray_bundle</span><span class="p">:</span> <span class="n">RayBundle</span><span class="p">,</span>
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<span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
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<span class="p">):</span>
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<span class="sd">"""</span>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> The forward function accepts the parametrizations of</span>
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<span class="sd"> 3D points sampled along projection rays. The forward</span>
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<span class="sd"> pass is responsible for attaching a 3D vector</span>
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@@ -562,7 +559,7 @@ It renders the cow mesh from the <code>fit_textured_mesh.ipynb</code> tutorial f
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<span class="n">n_batches</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16</span><span class="p">,</span>
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<span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
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<span class="p">):</span>
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<span class="sd">"""</span>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> This function is used to allow for memory efficient processing</span>
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<span class="sd"> of input rays. The input rays are first split to `n_batches`</span>
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<span class="sd"> chunks and passed through the `self.forward` function one at a time</span>
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@@ -639,7 +636,7 @@ It renders the cow mesh from the <code>fit_textured_mesh.ipynb</code> tutorial f
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<div class="inner_cell">
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<div class="input_area">
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">huber</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">scaling</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> A helper function for evaluating the smooth L1 (huber) loss</span>
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<span class="sd"> between the rendered silhouettes and colors.</span>
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<span class="sd"> """</span>
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@@ -648,7 +645,7 @@ It renders the cow mesh from the <code>fit_textured_mesh.ipynb</code> tutorial f
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<span class="k">return</span> <span class="n">loss</span>
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<span class="k">def</span> <span class="nf">sample_images_at_mc_locs</span><span class="p">(</span><span class="n">target_images</span><span class="p">,</span> <span class="n">sampled_rays_xy</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> Given a set of Monte Carlo pixel locations `sampled_rays_xy`,</span>
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<span class="sd"> this method samples the tensor `target_images` at the</span>
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<span class="sd"> respective 2D locations.</span>
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@@ -681,7 +678,7 @@ It renders the cow mesh from the <code>fit_textured_mesh.ipynb</code> tutorial f
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<span class="n">target_image</span><span class="p">,</span> <span class="n">target_silhouette</span><span class="p">,</span>
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<span class="n">loss_history_color</span><span class="p">,</span> <span class="n">loss_history_sil</span><span class="p">,</span>
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<span class="p">):</span>
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<span class="sd">"""</span>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> This is a helper function for visualizing the</span>
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<span class="sd"> intermediate results of the learning. </span>
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<span class="sd"> </span>
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@@ -955,4 +952,4 @@ described in the previous cell.</p>
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</div>
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</div>
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</div>
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</div></div></div></div></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2022 Meta Platforms, Inc<br/>Legal:<a href="https://opensource.facebook.com/legal/privacy/" target="_blank" rel="noreferrer noopener">Privacy</a><a href="https://opensource.facebook.com/legal/terms/" target="_blank" rel="noreferrer noopener">Terms</a></section></footer></div></body></html>
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</div></div></div></div></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2023 Meta Platforms, Inc<br/>Legal:<a href="https://opensource.facebook.com/legal/privacy/" target="_blank" rel="noreferrer noopener">Privacy</a><a href="https://opensource.facebook.com/legal/terms/" target="_blank" rel="noreferrer noopener">Terms</a></section></footer></div></body></html>
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