Skip to content

GitLab

"tools/git@developer.sourcefind.cn:wangsen/paddle_dbnet.git" did not exist on "24040eb4ac1652dd7d445af8a3ca3b36be3a2576"
Unverified Commit 7404f1e9 authored by Steven Liu's avatar Steven Liu Committed by GitHub
Browse files

[docs] Clean up toctree (#7715) 

* toctree

* optim

* feedback

* improve overview
parent 5a692278
Show whitespace changes
Inline Side-by-side
Showing with 3817 additions and 179 deletions
docs/source/en/_toctree.yml
View file @ 7404f1e9
...@@ -23,7 +23,6 @@ ...@@ -23,7 +23,6 @@
title: Accelerate inference of text-to-image diffusion models title: Accelerate inference of text-to-image diffusion models
title: Tutorials title: Tutorials
- sections: - sections:
- sections:
- local: using-diffusers/loading - local: using-diffusers/loading
title: Load pipelines title: Load pipelines
- local: using-diffusers/custom_pipeline_overview - local: using-diffusers/custom_pipeline_overview
...@@ -38,10 +37,8 @@ ...@@ -38,10 +37,8 @@
title: Load adapters title: Load adapters
- local: using-diffusers/push_to_hub - local: using-diffusers/push_to_hub
title: Push files to the Hub title: Push files to the Hub
title: Loading & Hub title: Load pipelines and adapters
- sections: - sections:
- local: using-diffusers/pipeline_overview
title: Overview
- local: using-diffusers/unconditional_image_generation - local: using-diffusers/unconditional_image_generation
title: Unconditional image generation title: Unconditional image generation
- local: using-diffusers/conditional_image_generation - local: using-diffusers/conditional_image_generation
...@@ -54,16 +51,16 @@ ...@@ -54,16 +51,16 @@
title: Text or image-to-video title: Text or image-to-video
- local: using-diffusers/depth2img - local: using-diffusers/depth2img
title: Depth-to-image title: Depth-to-image
title: Tasks title: Generative tasks
- sections: - sections:
- local: using-diffusers/textual_inversion_inference - local: using-diffusers/overview_techniques
title: Textual inversion title: Overview
- local: using-diffusers/ip_adapter
title: IP-Adapter
- local: using-diffusers/merge_loras
title: Merge LoRAs
- local: training/distributed_inference - local: training/distributed_inference
title: Distributed inference with multiple GPUs title: Distributed inference with multiple GPUs
- local: using-diffusers/merge_loras
title: Merge LoRAs
- local: using-diffusers/callback
title: Pipeline callbacks
- local: using-diffusers/reusing_seeds - local: using-diffusers/reusing_seeds
title: Improve image quality with deterministic generation title: Improve image quality with deterministic generation
- local: using-diffusers/control_brightness - local: using-diffusers/control_brightness
...@@ -72,28 +69,26 @@ ...@@ -72,28 +69,26 @@
title: Prompt techniques title: Prompt techniques
- local: using-diffusers/freeu - local: using-diffusers/freeu
title: Improve generation quality with FreeU title: Improve generation quality with FreeU
title: Techniques title: Inference techniques
- sections: - sections:
- local: using-diffusers/pipeline_overview
title: Overview
- local: using-diffusers/sdxl - local: using-diffusers/sdxl
title: Stable Diffusion XL title: Stable Diffusion XL
- local: using-diffusers/sdxl_turbo - local: using-diffusers/sdxl_turbo
title: SDXL Turbo title: SDXL Turbo
- local: using-diffusers/kandinsky - local: using-diffusers/kandinsky
title: Kandinsky title: Kandinsky
- local: using-diffusers/ip_adapter
title: IP-Adapter
- local: using-diffusers/controlnet - local: using-diffusers/controlnet
title: ControlNet title: ControlNet
- local: using-diffusers/t2i_adapter - local: using-diffusers/t2i_adapter
title: T2I-Adapter title: T2I-Adapter
- local: using-diffusers/textual_inversion_inference
title: Textual inversion
- local: using-diffusers/shap-e - local: using-diffusers/shap-e
title: Shap-E title: Shap-E
- local: using-diffusers/diffedit - local: using-diffusers/diffedit
title: DiffEdit title: DiffEdit
- local: using-diffusers/distilled_sd
title: Distilled Stable Diffusion inference
- local: using-diffusers/callback
title: Pipeline callbacks
- local: using-diffusers/reproducibility - local: using-diffusers/reproducibility
title: Create reproducible pipelines title: Create reproducible pipelines
- local: using-diffusers/custom_pipeline_examples - local: using-diffusers/custom_pipeline_examples
...@@ -109,7 +104,7 @@ ...@@ -109,7 +104,7 @@
- local: using-diffusers/svd - local: using-diffusers/svd
title: Stable Video Diffusion title: Stable Video Diffusion
title: Specific pipeline examples title: Specific pipeline examples
- sections: - sections:
- local: training/overview - local: training/overview
title: Overview title: Overview
- local: training/create_dataset - local: training/create_dataset
...@@ -134,6 +129,7 @@ ...@@ -134,6 +129,7 @@
- local: training/instructpix2pix - local: training/instructpix2pix
title: InstructPix2Pix title: InstructPix2Pix
title: Models title: Models
isExpanded: false
- sections: - sections:
- local: training/text_inversion - local: training/text_inversion
title: Textual Inversion title: Textual Inversion
...@@ -148,18 +144,13 @@ ...@@ -148,18 +144,13 @@
- local: training/ddpo - local: training/ddpo
title: Reinforcement learning training with DDPO title: Reinforcement learning training with DDPO
title: Methods title: Methods
isExpanded: false
title: Training title: Training
- sections:
- local: using-diffusers/other-modalities
title: Other Modalities
title: Taking Diffusers Beyond Images
title: Using Diffusers
- sections: - sections:
- local: optimization/opt_overview
title: Overview
- sections:
- local: optimization/fp16 - local: optimization/fp16
title: Speed up inference title: Speed up inference
- local: using-diffusers/distilled_sd
title: Distilled Stable Diffusion inference
- local: optimization/memory - local: optimization/memory
title: Reduce memory usage title: Reduce memory usage
- local: optimization/torch2.0 - local: optimization/torch2.0
...@@ -172,7 +163,6 @@ ...@@ -172,7 +163,6 @@
title: DeepCache title: DeepCache
- local: optimization/tgate - local: optimization/tgate
title: TGATE title: TGATE
title: General optimizations
- sections: - sections:
- local: using-diffusers/stable_diffusion_jax_how_to - local: using-diffusers/stable_diffusion_jax_how_to
title: JAX/Flax title: JAX/Flax
...@@ -182,14 +172,14 @@ ...@@ -182,14 +172,14 @@
title: OpenVINO title: OpenVINO
- local: optimization/coreml - local: optimization/coreml
title: Core ML title: Core ML
title: Optimized model types title: Optimized model formats
- sections: - sections:
- local: optimization/mps - local: optimization/mps
title: Metal Performance Shaders (MPS) title: Metal Performance Shaders (MPS)
- local: optimization/habana - local: optimization/habana
title: Habana Gaudi title: Habana Gaudi
title: Optimized hardware title: Optimized hardware
title: Optimization title: Accelerate inference and reduce memory
- sections: - sections:
- local: conceptual/philosophy - local: conceptual/philosophy
title: Philosophy title: Philosophy
...@@ -211,6 +201,7 @@ ...@@ -211,6 +201,7 @@
- local: api/outputs - local: api/outputs
title: Outputs title: Outputs
title: Main Classes title: Main Classes
isExpanded: false
- sections: - sections:
- local: api/loaders/ip_adapter - local: api/loaders/ip_adapter
title: IP-Adapter title: IP-Adapter
...@@ -225,6 +216,7 @@ ...@@ -225,6 +216,7 @@
- local: api/loaders/peft - local: api/loaders/peft
title: PEFT title: PEFT
title: Loaders title: Loaders
isExpanded: false
- sections: - sections:
- local: api/models/overview - local: api/models/overview
title: Overview title: Overview
...@@ -259,6 +251,7 @@ ...@@ -259,6 +251,7 @@
- local: api/models/controlnet - local: api/models/controlnet
title: ControlNet title: ControlNet
title: Models title: Models
isExpanded: false
- sections: - sections:
- local: api/pipelines/overview - local: api/pipelines/overview
title: Overview title: Overview
...@@ -383,6 +376,7 @@ ...@@ -383,6 +376,7 @@
- local: api/pipelines/wuerstchen - local: api/pipelines/wuerstchen
title: Wuerstchen title: Wuerstchen
title: Pipelines title: Pipelines
isExpanded: false
- sections: - sections:
- local: api/schedulers/overview - local: api/schedulers/overview
title: Overview title: Overview
...@@ -443,6 +437,7 @@ ...@@ -443,6 +437,7 @@
- local: api/schedulers/vq_diffusion - local: api/schedulers/vq_diffusion
title: VQDiffusionScheduler title: VQDiffusionScheduler
title: Schedulers title: Schedulers
isExpanded: false
- sections: - sections:
- local: api/internal_classes_overview - local: api/internal_classes_overview
title: Overview title: Overview
...@@ -457,4 +452,5 @@ ...@@ -457,4 +452,5 @@
- local: api/image_processor - local: api/image_processor
title: VAE Image Processor title: VAE Image Processor
title: Internal classes title: Internal classes
isExpanded: false
title: API title: API
docs/source/en/optimization/opt_overview.md deleted 100644 → 0
View file @ 5a692278
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Overview
Generating high-quality outputs is computationally intensive, especially during each iterative step where you go from a noisy output to a less noisy output. One of 🤗 Diffuser's goals is to make this technology widely accessible to everyone, which includes enabling fast inference on consumer and specialized hardware.
This section will cover tips and tricks - like half-precision weights and sliced attention - for optimizing inference speed and reducing memory-consumption. You'll also learn how to speed up your PyTorch code with [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) or [ONNX Runtime](https://onnxruntime.ai/docs/), and enable memory-efficient attention with [xFormers](https://facebookresearch.github.io/xformers/). There are also guides for running inference on specific hardware like Apple Silicon, and Intel or Habana processors.
docs/source/en/using-diffusers/other-modalities.md → docs/source/en/using-diffusers/overview_techniques.md
View file @ 7404f1e9
...@@ -10,12 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o ...@@ -10,12 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License. specific language governing permissions and limitations under the License.
--> -->
# Using Diffusers with other modalities # Overview
Diffusers is in the process of expanding to modalities other than images. The inference pipeline supports and enables a wide range of techniques that are divided into two categories:
Example type | Colab | Pipeline | * Pipeline functionality: these techniques modify the pipeline or extend it for other applications. For example, pipeline callbacks add new features to a pipeline and a pipeline can also be extended for distributed inference.
:-------------------------:|:-------------------------:|:-------------------------:| * Improve inference quality: these techniques increase the visual quality of the generated images. For example, you can enhance your prompts with GPT2 to create better images with lower effort.
[Molecule conformation](https://www.nature.com/subjects/molecular-conformation#:~:text=Definition,to%20changes%20in%20their%20environment.) generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/geodiff_molecule_conformation.ipynb) | ❌
More coming soon!
\ No newline at end of file
docs/source/en/using-diffusers/pipeline_overview.md deleted 100644 → 0
View file @ 5a692278
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Overview
A pipeline is an end-to-end class that provides a quick and easy way to use a diffusion system for inference by bundling independently trained models and schedulers together. Certain combinations of models and schedulers define specific pipeline types, like [`StableDiffusionXLPipeline`] or [`StableDiffusionControlNetPipeline`], with specific capabilities. All pipeline types inherit from the base [`DiffusionPipeline`] class; pass it any checkpoint, and it'll automatically detect the pipeline type and load the necessary components.
This section demonstrates how to use specific pipelines such as Stable Diffusion XL, ControlNet, and DiffEdit. You'll also learn how to use a distilled version of the Stable Diffusion model to speed up inference, how to create reproducible pipelines, and how to use and contribute community pipelines.
examples/research_projects/geodiff/README.md 0 → 100644
View file @ 7404f1e9
# GeoDiff
> [!TIP]
> This notebook is not actively maintained by the Diffusers team. For any questions or comments, please contact [natolambert](https://twitter.com/natolambert).
This is an experimental research notebook demonstrating how to generate stable 3D structures of molecules with [GeoDiff](https://github.com/MinkaiXu/GeoDiff) and Diffusers.
examples/research_projects/geodiff/geodiff_molecule_conformation.ipynb 0 → 100644
View file @ 7404f1e9
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "F88mignPnalS"
},
"source": [
"# Introduction\n",
"\n",
"This colab is design to run the pretrained models from [GeoDiff](https://github.com/MinkaiXu/GeoDiff).\n",
"The visualization code is inspired by this PyMol [colab](https://colab.research.google.com/gist/iwatobipen/2ec7faeafe5974501e69fcc98c122922/pymol.ipynb#scrollTo=Hm4kY7CaZSlw).\n",
"\n",
"The goal is to generate physically accurate molecules. Given the input of a molecule graph (atom and bond structures with their connectivity -- in the form of a 2d graph). What we want to generate is a stable 3d structure of the molecule.\n",
"\n",
"This colab uses GEOM datasets that have multiple 3d targets per configuration, which provide more compelling targets for generative methods.\n",
"\n",
"> Colab made by [natolambert](https://twitter.com/natolambert).\n",
"\n",
"![diffusers_library](https://github.com/huggingface/diffusers/raw/main/docs/source/imgs/diffusers_library.jpg)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7cnwXMocnuzB"
},
"source": [
"## Installations\n",
"\n"
]
},
{
"cell_type": "markdown",
"source": [
"### Install Conda"
],
"metadata": {
"id": "ff9SxWnaNId9"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "1g_6zOabItDk"
},
"source": [
"Here we check the `cuda` version of colab. When this was built, the version was always 11.1, which impacts some installation decisions below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "K0ofXobG5Y-X",
"outputId": "572c3d25-6f19-4c1e-83f5-a1d084a3207f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"nvcc: NVIDIA (R) Cuda compiler driver\n",
"Copyright (c) 2005-2021 NVIDIA Corporation\n",
"Built on Sun_Feb_14_21:12:58_PST_2021\n",
"Cuda compilation tools, release 11.2, V11.2.152\n",
"Build cuda_11.2.r11.2/compiler.29618528_0\n"
]
}
],
"source": [
"!nvcc --version"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VfthW90vI0nw"
},
"source": [
"Install Conda for some more complex dependencies for geometric networks."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2WNFzSnbiE0k",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "690d0d4d-9d0a-4ead-c6dc-086f113f532f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
}
],
"source": [
"!pip install -q condacolab"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NUsbWYCUI7Km"
},
"source": [
"Setup Conda"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FZelreINdmd0",
"outputId": "635f0cb8-0af4-499f-e0a4-b3790cb12e9f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"✨🍰✨ Everything looks OK!\n"
]
}
],
"source": [
"import condacolab\n",
"condacolab.install()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JzDHaPU7I9Sn"
},
"source": [
"Install pytorch requirements (this takes a few minutes, go grab yourself a coffee 🤗)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JMxRjHhL7w8V",
"outputId": "6ed511b3-9262-49e8-b340-08e76b05ebd8"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
"Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
"\n",
"## Package Plan ##\n",
"\n",
" environment location: /usr/local\n",
"\n",
" added / updated specs:\n",
" - cudatoolkit=11.1\n",
" - pytorch\n",
" - torchaudio\n",
" - torchvision\n",
"\n",
"\n",
"The following packages will be downloaded:\n",
"\n",
" package | build\n",
" ---------------------------|-----------------\n",
" conda-22.9.0 | py37h89c1867_1 960 KB conda-forge\n",
" ------------------------------------------------------------\n",
" Total: 960 KB\n",
"\n",
"The following packages will be UPDATED:\n",
"\n",
" conda 4.14.0-py37h89c1867_0 --> 22.9.0-py37h89c1867_1\n",
"\n",
"\n",
"\n",
"Downloading and Extracting Packages\n",
"conda-22.9.0 | 960 KB | : 100% 1.0/1 [00:00<00:00, 4.15it/s]\n",
"Preparing transaction: / \b\bdone\n",
"Verifying transaction: \\ \b\bdone\n",
"Executing transaction: / \b\bdone\n",
"Retrieving notices: ...working... done\n"
]
}
],
"source": [
"!conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia\n",
"# !conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge"
]
},
{
"cell_type": "markdown",
"source": [
"Need to remove a pathspec for colab that specifies the incorrect cuda version."
],
"metadata": {
"id": "QDS6FPZ0Tu5b"
}
},
{
"cell_type": "code",
"source": [
"!rm /usr/local/conda-meta/pinned"
],
"metadata": {
"id": "dq1lxR10TtrR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ed9c5a71-b449-418f-abb7-072b74e7f6c8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"rm: cannot remove '/usr/local/conda-meta/pinned': No such file or directory\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z1L3DdZOJB30"
},
"source": [
"Install torch geometric (used in the model later)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "D5ukfCOWfjzK",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8437485a-5aa6-4d53-8f7f-23517ac1ace6"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
"Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
"\n",
"## Package Plan ##\n",
"\n",
" environment location: /usr/local\n",
"\n",
" added / updated specs:\n",
" - pytorch-geometric=1.7.2\n",
"\n",
"\n",
"The following packages will be downloaded:\n",
"\n",
" package | build\n",
" ---------------------------|-----------------\n",
" decorator-4.4.2 | py_0 11 KB conda-forge\n",
" googledrivedownloader-0.4 | pyhd3deb0d_1 7 KB conda-forge\n",
" jinja2-3.1.2 | pyhd8ed1ab_1 99 KB conda-forge\n",
" joblib-1.2.0 | pyhd8ed1ab_0 205 KB conda-forge\n",
" markupsafe-2.1.1 | py37h540881e_1 22 KB conda-forge\n",
" networkx-2.5.1 | pyhd8ed1ab_0 1.2 MB conda-forge\n",
" pandas-1.2.3 | py37hdc94413_0 11.8 MB conda-forge\n",
" pyparsing-3.0.9 | pyhd8ed1ab_0 79 KB conda-forge\n",
" python-dateutil-2.8.2 | pyhd8ed1ab_0 240 KB conda-forge\n",
" python-louvain-0.15 | pyhd8ed1ab_1 13 KB conda-forge\n",
" pytorch-cluster-1.5.9 |py37_torch_1.8.0_cu111 1.2 MB rusty1s\n",
" pytorch-geometric-1.7.2 |py37_torch_1.8.0_cu111 445 KB rusty1s\n",
" pytorch-scatter-2.0.8 |py37_torch_1.8.0_cu111 6.1 MB rusty1s\n",
" pytorch-sparse-0.6.12 |py37_torch_1.8.0_cu111 2.9 MB rusty1s\n",
" pytorch-spline-conv-1.2.1 |py37_torch_1.8.0_cu111 736 KB rusty1s\n",
" pytz-2022.4 | pyhd8ed1ab_0 232 KB conda-forge\n",
" scikit-learn-1.0.2 | py37hf9e9bfc_0 7.8 MB conda-forge\n",
" scipy-1.7.3 | py37hf2a6cf1_0 21.8 MB conda-forge\n",
" setuptools-59.8.0 | py37h89c1867_1 1.0 MB conda-forge\n",
" threadpoolctl-3.1.0 | pyh8a188c0_0 18 KB conda-forge\n",
" ------------------------------------------------------------\n",
" Total: 55.9 MB\n",
"\n",
"The following NEW packages will be INSTALLED:\n",
"\n",
" decorator conda-forge/noarch::decorator-4.4.2-py_0 None\n",
" googledrivedownlo~ conda-forge/noarch::googledrivedownloader-0.4-pyhd3deb0d_1 None\n",
" jinja2 conda-forge/noarch::jinja2-3.1.2-pyhd8ed1ab_1 None\n",
" joblib conda-forge/noarch::joblib-1.2.0-pyhd8ed1ab_0 None\n",
" markupsafe conda-forge/linux-64::markupsafe-2.1.1-py37h540881e_1 None\n",
" networkx conda-forge/noarch::networkx-2.5.1-pyhd8ed1ab_0 None\n",
" pandas conda-forge/linux-64::pandas-1.2.3-py37hdc94413_0 None\n",
" pyparsing conda-forge/noarch::pyparsing-3.0.9-pyhd8ed1ab_0 None\n",
" python-dateutil conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0 None\n",
" python-louvain conda-forge/noarch::python-louvain-0.15-pyhd8ed1ab_1 None\n",
" pytorch-cluster rusty1s/linux-64::pytorch-cluster-1.5.9-py37_torch_1.8.0_cu111 None\n",
" pytorch-geometric rusty1s/linux-64::pytorch-geometric-1.7.2-py37_torch_1.8.0_cu111 None\n",
" pytorch-scatter rusty1s/linux-64::pytorch-scatter-2.0.8-py37_torch_1.8.0_cu111 None\n",
" pytorch-sparse rusty1s/linux-64::pytorch-sparse-0.6.12-py37_torch_1.8.0_cu111 None\n",
" pytorch-spline-co~ rusty1s/linux-64::pytorch-spline-conv-1.2.1-py37_torch_1.8.0_cu111 None\n",
" pytz conda-forge/noarch::pytz-2022.4-pyhd8ed1ab_0 None\n",
" scikit-learn conda-forge/linux-64::scikit-learn-1.0.2-py37hf9e9bfc_0 None\n",
" scipy conda-forge/linux-64::scipy-1.7.3-py37hf2a6cf1_0 None\n",
" threadpoolctl conda-forge/noarch::threadpoolctl-3.1.0-pyh8a188c0_0 None\n",
"\n",
"The following packages will be DOWNGRADED:\n",
"\n",
" setuptools 65.3.0-py37h89c1867_0 --> 59.8.0-py37h89c1867_1 None\n",
"\n",
"\n",
"\n",
"Downloading and Extracting Packages\n",
"scikit-learn-1.0.2 | 7.8 MB | : 100% 1.0/1 [00:01<00:00, 1.37s/it] \n",
"pytorch-scatter-2.0. | 6.1 MB | : 100% 1.0/1 [00:06<00:00, 6.18s/it]\n",
"pytorch-geometric-1. | 445 KB | : 100% 1.0/1 [00:02<00:00, 2.53s/it]\n",
"scipy-1.7.3 | 21.8 MB | : 100% 1.0/1 [00:03<00:00, 3.06s/it]\n",
"python-dateutil-2.8. | 240 KB | : 100% 1.0/1 [00:00<00:00, 21.48it/s]\n",
"pytorch-spline-conv- | 736 KB | : 100% 1.0/1 [00:01<00:00, 1.00s/it]\n",
"pytorch-sparse-0.6.1 | 2.9 MB | : 100% 1.0/1 [00:07<00:00, 7.51s/it]\n",
"pyparsing-3.0.9 | 79 KB | : 100% 1.0/1 [00:00<00:00, 26.32it/s]\n",
"pytorch-cluster-1.5. | 1.2 MB | : 100% 1.0/1 [00:02<00:00, 2.78s/it]\n",
"jinja2-3.1.2 | 99 KB | : 100% 1.0/1 [00:00<00:00, 20.28it/s]\n",
"decorator-4.4.2 | 11 KB | : 100% 1.0/1 [00:00<00:00, 21.57it/s]\n",
"joblib-1.2.0 | 205 KB | : 100% 1.0/1 [00:00<00:00, 15.04it/s]\n",
"pytz-2022.4 | 232 KB | : 100% 1.0/1 [00:00<00:00, 10.21it/s]\n",
"python-louvain-0.15 | 13 KB | : 100% 1.0/1 [00:00<00:00, 3.34it/s]\n",
"googledrivedownloade | 7 KB | : 100% 1.0/1 [00:00<00:00, 3.33it/s]\n",
"threadpoolctl-3.1.0 | 18 KB | : 100% 1.0/1 [00:00<00:00, 29.40it/s]\n",
"markupsafe-2.1.1 | 22 KB | : 100% 1.0/1 [00:00<00:00, 28.62it/s]\n",
"pandas-1.2.3 | 11.8 MB | : 100% 1.0/1 [00:02<00:00, 2.08s/it] \n",
"networkx-2.5.1 | 1.2 MB | : 100% 1.0/1 [00:01<00:00, 1.39s/it]\n",
"setuptools-59.8.0 | 1.0 MB | : 100% 1.0/1 [00:00<00:00, 4.25it/s]\n",
"Preparing transaction: / \b\b- \b\b\\ \b\bdone\n",
"Verifying transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
"Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
"Retrieving notices: ...working... done\n"
]
}
],
"source": [
"!conda install -c rusty1s pytorch-geometric=1.7.2"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ppxv6Mdkalbc"
},
"source": [
"### Install Diffusers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mgQA_XN-XGY2",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "85392615-b6a4-4052-9d2a-79604be62c94"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content\n",
"Cloning into 'diffusers'...\n",
"remote: Enumerating objects: 9298, done.\u001b[K\n",
"remote: Counting objects: 100% (40/40), done.\u001b[K\n",
"remote: Compressing objects: 100% (23/23), done.\u001b[K\n",
"remote: Total 9298 (delta 17), reused 23 (delta 11), pack-reused 9258\u001b[K\n",
"Receiving objects: 100% (9298/9298), 7.38 MiB | 5.28 MiB/s, done.\n",
"Resolving deltas: 100% (6168/6168), done.\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m757.0/757.0 kB\u001b[0m \u001b[31m52.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m163.5/163.5 kB\u001b[0m \u001b[31m21.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m596.3/596.3 kB\u001b[0m \u001b[31m51.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m432.7/432.7 kB\u001b[0m \u001b[31m36.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.3/5.3 MB\u001b[0m \u001b[31m90.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.3/35.3 MB\u001b[0m \u001b[31m39.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.1/115.1 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m948.0/948.0 kB\u001b[0m \u001b[31m63.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.2/212.2 kB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m95.8/95.8 kB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.8/140.8 kB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m104.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m148.0/148.0 kB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m231.3/231.3 kB\u001b[0m \u001b[31m30.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.8/94.8 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
}
],
"source": [
"%cd /content\n",
"\n",
"# install latest HF diffusers (will update to the release once added)\n",
"!git clone https://github.com/huggingface/diffusers.git\n",
"!pip install -q /content/diffusers\n",
"\n",
"# dependencies for diffusers\n",
"!pip install -q datasets transformers"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LZO6AJKuJKO8"
},
"source": [
"Check that torch is installed correctly and utilizing the GPU in the colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gZt7BNi1e1PA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
},
"outputId": "a0e1832c-9c02-49aa-cff8-1339e6cdc889"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"True\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'1.8.2'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"import torch\n",
"print(torch.cuda.is_available())\n",
"torch.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KLE7CqlfJNUO"
},
"source": [
"### Install Chemistry-specific Dependencies\n",
"\n",
"Install RDKit, a tool for working with and visualizing chemsitry in python (you use this to visualize the generate models later)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0CPv_NvehRz3",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6ee0ae4e-4511-4816-de29-22b1c21d49bc"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting rdkit\n",
" Downloading rdkit-2022.3.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.8 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m36.8/36.8 MB\u001b[0m \u001b[31m34.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: Pillow in /usr/local/lib/python3.7/site-packages (from rdkit) (9.2.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (from rdkit) (1.21.6)\n",
"Installing collected packages: rdkit\n",
"Successfully installed rdkit-2022.3.5\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
}
],
"source": [
"!pip install rdkit"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "88GaDbDPxJ5I"
},
"source": [
"### Get viewer from nglview\n",
"\n",
"The model you will use outputs a position matrix tensor. This pytorch geometric data object will have many features (positions, known features, edge features -- all tensors).\n",
"The data we give to the model will also have a rdmol object (which can extract features to geometric if needed).\n",
"The rdmol in this object is a source of ground truth for the generated molecules.\n",
"\n",
"You will use one rendering function from nglviewer later!\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jcl8GCS2mz6t",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "99b5cc40-67bb-4d8e-faa0-47d7cb33e98f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting nglview\n",
" Downloading nglview-3.0.3.tar.gz (5.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.7/5.7 MB\u001b[0m \u001b[31m91.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (from nglview) (1.21.6)\n",
"Collecting jupyterlab-widgets\n",
" Downloading jupyterlab_widgets-3.0.3-py3-none-any.whl (384 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m384.1/384.1 kB\u001b[0m \u001b[31m40.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting ipywidgets>=7\n",
" Downloading ipywidgets-8.0.2-py3-none-any.whl (134 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.4/134.4 kB\u001b[0m \u001b[31m21.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting widgetsnbextension~=4.0\n",
" Downloading widgetsnbextension-4.0.3-py3-none-any.whl (2.0 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m84.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting ipython>=6.1.0\n",
" Downloading ipython-7.34.0-py3-none-any.whl (793 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m793.8/793.8 kB\u001b[0m \u001b[31m60.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting ipykernel>=4.5.1\n",
" Downloading ipykernel-6.16.0-py3-none-any.whl (138 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.4/138.4 kB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting traitlets>=4.3.1\n",
" Downloading traitlets-5.4.0-py3-none-any.whl (107 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.1/107.1 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/site-packages (from ipykernel>=4.5.1->ipywidgets>=7->nglview) (21.3)\n",
"Collecting pyzmq>=17\n",
" Downloading pyzmq-24.0.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.1 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m68.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting matplotlib-inline>=0.1\n",
" Downloading matplotlib_inline-0.1.6-py3-none-any.whl (9.4 kB)\n",
"Collecting tornado>=6.1\n",
" Downloading tornado-6.2-cp37-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (423 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m424.0/424.0 kB\u001b[0m \u001b[31m41.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting nest-asyncio\n",
" Downloading nest_asyncio-1.5.6-py3-none-any.whl (5.2 kB)\n",
"Collecting debugpy>=1.0\n",
" Downloading debugpy-1.6.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.8 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m83.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting psutil\n",
" Downloading psutil-5.9.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (281 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m281.3/281.3 kB\u001b[0m \u001b[31m33.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting jupyter-client>=6.1.12\n",
" Downloading jupyter_client-7.4.2-py3-none-any.whl (132 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.2/132.2 kB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting pickleshare\n",
" Downloading pickleshare-0.7.5-py2.py3-none-any.whl (6.9 kB)\n",
"Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/site-packages (from ipython>=6.1.0->ipywidgets>=7->nglview) (59.8.0)\n",
"Collecting backcall\n",
" Downloading backcall-0.2.0-py2.py3-none-any.whl (11 kB)\n",
"Collecting pexpect>4.3\n",
" Downloading pexpect-4.8.0-py2.py3-none-any.whl (59 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.0/59.0 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting pygments\n",
" Downloading Pygments-2.13.0-py3-none-any.whl (1.1 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m70.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting jedi>=0.16\n",
" Downloading jedi-0.18.1-py2.py3-none-any.whl (1.6 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m83.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0\n",
" Downloading prompt_toolkit-3.0.31-py3-none-any.whl (382 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m382.3/382.3 kB\u001b[0m \u001b[31m40.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.7/site-packages (from ipython>=6.1.0->ipywidgets>=7->nglview) (4.4.2)\n",
"Collecting parso<0.9.0,>=0.8.0\n",
" Downloading parso-0.8.3-py2.py3-none-any.whl (100 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m100.8/100.8 kB\u001b[0m \u001b[31m14.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.7/site-packages (from jupyter-client>=6.1.12->ipykernel>=4.5.1->ipywidgets>=7->nglview) (2.8.2)\n",
"Collecting entrypoints\n",
" Downloading entrypoints-0.4-py3-none-any.whl (5.3 kB)\n",
"Collecting jupyter-core>=4.9.2\n",
" Downloading jupyter_core-4.11.1-py3-none-any.whl (88 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.4/88.4 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting ptyprocess>=0.5\n",
" Downloading ptyprocess-0.7.0-py2.py3-none-any.whl (13 kB)\n",
"Collecting wcwidth\n",
" Downloading wcwidth-0.2.5-py2.py3-none-any.whl (30 kB)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/site-packages (from packaging->ipykernel>=4.5.1->ipywidgets>=7->nglview) (3.0.9)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.12->ipykernel>=4.5.1->ipywidgets>=7->nglview) (1.16.0)\n",
"Building wheels for collected packages: nglview\n",
" Building wheel for nglview (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for nglview: filename=nglview-3.0.3-py3-none-any.whl size=8057538 sha256=b7e1071bb91822e48515bf27f4e6b197c6e85e06b90912b3439edc8be1e29514\n",
" Stored in directory: /root/.cache/pip/wheels/01/0c/49/c6f79d8edba8fe89752bf20de2d99040bfa57db0548975c5d5\n",
"Successfully built nglview\n",
"Installing collected packages: wcwidth, ptyprocess, pickleshare, backcall, widgetsnbextension, traitlets, tornado, pyzmq, pygments, psutil, prompt-toolkit, pexpect, parso, nest-asyncio, jupyterlab-widgets, entrypoints, debugpy, matplotlib-inline, jupyter-core, jedi, jupyter-client, ipython, ipykernel, ipywidgets, nglview\n",
"Successfully installed backcall-0.2.0 debugpy-1.6.3 entrypoints-0.4 ipykernel-6.16.0 ipython-7.34.0 ipywidgets-8.0.2 jedi-0.18.1 jupyter-client-7.4.2 jupyter-core-4.11.1 jupyterlab-widgets-3.0.3 matplotlib-inline-0.1.6 nest-asyncio-1.5.6 nglview-3.0.3 parso-0.8.3 pexpect-4.8.0 pickleshare-0.7.5 prompt-toolkit-3.0.31 psutil-5.9.2 ptyprocess-0.7.0 pygments-2.13.0 pyzmq-24.0.1 tornado-6.2 traitlets-5.4.0 wcwidth-0.2.5 widgetsnbextension-4.0.3\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"pexpect",
"pickleshare",
"wcwidth"
]
}
}
},
"metadata": {}
}
],
"source": [
"!pip install nglview"
]
},
{
"cell_type": "markdown",
"source": [
"## Create a diffusion model"
],
"metadata": {
"id": "8t8_e_uVLdKB"
}
},
{
"cell_type": "markdown",
"source": [
"### Model class(es)"
],
"metadata": {
"id": "G0rMncVtNSqU"
}
},
{
"cell_type": "markdown",
"source": [
"Imports"
],
"metadata": {
"id": "L5FEXz5oXkzt"
}
},
{
"cell_type": "code",
"source": [
"# Model adapted from GeoDiff https://github.com/MinkaiXu/GeoDiff\n",
"# Model inspired by https://github.com/DeepGraphLearning/torchdrug/tree/master/torchdrug/models\n",
"from dataclasses import dataclass\n",
"from typing import Callable, Tuple, Union\n",
"\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn.functional as F\n",
"from torch import Tensor, nn\n",
"from torch.nn import Embedding, Linear, Module, ModuleList, Sequential\n",
"\n",
"from torch_geometric.nn import MessagePassing, radius, radius_graph\n",
"from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size\n",
"from torch_geometric.utils import dense_to_sparse, to_dense_adj\n",
"from torch_scatter import scatter_add\n",
"from torch_sparse import SparseTensor, coalesce\n",
"\n",
"from diffusers.configuration_utils import ConfigMixin, register_to_config\n",
"from diffusers.modeling_utils import ModelMixin\n",
"from diffusers.utils import BaseOutput\n"
],
"metadata": {
"id": "-3-P4w5sXkRU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Helper classes"
],
"metadata": {
"id": "EzJQXPN_XrMX"
}
},
{
"cell_type": "code",
"source": [
"@dataclass\n",
"class MoleculeGNNOutput(BaseOutput):\n",
" \"\"\"\n",
" Args:\n",
" sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n",
" Hidden states output. Output of last layer of model.\n",
" \"\"\"\n",
"\n",
" sample: torch.FloatTensor\n",
"\n",
"\n",
"class MultiLayerPerceptron(nn.Module):\n",
" \"\"\"\n",
" Multi-layer Perceptron. Note there is no activation or dropout in the last layer.\n",
" Args:\n",
" input_dim (int): input dimension\n",
" hidden_dim (list of int): hidden dimensions\n",
" activation (str or function, optional): activation function\n",
" dropout (float, optional): dropout rate\n",
" \"\"\"\n",
"\n",
" def __init__(self, input_dim, hidden_dims, activation=\"relu\", dropout=0):\n",
" super(MultiLayerPerceptron, self).__init__()\n",
"\n",
" self.dims = [input_dim] + hidden_dims\n",
" if isinstance(activation, str):\n",
" self.activation = getattr(F, activation)\n",
" else:\n",
" print(f\"Warning, activation passed {activation} is not string and ignored\")\n",
" self.activation = None\n",
" if dropout > 0:\n",
" self.dropout = nn.Dropout(dropout)\n",
" else:\n",
" self.dropout = None\n",
"\n",
" self.layers = nn.ModuleList()\n",
" for i in range(len(self.dims) - 1):\n",
" self.layers.append(nn.Linear(self.dims[i], self.dims[i + 1]))\n",
"\n",
" def forward(self, x):\n",
" \"\"\"\"\"\"\n",
" for i, layer in enumerate(self.layers):\n",
" x = layer(x)\n",
" if i < len(self.layers) - 1:\n",
" if self.activation:\n",
" x = self.activation(x)\n",
" if self.dropout:\n",
" x = self.dropout(x)\n",
" return x\n",
"\n",
"\n",
"class ShiftedSoftplus(torch.nn.Module):\n",
" def __init__(self):\n",
" super(ShiftedSoftplus, self).__init__()\n",
" self.shift = torch.log(torch.tensor(2.0)).item()\n",
"\n",
" def forward(self, x):\n",
" return F.softplus(x) - self.shift\n",
"\n",
"\n",
"class CFConv(MessagePassing):\n",
" def __init__(self, in_channels, out_channels, num_filters, mlp, cutoff, smooth):\n",
" super(CFConv, self).__init__(aggr=\"add\")\n",
" self.lin1 = Linear(in_channels, num_filters, bias=False)\n",
" self.lin2 = Linear(num_filters, out_channels)\n",
" self.nn = mlp\n",
" self.cutoff = cutoff\n",
" self.smooth = smooth\n",
"\n",
" self.reset_parameters()\n",
"\n",
" def reset_parameters(self):\n",
" torch.nn.init.xavier_uniform_(self.lin1.weight)\n",
" torch.nn.init.xavier_uniform_(self.lin2.weight)\n",
" self.lin2.bias.data.fill_(0)\n",
"\n",
" def forward(self, x, edge_index, edge_length, edge_attr):\n",
" if self.smooth:\n",
" C = 0.5 * (torch.cos(edge_length * np.pi / self.cutoff) + 1.0)\n",
" C = C * (edge_length <= self.cutoff) * (edge_length >= 0.0) # Modification: cutoff\n",
" else:\n",
" C = (edge_length <= self.cutoff).float()\n",
" W = self.nn(edge_attr) * C.view(-1, 1)\n",
"\n",
" x = self.lin1(x)\n",
" x = self.propagate(edge_index, x=x, W=W)\n",
" x = self.lin2(x)\n",
" return x\n",
"\n",
" def message(self, x_j: torch.Tensor, W) -> torch.Tensor:\n",
" return x_j * W\n",
"\n",
"\n",
"class InteractionBlock(torch.nn.Module):\n",
" def __init__(self, hidden_channels, num_gaussians, num_filters, cutoff, smooth):\n",
" super(InteractionBlock, self).__init__()\n",
" mlp = Sequential(\n",
" Linear(num_gaussians, num_filters),\n",
" ShiftedSoftplus(),\n",
" Linear(num_filters, num_filters),\n",
" )\n",
" self.conv = CFConv(hidden_channels, hidden_channels, num_filters, mlp, cutoff, smooth)\n",
" self.act = ShiftedSoftplus()\n",
" self.lin = Linear(hidden_channels, hidden_channels)\n",
"\n",
" def forward(self, x, edge_index, edge_length, edge_attr):\n",
" x = self.conv(x, edge_index, edge_length, edge_attr)\n",
" x = self.act(x)\n",
" x = self.lin(x)\n",
" return x\n",
"\n",
"\n",
"class SchNetEncoder(Module):\n",
" def __init__(\n",
" self, hidden_channels=128, num_filters=128, num_interactions=6, edge_channels=100, cutoff=10.0, smooth=False\n",
" ):\n",
" super().__init__()\n",
"\n",
" self.hidden_channels = hidden_channels\n",
" self.num_filters = num_filters\n",
" self.num_interactions = num_interactions\n",
" self.cutoff = cutoff\n",
"\n",
" self.embedding = Embedding(100, hidden_channels, max_norm=10.0)\n",
"\n",
" self.interactions = ModuleList()\n",
" for _ in range(num_interactions):\n",
" block = InteractionBlock(hidden_channels, edge_channels, num_filters, cutoff, smooth)\n",
" self.interactions.append(block)\n",
"\n",
" def forward(self, z, edge_index, edge_length, edge_attr, embed_node=True):\n",
" if embed_node:\n",
" assert z.dim() == 1 and z.dtype == torch.long\n",
" h = self.embedding(z)\n",
" else:\n",
" h = z\n",
" for interaction in self.interactions:\n",
" h = h + interaction(h, edge_index, edge_length, edge_attr)\n",
"\n",
" return h\n",
"\n",
"\n",
"class GINEConv(MessagePassing):\n",
" \"\"\"\n",
" Custom class of the graph isomorphism operator from the \"How Powerful are Graph Neural Networks?\n",
" https://arxiv.org/abs/1810.00826 paper. Note that this implementation has the added option of a custom activation.\n",
" \"\"\"\n",
"\n",
" def __init__(self, mlp: Callable, eps: float = 0.0, train_eps: bool = False, activation=\"softplus\", **kwargs):\n",
" super(GINEConv, self).__init__(aggr=\"add\", **kwargs)\n",
" self.nn = mlp\n",
" self.initial_eps = eps\n",
"\n",
" if isinstance(activation, str):\n",
" self.activation = getattr(F, activation)\n",
" else:\n",
" self.activation = None\n",
"\n",
" if train_eps:\n",
" self.eps = torch.nn.Parameter(torch.Tensor([eps]))\n",
" else:\n",
" self.register_buffer(\"eps\", torch.Tensor([eps]))\n",
"\n",
" def forward(\n",
" self, x: Union[Tensor, OptPairTensor], edge_index: Adj, edge_attr: OptTensor = None, size: Size = None\n",
" ) -> torch.Tensor:\n",
" \"\"\"\"\"\"\n",
" if isinstance(x, torch.Tensor):\n",
" x: OptPairTensor = (x, x)\n",
"\n",
" # Node and edge feature dimensionalites need to match.\n",
" if isinstance(edge_index, torch.Tensor):\n",
" assert edge_attr is not None\n",
" assert x[0].size(-1) == edge_attr.size(-1)\n",
" elif isinstance(edge_index, SparseTensor):\n",
" assert x[0].size(-1) == edge_index.size(-1)\n",
"\n",
" # propagate_type: (x: OptPairTensor, edge_attr: OptTensor)\n",
" out = self.propagate(edge_index, x=x, edge_attr=edge_attr, size=size)\n",
"\n",
" x_r = x[1]\n",
" if x_r is not None:\n",
" out += (1 + self.eps) * x_r\n",
"\n",
" return self.nn(out)\n",
"\n",
" def message(self, x_j: torch.Tensor, edge_attr: torch.Tensor) -> torch.Tensor:\n",
" if self.activation:\n",
" return self.activation(x_j + edge_attr)\n",
" else:\n",
" return x_j + edge_attr\n",
"\n",
" def __repr__(self):\n",
" return \"{}(nn={})\".format(self.__class__.__name__, self.nn)\n",
"\n",
"\n",
"class GINEncoder(torch.nn.Module):\n",
" def __init__(self, hidden_dim, num_convs=3, activation=\"relu\", short_cut=True, concat_hidden=False):\n",
" super().__init__()\n",
"\n",
" self.hidden_dim = hidden_dim\n",
" self.num_convs = num_convs\n",
" self.short_cut = short_cut\n",
" self.concat_hidden = concat_hidden\n",
" self.node_emb = nn.Embedding(100, hidden_dim)\n",
"\n",
" if isinstance(activation, str):\n",
" self.activation = getattr(F, activation)\n",
" else:\n",
" self.activation = None\n",
"\n",
" self.convs = nn.ModuleList()\n",
" for i in range(self.num_convs):\n",
" self.convs.append(\n",
" GINEConv(\n",
" MultiLayerPerceptron(hidden_dim, [hidden_dim, hidden_dim], activation=activation),\n",
" activation=activation,\n",
" )\n",
" )\n",
"\n",
" def forward(self, z, edge_index, edge_attr):\n",
" \"\"\"\n",
" Input:\n",
" data: (torch_geometric.data.Data): batched graph edge_index: bond indices of the original graph (num_node,\n",
" hidden) edge_attr: edge feature tensor with shape (num_edge, hidden)\n",
" Output:\n",
" node_feature: graph feature\n",
" \"\"\"\n",
"\n",
" node_attr = self.node_emb(z) # (num_node, hidden)\n",
"\n",
" hiddens = []\n",
" conv_input = node_attr # (num_node, hidden)\n",
"\n",
" for conv_idx, conv in enumerate(self.convs):\n",
" hidden = conv(conv_input, edge_index, edge_attr)\n",
" if conv_idx < len(self.convs) - 1 and self.activation is not None:\n",
" hidden = self.activation(hidden)\n",
" assert hidden.shape == conv_input.shape\n",
" if self.short_cut and hidden.shape == conv_input.shape:\n",
" hidden += conv_input\n",
"\n",
" hiddens.append(hidden)\n",
" conv_input = hidden\n",
"\n",
" if self.concat_hidden:\n",
" node_feature = torch.cat(hiddens, dim=-1)\n",
" else:\n",
" node_feature = hiddens[-1]\n",
"\n",
" return node_feature\n",
"\n",
"\n",
"class MLPEdgeEncoder(Module):\n",
" def __init__(self, hidden_dim=100, activation=\"relu\"):\n",
" super().__init__()\n",
" self.hidden_dim = hidden_dim\n",
" self.bond_emb = Embedding(100, embedding_dim=self.hidden_dim)\n",
" self.mlp = MultiLayerPerceptron(1, [self.hidden_dim, self.hidden_dim], activation=activation)\n",
"\n",
" @property\n",
" def out_channels(self):\n",
" return self.hidden_dim\n",
"\n",
" def forward(self, edge_length, edge_type):\n",
" \"\"\"\n",
" Input:\n",
" edge_length: The length of edges, shape=(E, 1). edge_type: The type pf edges, shape=(E,)\n",
" Returns:\n",
" edge_attr: The representation of edges. (E, 2 * num_gaussians)\n",
" \"\"\"\n",
" d_emb = self.mlp(edge_length) # (num_edge, hidden_dim)\n",
" edge_attr = self.bond_emb(edge_type) # (num_edge, hidden_dim)\n",
" return d_emb * edge_attr # (num_edge, hidden)\n",
"\n",
"\n",
"def assemble_atom_pair_feature(node_attr, edge_index, edge_attr):\n",
" h_row, h_col = node_attr[edge_index[0]], node_attr[edge_index[1]]\n",
" h_pair = torch.cat([h_row * h_col, edge_attr], dim=-1) # (E, 2H)\n",
" return h_pair\n",
"\n",
"\n",
"def _extend_graph_order(num_nodes, edge_index, edge_type, order=3):\n",
" \"\"\"\n",
" Args:\n",
" num_nodes: Number of atoms.\n",
" edge_index: Bond indices of the original graph.\n",
" edge_type: Bond types of the original graph.\n",
" order: Extension order.\n",
" Returns:\n",
" new_edge_index: Extended edge indices. new_edge_type: Extended edge types.\n",
" \"\"\"\n",
"\n",
" def binarize(x):\n",
" return torch.where(x > 0, torch.ones_like(x), torch.zeros_like(x))\n",
"\n",
" def get_higher_order_adj_matrix(adj, order):\n",
" \"\"\"\n",
" Args:\n",
" adj: (N, N)\n",
" type_mat: (N, N)\n",
" Returns:\n",
" Following attributes will be updated:\n",
" - edge_index\n",
" - edge_type\n",
" Following attributes will be added to the data object:\n",
" - bond_edge_index: Original edge_index.\n",
" \"\"\"\n",
" adj_mats = [\n",
" torch.eye(adj.size(0), dtype=torch.long, device=adj.device),\n",
" binarize(adj + torch.eye(adj.size(0), dtype=torch.long, device=adj.device)),\n",
" ]\n",
"\n",
" for i in range(2, order + 1):\n",
" adj_mats.append(binarize(adj_mats[i - 1] @ adj_mats[1]))\n",
" order_mat = torch.zeros_like(adj)\n",
"\n",
" for i in range(1, order + 1):\n",
" order_mat += (adj_mats[i] - adj_mats[i - 1]) * i\n",
"\n",
" return order_mat\n",
"\n",
" num_types = 22\n",
" # given from len(BOND_TYPES), where BOND_TYPES = {t: i for i, t in enumerate(BT.names.values())}\n",
" # from rdkit.Chem.rdchem import BondType as BT\n",
" N = num_nodes\n",
" adj = to_dense_adj(edge_index).squeeze(0)\n",
" adj_order = get_higher_order_adj_matrix(adj, order) # (N, N)\n",
"\n",
" type_mat = to_dense_adj(edge_index, edge_attr=edge_type).squeeze(0) # (N, N)\n",
" type_highorder = torch.where(adj_order > 1, num_types + adj_order - 1, torch.zeros_like(adj_order))\n",
" assert (type_mat * type_highorder == 0).all()\n",
" type_new = type_mat + type_highorder\n",
"\n",
" new_edge_index, new_edge_type = dense_to_sparse(type_new)\n",
" _, edge_order = dense_to_sparse(adj_order)\n",
"\n",
" # data.bond_edge_index = data.edge_index # Save original edges\n",
" new_edge_index, new_edge_type = coalesce(new_edge_index, new_edge_type.long(), N, N) # modify data\n",
"\n",
" return new_edge_index, new_edge_type\n",
"\n",
"\n",
"def _extend_to_radius_graph(pos, edge_index, edge_type, cutoff, batch, unspecified_type_number=0, is_sidechain=None):\n",
" assert edge_type.dim() == 1\n",
" N = pos.size(0)\n",
"\n",
" bgraph_adj = torch.sparse.LongTensor(edge_index, edge_type, torch.Size([N, N]))\n",
"\n",
" if is_sidechain is None:\n",
" rgraph_edge_index = radius_graph(pos, r=cutoff, batch=batch) # (2, E_r)\n",
" else:\n",
" # fetch sidechain and its batch index\n",
" is_sidechain = is_sidechain.bool()\n",
" dummy_index = torch.arange(pos.size(0), device=pos.device)\n",
" sidechain_pos = pos[is_sidechain]\n",
" sidechain_index = dummy_index[is_sidechain]\n",
" sidechain_batch = batch[is_sidechain]\n",
"\n",
" assign_index = radius(x=pos, y=sidechain_pos, r=cutoff, batch_x=batch, batch_y=sidechain_batch)\n",
" r_edge_index_x = assign_index[1]\n",
" r_edge_index_y = assign_index[0]\n",
" r_edge_index_y = sidechain_index[r_edge_index_y]\n",
"\n",
" rgraph_edge_index1 = torch.stack((r_edge_index_x, r_edge_index_y)) # (2, E)\n",
" rgraph_edge_index2 = torch.stack((r_edge_index_y, r_edge_index_x)) # (2, E)\n",
" rgraph_edge_index = torch.cat((rgraph_edge_index1, rgraph_edge_index2), dim=-1) # (2, 2E)\n",
" # delete self loop\n",
" rgraph_edge_index = rgraph_edge_index[:, (rgraph_edge_index[0] != rgraph_edge_index[1])]\n",
"\n",
" rgraph_adj = torch.sparse.LongTensor(\n",
" rgraph_edge_index,\n",
" torch.ones(rgraph_edge_index.size(1)).long().to(pos.device) * unspecified_type_number,\n",
" torch.Size([N, N]),\n",
" )\n",
"\n",
" composed_adj = (bgraph_adj + rgraph_adj).coalesce() # Sparse (N, N, T)\n",
"\n",
" new_edge_index = composed_adj.indices()\n",
" new_edge_type = composed_adj.values().long()\n",
"\n",
" return new_edge_index, new_edge_type\n",
"\n",
"\n",
"def extend_graph_order_radius(\n",
" num_nodes,\n",
" pos,\n",
" edge_index,\n",
" edge_type,\n",
" batch,\n",
" order=3,\n",
" cutoff=10.0,\n",
" extend_order=True,\n",
" extend_radius=True,\n",
" is_sidechain=None,\n",
"):\n",
" if extend_order:\n",
" edge_index, edge_type = _extend_graph_order(\n",
" num_nodes=num_nodes, edge_index=edge_index, edge_type=edge_type, order=order\n",
" )\n",
"\n",
" if extend_radius:\n",
" edge_index, edge_type = _extend_to_radius_graph(\n",
" pos=pos, edge_index=edge_index, edge_type=edge_type, cutoff=cutoff, batch=batch, is_sidechain=is_sidechain\n",
" )\n",
"\n",
" return edge_index, edge_type\n",
"\n",
"\n",
"def get_distance(pos, edge_index):\n",
" return (pos[edge_index[0]] - pos[edge_index[1]]).norm(dim=-1)\n",
"\n",
"\n",
"def graph_field_network(score_d, pos, edge_index, edge_length):\n",
" \"\"\"\n",
" Transformation to make the epsilon predicted from the diffusion model roto-translational equivariant. See equations\n",
" 5-7 of the GeoDiff Paper https://arxiv.org/pdf/2203.02923.pdf\n",
" \"\"\"\n",
" N = pos.size(0)\n",
" dd_dr = (1.0 / edge_length) * (pos[edge_index[0]] - pos[edge_index[1]]) # (E, 3)\n",
" score_pos = scatter_add(dd_dr * score_d, edge_index[0], dim=0, dim_size=N) + scatter_add(\n",
" -dd_dr * score_d, edge_index[1], dim=0, dim_size=N\n",
" ) # (N, 3)\n",
" return score_pos\n",
"\n",
"\n",
"def clip_norm(vec, limit, p=2):\n",
" norm = torch.norm(vec, dim=-1, p=2, keepdim=True)\n",
" denom = torch.where(norm > limit, limit / norm, torch.ones_like(norm))\n",
" return vec * denom\n",
"\n",
"\n",
"def is_local_edge(edge_type):\n",
" return edge_type > 0\n"
],
"metadata": {
"id": "oR1Y56QiLY90"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Main model class!"
],
"metadata": {
"id": "QWrHJFcYXyUB"
}
},
{
"cell_type": "code",
"source": [
"class MoleculeGNN(ModelMixin, ConfigMixin):\n",
" @register_to_config\n",
" def __init__(\n",
" self,\n",
" hidden_dim=128,\n",
" num_convs=6,\n",
" num_convs_local=4,\n",
" cutoff=10.0,\n",
" mlp_act=\"relu\",\n",
" edge_order=3,\n",
" edge_encoder=\"mlp\",\n",
" smooth_conv=True,\n",
" ):\n",
" super().__init__()\n",
" self.cutoff = cutoff\n",
" self.edge_encoder = edge_encoder\n",
" self.edge_order = edge_order\n",
"\n",
" \"\"\"\n",
" edge_encoder: Takes both edge type and edge length as input and outputs a vector [Note]: node embedding is done\n",
" in SchNetEncoder\n",
" \"\"\"\n",
" self.edge_encoder_global = MLPEdgeEncoder(hidden_dim, mlp_act) # get_edge_encoder(config)\n",
" self.edge_encoder_local = MLPEdgeEncoder(hidden_dim, mlp_act) # get_edge_encoder(config)\n",
"\n",
" \"\"\"\n",
" The graph neural network that extracts node-wise features.\n",
" \"\"\"\n",
" self.encoder_global = SchNetEncoder(\n",
" hidden_channels=hidden_dim,\n",
" num_filters=hidden_dim,\n",
" num_interactions=num_convs,\n",
" edge_channels=self.edge_encoder_global.out_channels,\n",
" cutoff=cutoff,\n",
" smooth=smooth_conv,\n",
" )\n",
" self.encoder_local = GINEncoder(\n",
" hidden_dim=hidden_dim,\n",
" num_convs=num_convs_local,\n",
" )\n",
"\n",
" \"\"\"\n",
" `output_mlp` takes a mixture of two nodewise features and edge features as input and outputs\n",
" gradients w.r.t. edge_length (out_dim = 1).\n",
" \"\"\"\n",
" self.grad_global_dist_mlp = MultiLayerPerceptron(\n",
" 2 * hidden_dim, [hidden_dim, hidden_dim // 2, 1], activation=mlp_act\n",
" )\n",
"\n",
" self.grad_local_dist_mlp = MultiLayerPerceptron(\n",
" 2 * hidden_dim, [hidden_dim, hidden_dim // 2, 1], activation=mlp_act\n",
" )\n",
"\n",
" \"\"\"\n",
" Incorporate parameters together\n",
" \"\"\"\n",
" self.model_global = nn.ModuleList([self.edge_encoder_global, self.encoder_global, self.grad_global_dist_mlp])\n",
" self.model_local = nn.ModuleList([self.edge_encoder_local, self.encoder_local, self.grad_local_dist_mlp])\n",
"\n",
" def _forward(\n",
" self,\n",
" atom_type,\n",
" pos,\n",
" bond_index,\n",
" bond_type,\n",
" batch,\n",
" time_step, # NOTE, model trained without timestep performed best\n",
" edge_index=None,\n",
" edge_type=None,\n",
" edge_length=None,\n",
" return_edges=False,\n",
" extend_order=True,\n",
" extend_radius=True,\n",
" is_sidechain=None,\n",
" ):\n",
" \"\"\"\n",
" Args:\n",
" atom_type: Types of atoms, (N, ).\n",
" bond_index: Indices of bonds (not extended, not radius-graph), (2, E).\n",
" bond_type: Bond types, (E, ).\n",
" batch: Node index to graph index, (N, ).\n",
" \"\"\"\n",
" N = atom_type.size(0)\n",
" if edge_index is None or edge_type is None or edge_length is None:\n",
" edge_index, edge_type = extend_graph_order_radius(\n",
" num_nodes=N,\n",
" pos=pos,\n",
" edge_index=bond_index,\n",
" edge_type=bond_type,\n",
" batch=batch,\n",
" order=self.edge_order,\n",
" cutoff=self.cutoff,\n",
" extend_order=extend_order,\n",
" extend_radius=extend_radius,\n",
" is_sidechain=is_sidechain,\n",
" )\n",
" edge_length = get_distance(pos, edge_index).unsqueeze(-1) # (E, 1)\n",
" local_edge_mask = is_local_edge(edge_type) # (E, )\n",
"\n",
" # with the parameterization of NCSNv2\n",
" # DDPM loss implicit handle the noise variance scale conditioning\n",
" sigma_edge = torch.ones(size=(edge_index.size(1), 1), device=pos.device) # (E, 1)\n",
"\n",
" # Encoding global\n",
" edge_attr_global = self.edge_encoder_global(edge_length=edge_length, edge_type=edge_type) # Embed edges\n",
"\n",
" # Global\n",
" node_attr_global = self.encoder_global(\n",
" z=atom_type,\n",
" edge_index=edge_index,\n",
" edge_length=edge_length,\n",
" edge_attr=edge_attr_global,\n",
" )\n",
" # Assemble pairwise features\n",
" h_pair_global = assemble_atom_pair_feature(\n",
" node_attr=node_attr_global,\n",
" edge_index=edge_index,\n",
" edge_attr=edge_attr_global,\n",
" ) # (E_global, 2H)\n",
" # Invariant features of edges (radius graph, global)\n",
" edge_inv_global = self.grad_global_dist_mlp(h_pair_global) * (1.0 / sigma_edge) # (E_global, 1)\n",
"\n",
" # Encoding local\n",
" edge_attr_local = self.edge_encoder_global(edge_length=edge_length, edge_type=edge_type) # Embed edges\n",
" # edge_attr += temb_edge\n",
"\n",
" # Local\n",
" node_attr_local = self.encoder_local(\n",
" z=atom_type,\n",
" edge_index=edge_index[:, local_edge_mask],\n",
" edge_attr=edge_attr_local[local_edge_mask],\n",
" )\n",
" # Assemble pairwise features\n",
" h_pair_local = assemble_atom_pair_feature(\n",
" node_attr=node_attr_local,\n",
" edge_index=edge_index[:, local_edge_mask],\n",
" edge_attr=edge_attr_local[local_edge_mask],\n",
" ) # (E_local, 2H)\n",
"\n",
" # Invariant features of edges (bond graph, local)\n",
" if isinstance(sigma_edge, torch.Tensor):\n",
" edge_inv_local = self.grad_local_dist_mlp(h_pair_local) * (\n",
" 1.0 / sigma_edge[local_edge_mask]\n",
" ) # (E_local, 1)\n",
" else:\n",
" edge_inv_local = self.grad_local_dist_mlp(h_pair_local) * (1.0 / sigma_edge) # (E_local, 1)\n",
"\n",
" if return_edges:\n",
" return edge_inv_global, edge_inv_local, edge_index, edge_type, edge_length, local_edge_mask\n",
" else:\n",
" return edge_inv_global, edge_inv_local\n",
"\n",
" def forward(\n",
" self,\n",
" sample,\n",
" timestep: Union[torch.Tensor, float, int],\n",
" return_dict: bool = True,\n",
" sigma=1.0,\n",
" global_start_sigma=0.5,\n",
" w_global=1.0,\n",
" extend_order=False,\n",
" extend_radius=True,\n",
" clip_local=None,\n",
" clip_global=1000.0,\n",
" ) -> Union[MoleculeGNNOutput, Tuple]:\n",
" r\"\"\"\n",
" Args:\n",
" sample: packed torch geometric object\n",
" timestep (`torch.FloatTensor` or `float` or `int): TODO verify type and shape (batch) timesteps\n",
" return_dict (`bool`, *optional*, defaults to `True`):\n",
" Whether or not to return a [`~models.molecule_gnn.MoleculeGNNOutput`] instead of a plain tuple.\n",
" Returns:\n",
" [`~models.molecule_gnn.MoleculeGNNOutput`] or `tuple`: [`~models.molecule_gnn.MoleculeGNNOutput`] if\n",
" `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.\n",
" \"\"\"\n",
"\n",
" # unpack sample\n",
" atom_type = sample.atom_type\n",
" bond_index = sample.edge_index\n",
" bond_type = sample.edge_type\n",
" num_graphs = sample.num_graphs\n",
" pos = sample.pos\n",
"\n",
" timesteps = torch.full(size=(num_graphs,), fill_value=timestep, dtype=torch.long, device=pos.device)\n",
"\n",
" edge_inv_global, edge_inv_local, edge_index, edge_type, edge_length, local_edge_mask = self._forward(\n",
" atom_type=atom_type,\n",
" pos=sample.pos,\n",
" bond_index=bond_index,\n",
" bond_type=bond_type,\n",
" batch=sample.batch,\n",
" time_step=timesteps,\n",
" return_edges=True,\n",
" extend_order=extend_order,\n",
" extend_radius=extend_radius,\n",
" ) # (E_global, 1), (E_local, 1)\n",
"\n",
" # Important equation in the paper for equivariant features - eqns 5-7 of GeoDiff\n",
" node_eq_local = graph_field_network(\n",
" edge_inv_local, pos, edge_index[:, local_edge_mask], edge_length[local_edge_mask]\n",
" )\n",
" if clip_local is not None:\n",
" node_eq_local = clip_norm(node_eq_local, limit=clip_local)\n",
"\n",
" # Global\n",
" if sigma < global_start_sigma:\n",
" edge_inv_global = edge_inv_global * (1 - local_edge_mask.view(-1, 1).float())\n",
" node_eq_global = graph_field_network(edge_inv_global, pos, edge_index, edge_length)\n",
" node_eq_global = clip_norm(node_eq_global, limit=clip_global)\n",
" else:\n",
" node_eq_global = 0\n",
"\n",
" # Sum\n",
" eps_pos = node_eq_local + node_eq_global * w_global\n",
"\n",
" if not return_dict:\n",
" return (-eps_pos,)\n",
"\n",
" return MoleculeGNNOutput(sample=torch.FloatTensor(-eps_pos).to(pos.device))"
],
"metadata": {
"id": "MCeZA1qQXzoK"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "CCIrPYSJj9wd"
},
"source": [
"### Load pretrained model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YdrAr6Ch--Ab"
},
"source": [
"#### Load a model\n",
"The model used is a design an\n",
"equivariant convolutional layer, named graph field network (GFN).\n",
"\n",
"The warning about `betas` and `alphas` can be ignored, those were moved to the scheduler."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DyCo0nsqjbml",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 172,
"referenced_widgets": [
"d90f304e9560472eacfbdd11e46765eb",
"1c6246f15b654f4daa11c9bcf997b78c",
"c2321b3bff6f490ca12040a20308f555",
"b7feb522161f4cf4b7cc7c1a078ff12d",
"e2d368556e494ae7ae4e2e992af2cd4f",
"bbef741e76ec41b7ab7187b487a383df",
"561f742d418d4721b0670cc8dd62e22c",
"872915dd1bb84f538c44e26badabafdd",
"d022575f1fa2446d891650897f187b4d",
"fdc393f3468c432aa0ada05e238a5436",
"2c9362906e4b40189f16d14aa9a348da",
"6010fc8daa7a44d5aec4b830ec2ebaa1",
"7e0bb1b8d65249d3974200686b193be2",
"ba98aa6d6a884e4ab8bbb5dfb5e4cf7a",
"6526646be5ed415c84d1245b040e629b",
"24d31fc3576e43dd9f8301d2ef3a37ab",
"2918bfaadc8d4b1a9832522c40dfefb8",
"a4bfdca35cc54dae8812720f1b276a08",
"e4901541199b45c6a18824627692fc39",
"f915cf874246446595206221e900b2fe",
"a9e388f22a9742aaaf538e22575c9433",
"42f6c3db29d7484ba6b4f73590abd2f4"
]
},
"outputId": "d6bce9d5-c51e-43a4-e680-e1e81bdfaf45"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading: 0%| | 0.00/3.27M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d90f304e9560472eacfbdd11e46765eb"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading: 0%| | 0.00/401 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "6010fc8daa7a44d5aec4b830ec2ebaa1"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"The config attributes {'type': 'diffusion', 'network': 'dualenc', 'beta_schedule': 'sigmoid', 'beta_start': 1e-07, 'beta_end': 0.002, 'num_diffusion_timesteps': 5000} were passed to MoleculeGNN, but are not expected and will be ignored. Please verify your config.json configuration file.\n",
"Some weights of the model checkpoint at fusing/gfn-molecule-gen-drugs were not used when initializing MoleculeGNN: ['betas', 'alphas']\n",
"- This IS expected if you are initializing MoleculeGNN from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing MoleculeGNN from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
}
],
"source": [
"DEVICE = 'cuda'\n",
"model = MoleculeGNN.from_pretrained(\"fusing/gfn-molecule-gen-drugs\").to(DEVICE)"
]
},
{
"cell_type": "markdown",
"source": [
"The warnings above are because the pre-trained model was uploaded before cleaning the code!"
],
"metadata": {
"id": "HdclRaqoUWUD"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "PlOkPySoJ1m9"
},
"source": [
"#### Create scheduler\n",
"Note, other schedulers are used in the paper for slightly improved performance over DDPM."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nNHnIk9CkAb2"
},
"outputs": [],
"source": [
"from diffusers import DDPMScheduler"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RnDJdDBztjFF"
},
"outputs": [],
"source": [
"num_timesteps = 1000\n",
"scheduler = DDPMScheduler(num_train_timesteps=num_timesteps,beta_schedule=\"sigmoid\",beta_start=1e-7, beta_end=2e-3, clip_sample=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1vh3fpSAflkL"
},
"source": [
"### Get a dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B6qzaGjVKFVk"
},
"source": [
"Grab a google tool so we can upload our data directly. Note you need to download the data from ***this [file](https://huggingface.co/datasets/fusing/geodiff-example-data/blob/main/data/molecules.pkl)***\n",
"\n",
"(direct downloading from the hub does not yet work for this datatype)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jbLl3EJdgj3x"
},
"outputs": [],
"source": [
"# from google.colab import files"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "E591lVuTgxPE"
},
"outputs": [],
"source": [
"# uploaded = files.upload()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KUNxfK3ln98Q"
},
"source": [
"Load the dataset with torch."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7L4iOShTpcQX",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7f2dcd29-493e-44de-98d1-3ad50f109a4a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2022-10-12 18:32:19-- https://huggingface.co/datasets/fusing/geodiff-example-data/resolve/main/data/molecules.pkl\n",
"Resolving huggingface.co (huggingface.co)... 44.195.102.200, 52.5.54.249, 54.210.225.113, ...\n",
"Connecting to huggingface.co (huggingface.co)|44.195.102.200|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 127774 (125K) [application/octet-stream]\n",
"Saving to: ‘molecules.pkl’\n",
"\n",
"molecules.pkl 100%[===================>] 124.78K 180KB/s in 0.7s \n",
"\n",
"2022-10-12 18:32:20 (180 KB/s) - ‘molecules.pkl’ saved [127774/127774]\n",
"\n"
]
}
],
"source": [
"import torch\n",
"import numpy as np\n",
"\n",
"!wget https://huggingface.co/datasets/fusing/geodiff-example-data/resolve/main/data/molecules.pkl\n",
"dataset = torch.load('/content/molecules.pkl')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QZcmy1EvKQRk"
},
"source": [
"Print out one entry of the dataset, it contains molecular formulas, atom types, positions, and more."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JVjz6iH_H6Eh",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "898cb0cf-a0b3-411b-fd4c-bea1fbfd17fe"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Data(atom_type=[51], bond_edge_index=[2, 108], edge_index=[2, 598], edge_order=[598], edge_type=[598], idx=[1], is_bond=[598], num_nodes_per_graph=[1], num_pos_ref=[1], nx=, pos=[51, 3], pos_ref=[255, 3], rdmol=<rdkit.Chem.rdchem.Mol object at 0x7f707d2cb130>, smiles=\"CC1CCCN(C(=O)C2CCN(S(=O)(=O)c3cccc4nonc34)CC2)C1\")"
]
},
"metadata": {},
"execution_count": 20
}
],
"source": [
"dataset[0]"
]
},
{
"cell_type": "markdown",
"source": [
"## Run the diffusion process"
],
"metadata": {
"id": "vHNiZAUxNgoy"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "jZ1KZrxKqENg"
},
"source": [
"#### Helper Functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "s240tYueqKKf"
},
"outputs": [],
"source": [
"from torch_geometric.data import Data, Batch\n",
"from torch_scatter import scatter_add, scatter_mean\n",
"from tqdm import tqdm\n",
"import copy\n",
"import os\n",
"\n",
"def repeat_data(data: Data, num_repeat) -> Batch:\n",
" datas = [copy.deepcopy(data) for i in range(num_repeat)]\n",
" return Batch.from_data_list(datas)\n",
"\n",
"def repeat_batch(batch: Batch, num_repeat) -> Batch:\n",
" datas = batch.to_data_list()\n",
" new_data = []\n",
" for i in range(num_repeat):\n",
" new_data += copy.deepcopy(datas)\n",
" return Batch.from_data_list(new_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AMnQTk0eqT7Z"
},
"source": [
"#### Constants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WYGkzqgzrHmF"
},
"outputs": [],
"source": [
"num_samples = 1 # solutions per molecule\n",
"num_molecules = 3\n",
"\n",
"DEVICE = 'cuda'\n",
"sampling_type = 'ddpm_noisy' #'' # paper also uses \"generalize\" and \"ld\"\n",
"# constants for inference\n",
"w_global = 0.5 #0,.3 for qm9\n",
"global_start_sigma = 0.5\n",
"eta = 1.0\n",
"clip_local = None\n",
"clip_pos = None\n",
"\n",
"# constands for data handling\n",
"save_traj = False\n",
"save_data = False\n",
"output_dir = '/content/'"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-xD5bJ3SqM7t"
},
"source": [
"#### Generate samples!\n",
"Note that the 3d representation of a molecule is referred to as the **conformation**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "x9xuLUNg26z1",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "236d2a60-09ed-4c4d-97c1-6e3c0f2d26c4"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
" after removing the cwd from sys.path.\n",
"100%|██████████| 5/5 [00:55<00:00, 11.06s/it]\n"
]
}
],
"source": [
"results = []\n",
"\n",
"# define sigmas\n",
"sigmas = torch.tensor(1.0 - scheduler.alphas_cumprod).sqrt() / torch.tensor(scheduler.alphas_cumprod).sqrt()\n",
"sigmas = sigmas.to(DEVICE)\n",
"\n",
"for count, data in enumerate(tqdm(dataset)):\n",
" num_samples = max(data.pos_ref.size(0) // data.num_nodes, 1)\n",
"\n",
" data_input = data.clone()\n",
" data_input['pos_ref'] = None\n",
" batch = repeat_data(data_input, num_samples).to(DEVICE)\n",
"\n",
" # initial configuration\n",
" pos_init = torch.randn(batch.num_nodes, 3).to(DEVICE)\n",
"\n",
" # for logging animation of denoising\n",
" pos_traj = []\n",
" with torch.no_grad():\n",
"\n",
" # scale initial sample\n",
" pos = pos_init * sigmas[-1]\n",
" for t in scheduler.timesteps:\n",
" batch.pos = pos\n",
"\n",
" # generate geometry with model, then filter it\n",
" epsilon = model.forward(batch, t, sigma=sigmas[t], return_dict=False)[0]\n",
"\n",
" # Update\n",
" reconstructed_pos = scheduler.step(epsilon, t, pos)[\"prev_sample\"].to(DEVICE)\n",
"\n",
" pos = reconstructed_pos\n",
"\n",
" if torch.isnan(pos).any():\n",
" print(\"NaN detected. Please restart.\")\n",
" raise FloatingPointError()\n",
"\n",
" # recenter graph of positions for next iteration\n",
" pos = pos - scatter_mean(pos, batch.batch, dim=0)[batch.batch]\n",
"\n",
" # optional clipping\n",
" if clip_pos is not None:\n",
" pos = torch.clamp(pos, min=-clip_pos, max=clip_pos)\n",
" pos_traj.append(pos.clone().cpu())\n",
"\n",
" pos_gen = pos.cpu()\n",
" if save_traj:\n",
" pos_gen_traj = pos_traj.cpu()\n",
" data.pos_gen = torch.stack(pos_gen_traj)\n",
" else:\n",
" data.pos_gen = pos_gen\n",
" results.append(data)\n",
"\n",
"\n",
"if save_data:\n",
" save_path = os.path.join(output_dir, 'samples_all.pkl')\n",
"\n",
" with open(save_path, 'wb') as f:\n",
" pickle.dump(results, f)"
]
},
{
"cell_type": "markdown",
"source": [
"## Render the results!"
],
"metadata": {
"id": "fSApwSaZNndW"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "d47Zxo2OKdgZ"
},
"source": [
"This function allows us to render 3d in colab."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "e9Cd0kCAv9b8"
},
"outputs": [],
"source": [
"from google.colab import output\n",
"output.enable_custom_widget_manager()"
]
},
{
"cell_type": "markdown",
"source": [
"### Helper functions"
],
"metadata": {
"id": "RjaVuR15NqzF"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "28rBYa9NKhlz"
},
"source": [
"Here is a helper function for copying the generated tensors into a format used by RDKit & NGLViewer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LKdKdwxcyTQ6"
},
"outputs": [],
"source": [
"from copy import deepcopy\n",
"def set_rdmol_positions(rdkit_mol, pos):\n",
" \"\"\"\n",
" Args:\n",
" rdkit_mol: An `rdkit.Chem.rdchem.Mol` object.\n",
" pos: (N_atoms, 3)\n",
" \"\"\"\n",
" mol = deepcopy(rdkit_mol)\n",
" set_rdmol_positions_(mol, pos)\n",
" return mol\n",
"\n",
"def set_rdmol_positions_(mol, pos):\n",
" \"\"\"\n",
" Args:\n",
" rdkit_mol: An `rdkit.Chem.rdchem.Mol` object.\n",
" pos: (N_atoms, 3)\n",
" \"\"\"\n",
" for i in range(pos.shape[0]):\n",
" mol.GetConformer(0).SetAtomPosition(i, pos[i].tolist())\n",
" return mol\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NuE10hcpKmzK"
},
"source": [
"Process the generated data to make it easy to view."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KieVE1vc0_Vs",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6faa185d-b1bc-47e8-be18-30d1e557e7c8"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"collect 5 generated molecules in `mols`\n"
]
}
],
"source": [
"# the model can generate multiple conformations per 2d geometry\n",
"num_gen = results[0]['pos_gen'].shape[0]\n",
"\n",
"# init storage objects\n",
"mols_gen = []\n",
"mols_orig = []\n",
"for to_process in results:\n",
"\n",
" # store the reference 3d position\n",
" to_process['pos_ref'] = to_process['pos_ref'].reshape(-1, to_process['rdmol'].GetNumAtoms(), 3)\n",
"\n",
" # store the generated 3d position\n",
" to_process['pos_gen'] = to_process['pos_gen'].reshape(-1, to_process['rdmol'].GetNumAtoms(), 3)\n",
"\n",
" # copy data to new object\n",
" new_mol = set_rdmol_positions(to_process.rdmol, to_process['pos_gen'][0])\n",
"\n",
" # append results\n",
" mols_gen.append(new_mol)\n",
" mols_orig.append(to_process.rdmol)\n",
"\n",
"print(f\"collect {len(mols_gen)} generated molecules in `mols`\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tin89JwMKp4v"
},
"source": [
"Import tools to visualize the 2d chemical diagram of the molecule."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yqV6gllSZn38"
},
"outputs": [],
"source": [
"from rdkit.Chem import AllChem\n",
"from rdkit import Chem\n",
"from rdkit.Chem.Draw import rdMolDraw2D as MD2\n",
"from IPython.display import SVG, display"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TFNKmGddVoOk"
},
"source": [
"Select molecule to visualize"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KzuwLlrrVaGc"
},
"outputs": [],
"source": [
"idx = 0\n",
"assert idx < len(results), \"selected molecule that was not generated\""
]
},
{
"cell_type": "markdown",
"source": [
"### Viewing"
],
"metadata": {
"id": "hkb8w0_SNtU8"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "I3R4QBQeKttN"
},
"source": [
"This 2D rendering is the equivalent of the **input to the model**!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gkQRWjraaKex",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 321
},
"outputId": "9c3d1a91-a51d-475d-9e34-2be2459abc47"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.SVG object>"
],
"image/svg+xml": "<svg baseProfile=\"full\" height=\"300px\" version=\"1.1\" viewBox=\"0 0 450 300\" width=\"450px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<!-- END OF HEADER -->\n<rect height=\"300.0\" style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"450.0\" x=\"0.0\" y=\"0.0\"> </rect>\n<path class=\"bond-0 atom-0 atom-1\" d=\"M 20.5,147.6 L 57.8,136.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-1 atom-1 atom-2\" d=\"M 57.8,136.7 L 67.1,98.9\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-2 atom-2 atom-3\" d=\"M 67.1,98.9 L 104.4,88.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-3 atom-3 atom-4\" d=\"M 104.4,88.1 L 132.5,115.0\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-4 atom-4 atom-5\" d=\"M 132.5,115.0 L 128.7,130.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-4 atom-4 atom-5\" d=\"M 128.7,130.5 L 124.9,146.0\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-5 atom-5 atom-6\" d=\"M 128.7,158.0 L 140.0,168.8\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-5 atom-5 atom-6\" d=\"M 140.0,168.8 L 151.3,179.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-6 atom-6 atom-7\" d=\"M 155.1,180.6 L 151.3,196.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-6 atom-6 atom-7\" d=\"M 151.3,196.1 L 147.5,211.5\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-6 atom-6 atom-7\" d=\"M 147.5,178.8 L 143.7,194.2\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-6 atom-6 atom-7\" d=\"M 143.7,194.2 L 139.9,209.7\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-7 atom-6 atom-8\" d=\"M 151.3,179.7 L 188.7,168.8\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-8 atom-8 atom-9\" d=\"M 188.7,168.8 L 216.7,195.8\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-9 atom-9 atom-10\" d=\"M 216.7,195.8 L 254.1,184.9\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-10 atom-10 atom-11\" d=\"M 254.1,184.9 L 257.9,169.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-10 atom-10 atom-11\" d=\"M 257.9,169.4 L 261.7,153.9\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-11 atom-11 atom-12\" d=\"M 268.8,145.5 L 282.4,141.6\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-11 atom-11 atom-12\" d=\"M 282.4,141.6 L 295.9,137.7\" style=\"fill:none;fill-rule:evenodd;stroke:#CCCC00;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-12 atom-12 atom-13\" d=\"M 295.0,130.6 L 291.6,118.8\" style=\"fill:none;fill-rule:evenodd;stroke:#CCCC00;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-12 atom-12 atom-13\" d=\"M 291.6,118.8 L 288.2,107.0\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-12 atom-12 atom-13\" d=\"M 302.5,128.4 L 299.1,116.6\" style=\"fill:none;fill-rule:evenodd;stroke:#CCCC00;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-12 atom-12 atom-13\" d=\"M 299.1,116.6 L 295.6,104.9\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-13 atom-12 atom-14\" d=\"M 306.5,142.3 L 309.9,154.0\" style=\"fill:none;fill-rule:evenodd;stroke:#CCCC00;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-13 atom-12 atom-14\" d=\"M 309.9,154.0 L 313.3,165.7\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-13 atom-12 atom-14\" d=\"M 299.0,144.4 L 302.4,156.1\" style=\"fill:none;fill-rule:evenodd;stroke:#CCCC00;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-13 atom-12 atom-14\" d=\"M 302.4,156.1 L 305.8,167.9\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-14 atom-12 atom-15\" d=\"M 305.5,134.9 L 321.8,130.1\" style=\"fill:none;fill-rule:evenodd;stroke:#CCCC00;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-14 atom-12 atom-15\" d=\"M 321.8,130.1 L 338.1,125.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-15 atom-15 atom-16\" d=\"M 338.1,125.4 L 347.4,87.6\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-15 atom-15 atom-16\" d=\"M 347.0,121.6 L 353.5,95.2\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-16 atom-16 atom-17\" d=\"M 347.4,87.6 L 384.7,76.8\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-17 atom-17 atom-18\" d=\"M 384.7,76.8 L 412.8,103.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-17 atom-17 atom-18\" d=\"M 383.5,86.4 L 403.2,105.3\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-18 atom-18 atom-19\" d=\"M 412.8,103.7 L 403.5,141.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-19 atom-19 atom-20\" d=\"M 403.5,141.5 L 412.1,154.2\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-19 atom-19 atom-20\" d=\"M 412.1,154.2 L 420.8,166.9\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-19 atom-19 atom-20\" d=\"M 399.7,149.7 L 405.7,158.6\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-19 atom-19 atom-20\" d=\"M 405.7,158.6 L 411.7,167.4\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-20 atom-20 atom-21\" d=\"M 420.1,180.5 L 413.5,189.0\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-20 atom-20 atom-21\" d=\"M 413.5,189.0 L 406.8,197.5\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-21 atom-21 atom-22\" d=\"M 395.2,202.1 L 382.8,197.7\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-21 atom-21 atom-22\" d=\"M 382.8,197.7 L 370.4,193.2\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-22 atom-22 atom-23\" d=\"M 365.1,184.4 L 365.6,168.4\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-22 atom-22 atom-23\" d=\"M 365.6,168.4 L 366.2,152.3\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-22 atom-22 atom-23\" d=\"M 373.1,179.9 L 373.4,168.6\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-22 atom-22 atom-23\" d=\"M 373.4,168.6 L 373.8,157.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-23 atom-11 atom-24\" d=\"M 257.9,141.9 L 246.6,131.1\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-23 atom-11 atom-24\" d=\"M 246.6,131.1 L 235.3,120.2\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-24 atom-24 atom-25\" d=\"M 235.3,120.2 L 197.9,131.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-25 atom-5 atom-26\" d=\"M 117.8,154.4 L 101.8,159.0\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-25 atom-5 atom-26\" d=\"M 101.8,159.0 L 85.9,163.6\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-26 atom-26 atom-1\" d=\"M 85.9,163.6 L 57.8,136.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-27 atom-25 atom-8\" d=\"M 197.9,131.1 L 188.7,168.8\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-28 atom-23 atom-15\" d=\"M 366.2,152.3 L 338.1,125.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"bond-29 atom-23 atom-19\" d=\"M 366.2,152.3 L 403.5,141.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n<path class=\"atom-5\" d=\"M 120.8 147.3 L 124.4 153.1 Q 124.8 153.7, 125.3 154.7 Q 125.9 155.8, 126.0 155.8 L 126.0 147.3 L 127.4 147.3 L 127.4 158.3 L 125.9 158.3 L 122.0 151.9 Q 121.6 151.2, 121.1 150.3 Q 120.6 149.4, 120.5 149.2 L 120.5 158.3 L 119.1 158.3 L 119.1 147.3 L 120.8 147.3 \" fill=\"#0000FF\"/>\n<path class=\"atom-7\" d=\"M 137.0 217.5 Q 137.0 214.9, 138.3 213.4 Q 139.6 211.9, 142.0 211.9 Q 144.5 211.9, 145.8 213.4 Q 147.1 214.9, 147.1 217.5 Q 147.1 220.2, 145.8 221.7 Q 144.4 223.2, 142.0 223.2 Q 139.6 223.2, 138.3 221.7 Q 137.0 220.2, 137.0 217.5 M 142.0 222.0 Q 143.7 222.0, 144.6 220.8 Q 145.5 219.7, 145.5 217.5 Q 145.5 215.3, 144.6 214.2 Q 143.7 213.1, 142.0 213.1 Q 140.4 213.1, 139.4 214.2 Q 138.5 215.3, 138.5 217.5 Q 138.5 219.7, 139.4 220.8 Q 140.4 222.0, 142.0 222.0 \" fill=\"#FF0000\"/>\n<path class=\"atom-11\" d=\"M 260.9 141.6 L 264.5 147.5 Q 264.9 148.0, 265.5 149.1 Q 266.1 150.1, 266.1 150.2 L 266.1 141.6 L 267.5 141.6 L 267.5 152.6 L 266.0 152.6 L 262.2 146.3 Q 261.7 145.5, 261.2 144.7 Q 260.8 143.8, 260.6 143.5 L 260.6 152.6 L 259.2 152.6 L 259.2 141.6 L 260.9 141.6 \" fill=\"#0000FF\"/>\n<path class=\"atom-12\" d=\"M 297.6 140.1 Q 297.7 140.1, 298.2 140.3 Q 298.8 140.5, 299.3 140.7 Q 299.9 140.8, 300.5 140.8 Q 301.5 140.8, 302.1 140.3 Q 302.7 139.8, 302.7 138.9 Q 302.7 138.3, 302.4 137.9 Q 302.1 137.6, 301.6 137.3 Q 301.2 137.1, 300.4 136.9 Q 299.4 136.6, 298.8 136.3 Q 298.2 136.1, 297.8 135.5 Q 297.4 134.9, 297.4 133.9 Q 297.4 132.5, 298.4 131.6 Q 299.3 130.8, 301.2 130.8 Q 302.4 130.8, 303.9 131.4 L 303.5 132.6 Q 302.2 132.0, 301.2 132.0 Q 300.1 132.0, 299.6 132.5 Q 299.0 132.9, 299.0 133.7 Q 299.0 134.3, 299.3 134.6 Q 299.6 135.0, 300.0 135.2 Q 300.5 135.4, 301.2 135.6 Q 302.2 135.9, 302.8 136.3 Q 303.4 136.6, 303.8 137.2 Q 304.3 137.8, 304.3 138.9 Q 304.3 140.4, 303.2 141.3 Q 302.2 142.1, 300.5 142.1 Q 299.5 142.1, 298.8 141.8 Q 298.1 141.6, 297.2 141.3 L 297.6 140.1 \" fill=\"#CCCC00\"/>\n<path class=\"atom-13\" d=\"M 284.8 99.0 Q 284.8 96.3, 286.1 94.8 Q 287.4 93.4, 289.9 93.4 Q 292.3 93.4, 293.6 94.8 Q 294.9 96.3, 294.9 99.0 Q 294.9 101.6, 293.6 103.2 Q 292.3 104.7, 289.9 104.7 Q 287.4 104.7, 286.1 103.2 Q 284.8 101.6, 284.8 99.0 M 289.9 103.4 Q 291.5 103.4, 292.5 102.3 Q 293.4 101.2, 293.4 99.0 Q 293.4 96.8, 292.5 95.7 Q 291.5 94.6, 289.9 94.6 Q 288.2 94.6, 287.3 95.7 Q 286.4 96.8, 286.4 99.0 Q 286.4 101.2, 287.3 102.3 Q 288.2 103.4, 289.9 103.4 \" fill=\"#FF0000\"/>\n<path class=\"atom-14\" d=\"M 306.5 173.7 Q 306.5 171.0, 307.8 169.5 Q 309.1 168.1, 311.6 168.1 Q 314.0 168.1, 315.3 169.5 Q 316.6 171.0, 316.6 173.7 Q 316.6 176.3, 315.3 177.9 Q 314.0 179.4, 311.6 179.4 Q 309.1 179.4, 307.8 177.9 Q 306.5 176.4, 306.5 173.7 M 311.6 178.1 Q 313.3 178.1, 314.2 177.0 Q 315.1 175.9, 315.1 173.7 Q 315.1 171.5, 314.2 170.4 Q 313.3 169.3, 311.6 169.3 Q 309.9 169.3, 309.0 170.4 Q 308.1 171.5, 308.1 173.7 Q 308.1 175.9, 309.0 177.0 Q 309.9 178.1, 311.6 178.1 \" fill=\"#FF0000\"/>\n<path class=\"atom-20\" d=\"M 422.9 168.2 L 426.5 174.0 Q 426.9 174.6, 427.5 175.6 Q 428.1 176.6, 428.1 176.7 L 428.1 168.2 L 429.5 168.2 L 429.5 179.2 L 428.0 179.2 L 424.2 172.8 Q 423.7 172.0, 423.2 171.2 Q 422.8 170.3, 422.6 170.1 L 422.6 179.2 L 421.2 179.2 L 421.2 168.2 L 422.9 168.2 \" fill=\"#0000FF\"/>\n<path class=\"atom-21\" d=\"M 396.5 204.4 Q 396.5 201.8, 397.8 200.3 Q 399.1 198.8, 401.5 198.8 Q 404.0 198.8, 405.3 200.3 Q 406.6 201.8, 406.6 204.4 Q 406.6 207.1, 405.3 208.6 Q 403.9 210.1, 401.5 210.1 Q 399.1 210.1, 397.8 208.6 Q 396.5 207.1, 396.5 204.4 M 401.5 208.9 Q 403.2 208.9, 404.1 207.8 Q 405.0 206.6, 405.0 204.4 Q 405.0 202.3, 404.1 201.2 Q 403.2 200.1, 401.5 200.1 Q 399.8 200.1, 398.9 201.2 Q 398.0 202.2, 398.0 204.4 Q 398.0 206.7, 398.9 207.8 Q 399.8 208.9, 401.5 208.9 \" fill=\"#FF0000\"/>\n<path class=\"atom-22\" d=\"M 362.5 185.7 L 366.1 191.5 Q 366.5 192.1, 367.0 193.2 Q 367.6 194.2, 367.6 194.3 L 367.6 185.7 L 369.1 185.7 L 369.1 196.7 L 367.6 196.7 L 363.7 190.4 Q 363.3 189.6, 362.8 188.7 Q 362.3 187.9, 362.2 187.6 L 362.2 196.7 L 360.8 196.7 L 360.8 185.7 L 362.5 185.7 \" fill=\"#0000FF\"/>\n</svg>"
},
"metadata": {}
}
],
"source": [
"mc = Chem.MolFromSmiles(dataset[0]['smiles'])\n",
"molSize=(450,300)\n",
"drawer = MD2.MolDraw2DSVG(molSize[0],molSize[1])\n",
"drawer.DrawMolecule(mc)\n",
"drawer.FinishDrawing()\n",
"svg = drawer.GetDrawingText()\n",
"display(SVG(svg.replace('svg:','')))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "z4FDMYMxKw2I"
},
"source": [
"Generate the 3d molecule!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aT1Bkb8YxJfV",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17,
"referenced_widgets": [
"695ab5bbf30a4ab19df1f9f33469f314",
"eac6a8dcdc9d4335a2e51031793ead29"
]
},
"outputId": "b98870ae-049d-4386-b676-166e9526bda2"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "695ab5bbf30a4ab19df1f9f33469f314"
}
},
"metadata": {
"application/vnd.jupyter.widget-view+json": {
"colab": {
"custom_widget_manager": {
"url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/d2e234f7cc04bf79/manager.min.js"
}
}
}
}
}
],
"source": [
"from nglview import show_rdkit as show"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pxtq8I-I18C-",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 337,
"referenced_widgets": [
"be446195da2b4ff2aec21ec5ff963a54",
"c6596896148b4a8a9c57963b67c7782f",
"2489b5e5648541fbbdceadb05632a050",
"01e0ba4e5da04914b4652b8d58565d7b",
"c30e6c2f3e2a44dbbb3d63bd519acaa4",
"f31c6e40e9b2466a9064a2669933ecd5",
"19308ccac642498ab8b58462e3f1b0bb",
"4a081cdc2ec3421ca79dd933b7e2b0c4",
"e5c0d75eb5e1447abd560c8f2c6017e1",
"5146907ef6764654ad7d598baebc8b58",
"144ec959b7604a2cabb5ca46ae5e5379",
"abce2a80e6304df3899109c6d6cac199",
"65195cb7a4134f4887e9dd19f3676462"
]
},
"outputId": "72ed63ac-d2ec-4f5c-a0b1-4e7c1840a4e7"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"NGLWidget()"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "be446195da2b4ff2aec21ec5ff963a54"
}
},
"metadata": {
"application/vnd.jupyter.widget-view+json": {
"colab": {
"custom_widget_manager": {
"url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/d2e234f7cc04bf79/manager.min.js"
}
}
}
}
}
],
"source": [
"# new molecule\n",
"show(mols_gen[idx])"
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "KJr4h2mwXeTo"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"d90f304e9560472eacfbdd11e46765eb": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_1c6246f15b654f4daa11c9bcf997b78c",
"IPY_MODEL_c2321b3bff6f490ca12040a20308f555",
"IPY_MODEL_b7feb522161f4cf4b7cc7c1a078ff12d"
],
"layout": "IPY_MODEL_e2d368556e494ae7ae4e2e992af2cd4f"
}
},
"1c6246f15b654f4daa11c9bcf997b78c": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_bbef741e76ec41b7ab7187b487a383df",
"placeholder": "​",
"style": "IPY_MODEL_561f742d418d4721b0670cc8dd62e22c",
"value": "Downloading: 100%"
}
},
"c2321b3bff6f490ca12040a20308f555": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_872915dd1bb84f538c44e26badabafdd",
"max": 3271865,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_d022575f1fa2446d891650897f187b4d",
"value": 3271865
}
},
"b7feb522161f4cf4b7cc7c1a078ff12d": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_fdc393f3468c432aa0ada05e238a5436",
"placeholder": "​",
"style": "IPY_MODEL_2c9362906e4b40189f16d14aa9a348da",
"value": " 3.27M/3.27M [00:01&lt;00:00, 3.25MB/s]"
}
},
"e2d368556e494ae7ae4e2e992af2cd4f": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"bbef741e76ec41b7ab7187b487a383df": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"561f742d418d4721b0670cc8dd62e22c": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"872915dd1bb84f538c44e26badabafdd": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"d022575f1fa2446d891650897f187b4d": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"fdc393f3468c432aa0ada05e238a5436": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"2c9362906e4b40189f16d14aa9a348da": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"6010fc8daa7a44d5aec4b830ec2ebaa1": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_7e0bb1b8d65249d3974200686b193be2",
"IPY_MODEL_ba98aa6d6a884e4ab8bbb5dfb5e4cf7a",
"IPY_MODEL_6526646be5ed415c84d1245b040e629b"
],
"layout": "IPY_MODEL_24d31fc3576e43dd9f8301d2ef3a37ab"
}
},
"7e0bb1b8d65249d3974200686b193be2": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_2918bfaadc8d4b1a9832522c40dfefb8",
"placeholder": "​",
"style": "IPY_MODEL_a4bfdca35cc54dae8812720f1b276a08",
"value": "Downloading: 100%"
}
},
"ba98aa6d6a884e4ab8bbb5dfb5e4cf7a": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_e4901541199b45c6a18824627692fc39",
"max": 401,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_f915cf874246446595206221e900b2fe",
"value": 401
}
},
"6526646be5ed415c84d1245b040e629b": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_a9e388f22a9742aaaf538e22575c9433",
"placeholder": "​",
"style": "IPY_MODEL_42f6c3db29d7484ba6b4f73590abd2f4",
"value": " 401/401 [00:00&lt;00:00, 13.5kB/s]"
}
},
"24d31fc3576e43dd9f8301d2ef3a37ab": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"2918bfaadc8d4b1a9832522c40dfefb8": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"a4bfdca35cc54dae8812720f1b276a08": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"e4901541199b45c6a18824627692fc39": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"f915cf874246446595206221e900b2fe": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"a9e388f22a9742aaaf538e22575c9433": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"42f6c3db29d7484ba6b4f73590abd2f4": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"695ab5bbf30a4ab19df1f9f33469f314": {
"model_module": "nglview-js-widgets",
"model_name": "ColormakerRegistryModel",
"model_module_version": "3.0.1",
"state": {
"_dom_classes": [],
"_model_module": "nglview-js-widgets",
"_model_module_version": "3.0.1",
"_model_name": "ColormakerRegistryModel",
"_msg_ar": [],
"_msg_q": [],
"_ready": false,
"_view_count": null,
"_view_module": "nglview-js-widgets",
"_view_module_version": "3.0.1",
"_view_name": "ColormakerRegistryView",
"layout": "IPY_MODEL_eac6a8dcdc9d4335a2e51031793ead29"
}
},
"eac6a8dcdc9d4335a2e51031793ead29": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"be446195da2b4ff2aec21ec5ff963a54": {
"model_module": "nglview-js-widgets",
"model_name": "NGLModel",
"model_module_version": "3.0.1",
"state": {
"_camera_orientation": [
-15.519693580202304,
-14.065056548036177,
-23.53197484807691,
0,
-23.357853515109753,
20.94055073042662,
2.888695042134944,
0,
14.352363398292777,
18.870825741878015,
-20.744689572909344,
0,
0.2724999189376831,
0.6940000057220459,
-0.3734999895095825,
1
],
"_camera_str": "orthographic",
"_dom_classes": [],
"_gui_theme": null,
"_ibtn_fullscreen": "IPY_MODEL_2489b5e5648541fbbdceadb05632a050",
"_igui": null,
"_iplayer": "IPY_MODEL_01e0ba4e5da04914b4652b8d58565d7b",
"_model_module": "nglview-js-widgets",
"_model_module_version": "3.0.1",
"_model_name": "NGLModel",
"_ngl_color_dict": {},
"_ngl_coordinate_resource": {},
"_ngl_full_stage_parameters": {
"impostor": true,
"quality": "medium",
"workerDefault": true,
"sampleLevel": 0,
"backgroundColor": "white",
"rotateSpeed": 2,
"zoomSpeed": 1.2,
"panSpeed": 1,
"clipNear": 0,
"clipFar": 100,
"clipDist": 10,
"fogNear": 50,
"fogFar": 100,
"cameraFov": 40,
"cameraEyeSep": 0.3,
"cameraType": "perspective",
"lightColor": 14540253,
"lightIntensity": 1,
"ambientColor": 14540253,
"ambientIntensity": 0.2,
"hoverTimeout": 0,
"tooltip": true,
"mousePreset": "default"
},
"_ngl_msg_archive": [
{
"target": "Stage",
"type": "call_method",
"methodName": "loadFile",
"reconstruc_color_scheme": false,
"args": [
{
"type": "blob",
"data": "HETATM 1 C1 UNL 1 -0.025 3.128 2.316 1.00 0.00 C \nHETATM 2 H1 UNL 1 0.183 3.657 2.823 1.00 0.00 H \nHETATM 3 C2 UNL 1 0.590 3.559 0.963 1.00 0.00 C \nHETATM 4 C3 UNL 1 0.056 4.479 0.406 1.00 0.00 C \nHETATM 5 C4 UNL 1 -0.219 4.802 -1.065 1.00 0.00 C \nHETATM 6 H2 UNL 1 0.686 4.431 -1.575 1.00 0.00 H \nHETATM 7 H3 UNL 1 -0.524 5.217 -1.274 1.00 0.00 H \nHETATM 8 C5 UNL 1 -1.284 3.766 -1.342 1.00 0.00 C \nHETATM 9 N1 UNL 1 -1.073 2.494 -0.580 1.00 0.00 N \nHETATM 10 C6 UNL 1 -1.909 1.494 -0.964 1.00 0.00 C \nHETATM 11 O1 UNL 1 -2.487 1.531 -2.092 1.00 0.00 O \nHETATM 12 C7 UNL 1 -2.232 0.242 -0.130 1.00 0.00 C \nHETATM 13 C8 UNL 1 -2.161 -1.057 -1.037 1.00 0.00 C \nHETATM 14 C9 UNL 1 -0.744 -1.111 -1.610 1.00 0.00 C \nHETATM 15 N2 UNL 1 0.290 -0.917 -0.628 1.00 0.00 N \nHETATM 16 S1 UNL 1 1.717 -1.597 -0.914 1.00 0.00 S \nHETATM 17 O2 UNL 1 1.960 -1.671 -2.338 1.00 0.00 O \nHETATM 18 O3 UNL 1 2.713 -0.968 -0.082 1.00 0.00 O \nHETATM 19 C10 UNL 1 1.425 -3.170 -0.345 1.00 0.00 C \nHETATM 20 C11 UNL 1 1.225 -4.400 -1.271 1.00 0.00 C \nHETATM 21 C12 UNL 1 1.314 -5.913 -0.895 1.00 0.00 C \nHETATM 22 C13 UNL 1 1.823 -6.229 0.386 1.00 0.00 C \nHETATM 23 C14 UNL 1 2.031 -5.110 1.365 1.00 0.00 C \nHETATM 24 N3 UNL 1 1.850 -5.267 2.712 1.00 0.00 N \nHETATM 25 O4 UNL 1 1.382 -4.029 3.126 1.00 0.00 O \nHETATM 26 N4 UNL 1 1.300 -3.023 2.154 1.00 0.00 N \nHETATM 27 C15 UNL 1 1.731 -3.672 1.032 1.00 0.00 C \nHETATM 28 H4 UNL 1 2.380 -6.874 0.436 1.00 0.00 H \nHETATM 29 H5 UNL 1 0.704 -6.526 -1.420 1.00 0.00 H \nHETATM 30 H6 UNL 1 1.144 -4.035 -2.291 1.00 0.00 H \nHETATM 31 C16 UNL 1 0.044 -0.371 0.685 1.00 0.00 C \nHETATM 32 C17 UNL 1 -1.352 -0.045 1.077 1.00 0.00 C \nHETATM 33 H7 UNL 1 -1.395 0.770 1.768 1.00 0.00 H \nHETATM 34 H8 UNL 1 -1.792 -0.941 1.582 1.00 0.00 H \nHETATM 35 H9 UNL 1 0.583 -1.035 1.393 1.00 0.00 H \nHETATM 36 H10 UNL 1 0.664 0.613 0.663 1.00 0.00 H \nHETATM 37 H11 UNL 1 -0.631 -0.267 -2.335 1.00 0.00 H \nHETATM 38 H12 UNL 1 -0.571 -2.046 -2.098 1.00 0.00 H \nHETATM 39 H13 UNL 1 -2.872 -0.992 -1.826 1.00 0.00 H \nHETATM 40 H14 UNL 1 -2.370 -1.924 -0.444 1.00 0.00 H \nHETATM 41 H15 UNL 1 -3.258 0.364 0.197 1.00 0.00 H \nHETATM 42 C18 UNL 1 0.276 2.337 -0.078 1.00 0.00 C \nHETATM 43 H16 UNL 1 0.514 1.371 0.252 1.00 0.00 H \nHETATM 44 H17 UNL 1 0.988 2.413 -0.949 1.00 0.00 H \nHETATM 45 H18 UNL 1 -1.349 3.451 -2.379 1.00 0.00 H \nHETATM 46 H19 UNL 1 -2.224 4.055 -0.958 1.00 0.00 H \nHETATM 47 H20 UNL 1 0.793 5.486 0.669 1.00 0.00 H \nHETATM 48 H21 UNL 1 -0.849 4.974 0.937 1.00 0.00 H \nHETATM 49 H22 UNL 1 1.667 3.431 1.070 1.00 0.00 H \nHETATM 50 H23 UNL 1 0.379 2.143 2.689 1.00 0.00 H \nHETATM 51 H24 UNL 1 -1.094 2.983 2.223 1.00 0.00 H \nCONECT 1 2 3 50 51\nCONECT 3 4 42 49\nCONECT 4 5 47 48\nCONECT 5 6 7 8\nCONECT 8 9 45 46\nCONECT 9 10 42\nCONECT 10 11 11 12\nCONECT 12 13 32 41\nCONECT 13 14 39 40\nCONECT 14 15 37 38\nCONECT 15 16 31\nCONECT 16 17 17 18 18\nCONECT 16 19\nCONECT 19 20 20 27\nCONECT 20 21 30\nCONECT 21 22 22 29\nCONECT 22 23 28\nCONECT 23 24 24 27\nCONECT 24 25\nCONECT 25 26\nCONECT 26 27 27\nCONECT 31 32 35 36\nCONECT 32 33 34\nCONECT 42 43 44\nEND\n",
"binary": false
}
],
"kwargs": {
"defaultRepresentation": true,
"ext": "pdb"
}
}
],
"_ngl_original_stage_parameters": {
"impostor": true,
"quality": "medium",
"workerDefault": true,
"sampleLevel": 0,
"backgroundColor": "white",
"rotateSpeed": 2,
"zoomSpeed": 1.2,
"panSpeed": 1,
"clipNear": 0,
"clipFar": 100,
"clipDist": 10,
"fogNear": 50,
"fogFar": 100,
"cameraFov": 40,
"cameraEyeSep": 0.3,
"cameraType": "perspective",
"lightColor": 14540253,
"lightIntensity": 1,
"ambientColor": 14540253,
"ambientIntensity": 0.2,
"hoverTimeout": 0,
"tooltip": true,
"mousePreset": "default"
},
"_ngl_repr_dict": {
"0": {
"0": {
"type": "ball+stick",
"params": {
"lazy": false,
"visible": true,
"quality": "high",
"sphereDetail": 2,
"radialSegments": 20,
"openEnded": true,
"disableImpostor": false,
"aspectRatio": 1.5,
"lineOnly": false,
"cylinderOnly": false,
"multipleBond": "off",
"bondScale": 0.3,
"bondSpacing": 0.75,
"linewidth": 2,
"radiusType": "size",
"radiusData": {},
"radiusSize": 0.15,
"radiusScale": 2,
"assembly": "default",
"defaultAssembly": "",
"clipNear": 0,
"clipRadius": 0,
"clipCenter": {
"x": 0,
"y": 0,
"z": 0
},
"flatShaded": false,
"opacity": 1,
"depthWrite": true,
"side": "double",
"wireframe": false,
"colorScheme": "element",
"colorScale": "",
"colorReverse": false,
"colorValue": 9474192,
"colorMode": "hcl",
"roughness": 0.4,
"metalness": 0,
"diffuse": 16777215,
"diffuseInterior": false,
"useInteriorColor": true,
"interiorColor": 2236962,
"interiorDarkening": 0,
"matrix": {
"elements": [
1,
0,
0,
0,
0,
1,
0,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
},
"disablePicking": false,
"sele": ""
}
}
},
"1": {
"0": {
"type": "ball+stick",
"params": {
"lazy": false,
"visible": true,
"quality": "high",
"sphereDetail": 2,
"radialSegments": 20,
"openEnded": true,
"disableImpostor": false,
"aspectRatio": 1.5,
"lineOnly": false,
"cylinderOnly": false,
"multipleBond": "off",
"bondScale": 0.3,
"bondSpacing": 0.75,
"linewidth": 2,
"radiusType": "size",
"radiusData": {},
"radiusSize": 0.15,
"radiusScale": 2,
"assembly": "default",
"defaultAssembly": "",
"clipNear": 0,
"clipRadius": 0,
"clipCenter": {
"x": 0,
"y": 0,
"z": 0
},
"flatShaded": false,
"opacity": 1,
"depthWrite": true,
"side": "double",
"wireframe": false,
"colorScheme": "element",
"colorScale": "",
"colorReverse": false,
"colorValue": 9474192,
"colorMode": "hcl",
"roughness": 0.4,
"metalness": 0,
"diffuse": 16777215,
"diffuseInterior": false,
"useInteriorColor": true,
"interiorColor": 2236962,
"interiorDarkening": 0,
"matrix": {
"elements": [
1,
0,
0,
0,
0,
1,
0,
0,
0,
0,
1,
0,
0,
0,
0,
1
]
},
"disablePicking": false,
"sele": ""
}
}
}
},
"_ngl_serialize": false,
"_ngl_version": "",
"_ngl_view_id": [
"FB989FD1-5B9C-446B-8914-6B58AF85446D"
],
"_player_dict": {},
"_scene_position": {},
"_scene_rotation": {},
"_synced_model_ids": [],
"_synced_repr_model_ids": [],
"_view_count": null,
"_view_height": "",
"_view_module": "nglview-js-widgets",
"_view_module_version": "3.0.1",
"_view_name": "NGLView",
"_view_width": "",
"background": "white",
"frame": 0,
"gui_style": null,
"layout": "IPY_MODEL_c6596896148b4a8a9c57963b67c7782f",
"max_frame": 0,
"n_components": 2,
"picked": {}
}
},
"c6596896148b4a8a9c57963b67c7782f": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"2489b5e5648541fbbdceadb05632a050": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ButtonModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ButtonModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ButtonView",
"button_style": "",
"description": "",
"disabled": false,
"icon": "compress",
"layout": "IPY_MODEL_abce2a80e6304df3899109c6d6cac199",
"style": "IPY_MODEL_65195cb7a4134f4887e9dd19f3676462",
"tooltip": ""
}
},
"01e0ba4e5da04914b4652b8d58565d7b": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_e5c0d75eb5e1447abd560c8f2c6017e1",
"IPY_MODEL_5146907ef6764654ad7d598baebc8b58"
],
"layout": "IPY_MODEL_144ec959b7604a2cabb5ca46ae5e5379"
}
},
"c30e6c2f3e2a44dbbb3d63bd519acaa4": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"f31c6e40e9b2466a9064a2669933ecd5": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"19308ccac642498ab8b58462e3f1b0bb": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"4a081cdc2ec3421ca79dd933b7e2b0c4": {
"model_module": "@jupyter-widgets/controls",
"model_name": "SliderStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "SliderStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": "",
"handle_color": null
}
},
"e5c0d75eb5e1447abd560c8f2c6017e1": {
"model_module": "@jupyter-widgets/controls",
"model_name": "PlayModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "PlayModel",
"_playing": false,
"_repeat": false,
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "PlayView",
"description": "",
"description_tooltip": null,
"disabled": false,
"interval": 100,
"layout": "IPY_MODEL_c30e6c2f3e2a44dbbb3d63bd519acaa4",
"max": 0,
"min": 0,
"show_repeat": true,
"step": 1,
"style": "IPY_MODEL_f31c6e40e9b2466a9064a2669933ecd5",
"value": 0
}
},
"5146907ef6764654ad7d598baebc8b58": {
"model_module": "@jupyter-widgets/controls",
"model_name": "IntSliderModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "IntSliderModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "IntSliderView",
"continuous_update": true,
"description": "",
"description_tooltip": null,
"disabled": false,
"layout": "IPY_MODEL_19308ccac642498ab8b58462e3f1b0bb",
"max": 0,
"min": 0,
"orientation": "horizontal",
"readout": true,
"readout_format": "d",
"step": 1,
"style": "IPY_MODEL_4a081cdc2ec3421ca79dd933b7e2b0c4",
"value": 0
}
},
"144ec959b7604a2cabb5ca46ae5e5379": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"abce2a80e6304df3899109c6d6cac199": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": "34px"
}
},
"65195cb7a4134f4887e9dd19f3676462": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ButtonStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ButtonStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"button_color": null,
"font_weight": ""
}
}
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}
\ No newline at end of file
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment