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Duplicate from jannisborn/gt4sd-diffusers
Browse files- .gitattributes +34 -0
- .gitignore +1 -0
- LICENSE +21 -0
- README.md +15 -0
- app.py +72 -0
- model_cards/article.md +67 -0
- model_cards/description.md +6 -0
- model_cards/examples.csv +2 -0
- requirements.txt +29 -0
- utils.py +48 -0
.gitattributes
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.gitignore
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__pycache__/
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LICENSE
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MIT License
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Copyright (c) 2022 Generative Toolkit 4 Scientific Discovery
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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---
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title: GT4SD - Diffusers (image)
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emoji: 💡
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colorFrom: green
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colorTo: blue
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sdk: gradio
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sdk_version: 3.9.1
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app_file: app.py
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pinned: false
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python_version: 3.8.13
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pypi_version: 20.2.4
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duplicated_from: jannisborn/gt4sd-diffusers
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import logging
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import pathlib
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import gradio as gr
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import pandas as pd
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from gt4sd.algorithms.generation.diffusion import (
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DiffusersGenerationAlgorithm,
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DDPMGenerator,
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DDIMGenerator,
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ScoreSdeGenerator,
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LDMTextToImageGenerator,
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LDMGenerator,
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StableDiffusionGenerator,
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)
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from gt4sd.algorithms.registry import ApplicationsRegistry
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logger = logging.getLogger(__name__)
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logger.addHandler(logging.NullHandler())
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def run_inference(model_type: str, prompt: str):
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if prompt == "":
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config = eval(f"{model_type}()")
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else:
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config = eval(f"{model_type}(prompt={prompt})")
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if config.modality != "token2image" and prompt != "":
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raise ValueError(
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f"{model_type} is an unconditional generative model, please remove prompt (not={prompt})"
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)
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model = DiffusersGenerationAlgorithm(config)
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image = list(model.sample(1))[0]
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return image
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if __name__ == "__main__":
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# Preparation (retrieve all available algorithms)
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all_algos = ApplicationsRegistry.list_available()
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algos = [
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x["algorithm_application"]
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for x in list(filter(lambda x: "Diff" in x["algorithm_name"], all_algos))
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]
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algos = [a for a in algos if not "GeoDiff" in a]
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# Load metadata
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metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards")
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examples = pd.read_csv(metadata_root.joinpath("examples.csv"), header=None).fillna(
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""
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)
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with open(metadata_root.joinpath("article.md"), "r") as f:
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article = f.read()
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with open(metadata_root.joinpath("description.md"), "r") as f:
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description = f.read()
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demo = gr.Interface(
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fn=run_inference,
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title="Diffusion-based image generators",
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inputs=[
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gr.Dropdown(
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algos, label="Diffusion model", value="StableDiffusionGenerator"
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),
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gr.Textbox(label="Text prompt", placeholder="A blue tree", lines=1),
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],
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outputs=gr.outputs.Image(type="pil"),
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article=article,
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description=description,
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examples=examples.values.tolist(),
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)
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demo.launch(debug=True, show_error=True)
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model_cards/article.md
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# Model documentation & parameters
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**Diffusion model**: Which model version to use.
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**Prompt**: The text prompt used, only applies to *conditional* diffusion image generators. These are `LDMTextToImageGenerator` and `StableDiffusionGenerator`. The other four models (`DDPMGenerator`, `DDPMGenerator`, `LDMGenerator` and `ScoreSdeGenerator`) are *unconditional*.
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# Model card -- Image diffusion models
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**Model Details**: Six diffusion models for image generation:
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- `LDMTextToImageGenerator`
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- `StableDiffusionGenerator`
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- `DDPMGenerator`
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- `DDPMGenerator`
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- `LDMGenerator`
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- `ScoreSdeGenerator`
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For details, see the [Diffusers docs](https://huggingface.co/docs/diffusers/index)
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**Developers**: Various developers of above models, wrapped by Diffusers developers into [`diffusers`](https://github.com/huggingface/diffusers)
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**Distributors**: Diffusers code integrated into GT4SD.
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**Model date**: 2022.
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**Model version**: Diffusion models, checkpoints provided and distributed by [`diffusers`](https://github.com/huggingface/diffusers).
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**Model type**: Various, see [`diffusers`](https://github.com/huggingface/diffusers) docs.
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**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**:
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N.A.
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**Paper or other resource for more information**:
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N.A.
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**License**: MIT
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**Where to send questions or comments about the model**: Open an issue on [`diffusers`](https://github.com/huggingface/diffusers) repo.
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**Intended Use. Use cases that were envisioned during development**: Computer vision researchers experimenting with image generative models.
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**Primary intended uses/users**: Computer vision researchers
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**Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties.
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**Metrics**: N.A.
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**Datasets**: N.A.
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**Ethical Considerations**: Unclear, please consult with original authors in case of questions.
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**Caveats and Recommendations**: Unclear, please consult with original authors in case of questions.
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Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)
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## Citation
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```bib
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@misc{von-platen-etal-2022-diffusers,
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author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
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title = {Diffusers: State-of-the-art diffusion models},
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year = {2022},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/huggingface/diffusers}}
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}
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```
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model_cards/description.md
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<img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
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This UI provides access to various diffusion-based image generators implemented in the [`diffusers`](https://github.com/huggingface/diffusers) library, wrapped and re-distributed by GT4SD.
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For **examples** and **documentation** of the model parameters, please see below.
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Moreover, we provide a **model card** ([Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)) at the bottom of this page.
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model_cards/examples.csv
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LDMGenerator,
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LDMTextToImageGenerator,Generative models on the moon
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requirements.txt
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-f https://download.pytorch.org/whl/cpu/torch_stable.html
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-f https://data.pyg.org/whl/torch-1.12.1+cpu.html
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# pip==20.2.4
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torch==1.12.1
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torch-scatter
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torch-spline-conv
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torch-sparse
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torch-geometric
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torchvision==0.13.1
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torchaudio==0.12.1
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gt4sd>=1.0.5
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molgx>=0.22.0a1
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molecule_generation
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nglview
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PyTDC==0.3.7
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gradio==3.12.0
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markdown-it-py>=2.1.0
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mols2grid>=0.2.0
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numpy==1.23.5
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pandas>=1.0.0
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terminator @ git+https://github.com/IBM/regression-transformer@gt4sd
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guacamol_baselines @ git+https://github.com/GT4SD/[email protected]
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moses @ git+https://github.com/GT4SD/[email protected]
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paccmann_chemistry @ git+https://github.com/PaccMann/[email protected]
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paccmann_generator @ git+https://github.com/PaccMann/[email protected]
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paccmann_gp @ git+https://github.com/PaccMann/[email protected]
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paccmann_omics @ git+https://github.com/PaccMann/[email protected]
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paccmann_predictor @ git+https://github.com/PaccMann/paccmann_predictor@sarscov2
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reinvent_models @ git+https://github.com/GT4SD/[email protected]
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utils.py
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import logging
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from collections import defaultdict
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from typing import List
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import mols2grid
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import pandas as pd
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logger = logging.getLogger(__name__)
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logger.addHandler(logging.NullHandler())
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def draw_grid_generate(
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samples: List[str],
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seeds: List[str] = [],
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n_cols: int = 3,
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size=(140, 200),
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) -> str:
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18 |
+
"""
|
19 |
+
Uses mols2grid to draw a HTML grid for the generated molecules
|
20 |
+
|
21 |
+
Args:
|
22 |
+
samples: The generated samples.
|
23 |
+
n_cols: Number of columns in grid. Defaults to 5.
|
24 |
+
size: Size of molecule in grid. Defaults to (140, 200).
|
25 |
+
|
26 |
+
Returns:
|
27 |
+
HTML to display
|
28 |
+
"""
|
29 |
+
|
30 |
+
result = defaultdict(list)
|
31 |
+
result.update(
|
32 |
+
{
|
33 |
+
"SMILES": seeds + samples,
|
34 |
+
"Name": [f"Seed_{i}" for i in range(len(seeds))]
|
35 |
+
+ [f"Generated_{i}" for i in range(len(samples))],
|
36 |
+
},
|
37 |
+
)
|
38 |
+
|
39 |
+
result_df = pd.DataFrame(result)
|
40 |
+
obj = mols2grid.display(
|
41 |
+
result_df,
|
42 |
+
tooltip=list(result.keys()),
|
43 |
+
height=1100,
|
44 |
+
n_cols=n_cols,
|
45 |
+
name="Results",
|
46 |
+
size=size,
|
47 |
+
)
|
48 |
+
return obj.data
|