From d90d8add66e6fe584c87ec1729738643d369a344 Mon Sep 17 00:00:00 2001 From: Matthew Tancik Date: Mon, 1 Aug 2022 17:21:49 -0700 Subject: [PATCH] Update sampler docs (#239) * Update sampler docs * Remove unused imports * Metadata update --- docs/models/index.rst | 1 + docs/notebooks/index.rst | 6 +- docs/notebooks/visualize_samplers.ipynb | 5132 ++++++++++++++++++++++- 3 files changed, 5077 insertions(+), 62 deletions(-) diff --git a/docs/models/index.rst b/docs/models/index.rst index 52c4a608e..30480f0c5 100644 --- a/docs/models/index.rst +++ b/docs/models/index.rst @@ -4,6 +4,7 @@ Models ======== .. toctree:: + :maxdepth: 1 NeRF Mip-NeRF diff --git a/docs/notebooks/index.rst b/docs/notebooks/index.rst index 9a00ca98b..003b01b21 100644 --- a/docs/notebooks/index.rst +++ b/docs/notebooks/index.rst @@ -4,11 +4,11 @@ Components ========== .. toctree:: - :maxdepth: 0 + :maxdepth: 1 visualize_cameras.ipynb - visualize_encoders.ipynb visualize_samplers.ipynb - visualize_spatial_distortions.ipynb visualize_samples.ipynb + visualize_spatial_distortions.ipynb + visualize_encoders.ipynb \ No newline at end of file diff --git a/docs/notebooks/visualize_samplers.ipynb b/docs/notebooks/visualize_samplers.ipynb index efc01eb8c..04b71ecd6 100644 --- a/docs/notebooks/visualize_samplers.ipynb +++ b/docs/notebooks/visualize_samplers.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Ray Samplers" + "# Ray Samplers\n" ] }, { @@ -13,12 +13,12 @@ "source": [ "## Spaced Samplers\n", "\n", - "A basic sampler that spaces samples based on a predefined function." + "A basic sampler that spaces samples based on a predefined function.\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "metadata": { "tags": [ "hide-input" @@ -27,50 +27,5045 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -79,21 +5074,32 @@ "import torch\n", "from nerfactory.cameras.rays import RayBundle\n", "from nerfactory.graphs.modules import ray_sampler\n", - "from matplotlib import pyplot as plt\n", + "import plotly.graph_objects as go\n", + "from plotly.subplots import make_subplots\n", + "\n", "\n", "num_samples = 1000\n", "near = 2\n", "far = 5\n", "train_stratified = False\n", "\n", - "samplers = {\n", - " \"Uniform\": ray_sampler.UniformSampler,\n", - " \"Linear in Disparity\": ray_sampler.LinearDisparitySampler,\n", - " \"Square Root\": ray_sampler.SqrtSampler,\n", - " \"Log Sampler\": ray_sampler.LogSampler,\n", - "}\n", + "samplers = [\n", + " ray_sampler.UniformSampler,\n", + " ray_sampler.LinearDisparitySampler,\n", + " ray_sampler.SqrtSampler,\n", + " ray_sampler.LogSampler,\n", + "]\n", "\n", - "for name, Sampler in samplers.items():\n", + "fig = make_subplots(\n", + " rows=2,\n", + " cols=2,\n", + " subplot_titles=(\"Uniform\", \"Linear in Disparity\", \"Square Root\", \"Log Sampler\"),\n", + " shared_xaxes=True,\n", + " shared_yaxes=True,\n", + " vertical_spacing=0.1,\n", + ")\n", + "\n", + "for i, Sampler in enumerate(samplers):\n", " sampler = Sampler(num_samples=num_samples, train_stratified=train_stratified)\n", "\n", " ray_bundle = RayBundle(\n", @@ -107,11 +5113,19 @@ " samples = sampler.generate_ray_samples(ray_bundle)\n", " samples.frustums.starts.shape\n", "\n", - " plt.hist(samples.frustums.starts[0, :, 0], alpha=0.5, bins=100)\n", - " plt.xlabel(\"Distance\")\n", - " plt.ylabel(\"# Samples\")\n", - " plt.title(name)\n", - " plt.show()" + " trace = go.Histogram(x=samples.frustums.starts[0, :, 0], nbinsx=50)\n", + " fig.append_trace(trace, i // 2 + 1, i % 2 + 1)\n", + "\n", + "fig.update_yaxes(title_text=\"# Samples\", row=1, col=1)\n", + "fig.update_yaxes(title_text=\"# Samples\", row=2, col=1)\n", + "fig.update_xaxes(title_text=\"Distance\", row=2, col=1)\n", + "fig.update_xaxes(title_text=\"Distance\", row=2, col=2)\n", + "\n", + "# Overlay both histograms\n", + "fig.update_layout(height=700, hovermode=False, showlegend=False)\n", + "fig.update_yaxes(range=[0, 80])\n", + "fig.update_traces(opacity=0.7)\n", + "fig.show()" ] } ], @@ -135,7 +5149,7 @@ }, "vscode": { "interpreter": { - "hash": "41f62990c6e03ccb48eec29860b83c64e20fcd45204ae5014afab6809cd6f1f1" + "hash": "34c28001ff35fb390494047002768a8182dcf55b1b11415165e62ea61557ab83" } } },