diff --git a/.github/workflows/code-tests.yaml b/.github/workflows/code-tests.yaml index 05587a85..9e5a8468 100644 --- a/.github/workflows/code-tests.yaml +++ b/.github/workflows/code-tests.yaml @@ -19,16 +19,20 @@ jobs: strategy: matrix: platform: [ubuntu-latest, macos-latest, windows-latest] - python-version: [3.9, "3.10", 3.11] + python-version: [3.9, "3.10"] runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2.3.1 + - name: Setup miniconda + uses: conda-incubator/setup-miniconda@v2 with: + auto-update-conda: true + miniforge-variant: Mambaforge + channels: "conda-forge, pytorch, pyg" python-version: ${{ matrix.python-version }} + use-mamba: true - id: cache-dependencies name: Cache dependencies uses: actions/cache@v2.1.7 diff --git a/.gitignore b/.gitignore index f458283c..8758df9c 100644 --- a/.gitignore +++ b/.gitignore @@ -143,12 +143,12 @@ proteinworkshop/data/* !proteinworkshop/data/.gitkeep logs/ +ProteinWorkshop/ wandb/ .DS_Store .env +# Explanations +explanations/ # Visualisations visualisations/ - -# Explanations -explanations/ \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..109beb97 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,25 @@ +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-added-large-files +- repo: https://github.com/ambv/black + rev: 23.9.1 + hooks: + - id: black +- repo: https://github.com/jsh9/pydoclint + # pydoclint version. + rev: 0.3.3 + hooks: + - id: pydoclint + args: + - "--config=pyproject.toml" +- repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.1.1 + hooks: + - id: ruff + args: [--fix, --exit-non-zero-on-fix] diff --git a/CHANGELOG.md b/CHANGELOG.md index 7a19ad8e..14328430 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,33 @@ +### 0.2.6 (Unreleased) + + + +#### Datasets + +* Adds to antibody-specific datasets using the IGFold corpuses for paired OAS and Jaffe 2022 [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Set `in_memory=True` as default for most (small) datasets for improved performance [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Fix `num_classes` for GO datamodules * Set `in_memory=True` as default for most (downstream) datasets for improved performance [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Fixes GO labelling [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) + + +### Features +* Improves positional encoding performance by adding a `seq_pos` attribute on `Data/Protein` objects in the base dataset getter. [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) + +### Models +* Adds CDConv implementation [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Adds tuned hparams for models [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) + +### Framework +* Refactors beartype/jaxtyping to use latest recommended syntax [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Adds explainability module for performing attribution on a trained model [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Change default finetuning features in config: `ca_base` -> `ca_seq` [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Add optional hparam entry point to finetuning config [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Fixes GPU memory accumulation for some metrics [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Updates zenodo URL for processed datasets to reflect upstream API change [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Adds multi-hot label encoding transform [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Fixes auto PyG install for `torch>2.1.0` [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Adds `proteinworkshop.model_io` containing utils for loading trained models [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) + ### 0.2.5 (25/09/2024) * Implement ESM embedding encoder ([#33](https://github.com/a-r-j/ProteinWorkshop/pull/33), [#41](https://github.com/a-r-j/ProteinWorkshop/pull/33)) diff --git a/README.md b/README.md index 21a2fbf4..7a7b553e 100644 --- a/README.md +++ b/README.md @@ -37,6 +37,7 @@ Configuration files to run the experiments described in the manuscript are provi - [Running a sweep/experiment](#running-a-sweepexperiment) - [Embedding a dataset](#embedding-a-dataset) - [Visualising a dataset's embeddings](#visualising-pre-trained-model-embeddings-for-a-given-dataset) + - [Performing attribution of a pre-trained model](#performing-attribution-of-a-pre-trained-model) - [Verifying a config](#verifying-a-config) - [Using `proteinworkshop` modules functionally](#using-proteinworkshop-modules-functionally) - [Models](#models) @@ -67,14 +68,11 @@ Below, we outline how one may set up a virtual environment for `proteinworkshop` ### From PyPI -`proteinworkshop` is available for install [from PyPI](https://pypi.org/project/proteinworkshop/). This enables training of specific configurations via the CLI **or** using individual components from the benchmark, such as datasets, featurisers, or transforms, as drop-ins to other projects. Make sure to install [PyTorch](https://pytorch.org/) (specifically version `2.0.0`) using its official `pip` installation instructions, with CUDA support as desired. +`proteinworkshop` is available for install [from PyPI](https://pypi.org/project/proteinworkshop/). This enables training of specific configurations via the CLI **or** using individual components from the benchmark, such as datasets, featurisers, or transforms, as drop-ins to other projects. Make sure to install [PyTorch](https://pytorch.org/) (specifically version `2.1.2` or newer) using its official `pip` installation instructions, with CUDA support as desired. ```bash # install `proteinworkshop` from PyPI -pip install proteinworkshop --no-cache-dir - -# e.g., install PyTorch with CUDA 11.8 support on Linux -pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118 --no-cache-dir +pip install proteinworkshop # install PyTorch Geometric using the (now-installed) CLI workshop install pyg @@ -86,7 +84,7 @@ export DATA_PATH="where/you/want/data/" # e.g., `export DATA_PATH="proteinworksh However, for full exploration we recommend cloning the repository and building from source. ### Building from source -With a local virtual environment activated (e.g., one created with `conda create -n proteinworkshop python=3.9`): +With a local virtual environment activated (e.g., one created with `conda create -n proteinworkshop python=3.10`): 1. Clone and install the project ```bash @@ -95,11 +93,11 @@ With a local virtual environment activated (e.g., one created with `conda create pip install -e . ``` -2. Install [PyTorch](https://pytorch.org/) (specifically version `2.0.0`) using its official `pip` installation instructions, with CUDA support as desired (N.B. make sure to add `--no-cache-dir` to the end of the `pip` installation command) +2. Install [PyTorch](https://pytorch.org/) (specifically version `2.1.2` or newer) using its official `pip` installation instructions, with CUDA support as desired ```bash # e.g., to install PyTorch with CUDA 11.8 support on Linux: - pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118 --no-cache-dir + pip install torch==2.1.2+cu118 torchvision==0.16.2+cu118 torchaudio==2.1.2+cu118 --index-url https://download.pytorch.org/whl/cu118 ``` 3. Then use the newly-installed `proteinworkshop` CLI to install [PyTorch Geometric](https://pyg.org/) @@ -252,6 +250,21 @@ python proteinworkshop/visualise.py ckpt_path=PATH/TO/CHECKPOINT plot_filepath=V ``` See the `visualise` section of `proteinworkshop/config/visualise.yaml` for additional parameters. +### Performing attribution of a pre-trained model + +We provide a utility in `proteinworkshop/explain.py` for performing attribution of a pre-trained model using integrated gradients. + +This will write PDB files for all the structures in a dataset for a supervised task with residue-level attributions in the `b_factor` column. To visualise the attributions, we recommend using the [Protein Viewer VSCode extension](https://marketplace.visualstudio.com/items?itemName=ArianJamasb.protein-viewer) and changing the 3D representation to colour by `Uncertainty/Disorder`. + +To run the attribution: + +```bash +python proteinworkshop/explain.py ckpt_path=PATH/TO/CHECKPOINT output_dir=ATTRIBUTION/DIRECTORY +``` + +See the `explain` section of `proteinworkshop/config/explain.yaml` for additional parameters. + + ### Verifying a config ```bash @@ -309,6 +322,7 @@ Read [the docs](https://www.proteins.sh) for a full list of modules available in | `GearNet`| [Zhang et al.](https://arxiv.org/abs/2203.06125) | ✓ | `DimeNet++` | [Gasteiger et al.](https://arxiv.org/abs/2011.14115) | ✗ | `SchNet` | [Schütt et al.](https://arxiv.org/abs/1706.08566) | ✗ +| `CDConv` | [Fan et al.](https://openreview.net/forum?id=P5Z-Zl9XJ7) | ✓ ### Equivariant Graph Encoders @@ -361,8 +375,11 @@ Pre-training corpuses (with the exception of `pdb`, `cath`, and `astral`) are pr | `esmatlas` | [ESMAtlas](https://esmatlas.com/) predictions (full) | [Kim et al.](https://academic.oup.com/bioinformatics/article/39/4/btad153/7085592) | | 1 Tb | [GPL-3.0](https://github.com/steineggerlab/foldcomp/blob/master/LICENSE.txt) / [CC-BY 4.0](https://esmatlas.com/about) | `esmatlas_v2023_02`| [ESMAtlas](https://esmatlas.com/) predictions (v2023_02 release) | [Kim et al.](https://academic.oup.com/bioinformatics/article/39/4/btad153/7085592) | | 137 Gb| [GPL-3.0](https://github.com/steineggerlab/foldcomp/blob/master/LICENSE.txt) / [CC-BY 4.0](https://esmatlas.com/about) | `highquality_clust30`| [ESMAtlas](https://esmatlas.com/) High Quality predictions | [Kim et al.](https://academic.oup.com/bioinformatics/article/39/4/btad153/7085592) | 37M Chains | 114 Gb | [GPL-3.0](https://github.com/steineggerlab/foldcomp/blob/master/LICENSE.txt) / [CC-BY 4.0](https://esmatlas.com/about) +| `igfold_paired_oas` | IGFold Predictions for [Paired OAS](https://journals.aai.org/jimmunol/article/201/8/2502/107069/Observed-Antibody-Space-A-Resource-for-Data-Mining) | [Ruffolo et al.](https://www.nature.com/articles/s41467-023-38063-x) | 104,994 paired Ab chains | | [CC-BY 4.0](https://www.nature.com/articles/s41467-023-38063-x#rightslink) +| `igfold_jaffe` | IGFold predictions for [Jaffe2022](https://www.nature.com/articles/s41586-022-05371-z) data | [Ruffolo et al.](https://www.nature.com/articles/s41467-023-38063-x) | 1,340,180 paired Ab chains | | [CC-BY 4.0](https://www.nature.com/articles/s41467-023-38063-x#rightslink) | `pdb`| Experimental structures deposited in the [RCSB Protein Data Bank](https://www.rcsb.org/) | [wwPDB consortium](https://academic.oup.com/nar/article/47/D1/D520/5144142) | ~800k Chains |23 Gb | [CC0 1.0](https://www.rcsb.org/news/feature/611e8d97ef055f03d1f222c6) | +
Additionally, we provide several species-specific compilations (mostly reference species) @@ -528,8 +545,8 @@ We use `poetry` to manage the project's underlying dependencies and to push upda To keep with the code style for the `proteinworkshop` repository, using the following lines, please format your commits before opening a pull request: ```bash # assuming you are located in the `ProteinWorkshop` top-level directory -isort . -autoflake -r --in-place --remove-unused-variables --remove-all-unused-imports --ignore-init-module-imports . +isort . +autoflake -r --in-place --remove-unused-variables --remove-all-unused-imports --ignore-init-module-imports . black --config=pyproject.toml . ``` diff --git a/docs/source/conf.py b/docs/source/conf.py index 6c1a5c27..fd6cb62d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -23,6 +23,7 @@ "sphinx.ext.autosummary", "sphinx.ext.intersphinx", "sphinx.ext.viewcode", + "sphinx.ext.doctest", "sphinx_copybutton", "sphinx_inline_tabs", "sphinxcontrib.gtagjs", @@ -32,7 +33,7 @@ "nbsphinx_link", "sphinx.ext.napoleon", "sphinx_codeautolink", - "sphinxcontrib.jquery" + "sphinxcontrib.jquery", # "sphinx_autorun", ] @@ -109,7 +110,6 @@ "vu": "\\mathbf{u}", "vv": "\\mathbf{v}", "vw": "\\mathbf{w}", - "vx": "\\mathbf{x}", "vy": "\\mathbf{y}", "vz": "\\mathbf{z}", } diff --git a/docs/source/configs/dataset.rst b/docs/source/configs/dataset.rst index cd6a9aa6..d9133965 100644 --- a/docs/source/configs/dataset.rst +++ b/docs/source/configs/dataset.rst @@ -31,8 +31,8 @@ Unlabelled Datasets .. mdinclude:: ../../../README.md - :start-line: 331 - :end-line: 373 + :start-line: 361 + :end-line: 406 :py:class:`ASTRAL ` (``astral``) @@ -116,7 +116,7 @@ This is a dataset of approximately 3 million protein structures from the AlphaFo Species-Specific Datasets ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -TODO +Stay tuned! Graph-level Datasets diff --git a/docs/source/configs/features.rst b/docs/source/configs/features.rst index a84a0bc9..b515b17c 100644 --- a/docs/source/configs/features.rst +++ b/docs/source/configs/features.rst @@ -7,8 +7,8 @@ Features :width: 400 .. mdinclude:: ../../../README.md - :start-line: 426 - :end-line: 475 + :start-line: 459 + :end-line: 508 Default Features diff --git a/docs/source/configs/framework_components/env.rst b/docs/source/configs/framework_components/env.rst index 109b43f6..93d4b333 100644 --- a/docs/source/configs/framework_components/env.rst +++ b/docs/source/configs/framework_components/env.rst @@ -2,8 +2,8 @@ Environment ------------ .. mdinclude:: ../../../../README.md - :start-line: 109 - :end-line: 111 + :start-line: 108 + :end-line: 110 .. literalinclude:: ../../../../.env.example :language: bash diff --git a/docs/source/configs/model.rst b/docs/source/configs/model.rst index a7323632..a32c2f0f 100644 --- a/docs/source/configs/model.rst +++ b/docs/source/configs/model.rst @@ -34,14 +34,14 @@ Invariant Encoders ============================= .. mdinclude:: ../../../README.md - :start-line: 295 - :end-line: 302 + :start-line: 319 + :end-line: 326 :py:class:`SchNet ` (``schnet``) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ SchNet is one of the most popular and simplest instantiation of E(3) invariant message passing GNNs. SchNet constructs messages through element-wise multiplication of scalar features modulated by a radial filter conditioned on the pairwise distance :math:`\Vert \vec{\vx}_{ij} \Vert`` between two neighbours. -Scalar features are update from iteration :math:`t`` to :math:`t+1` via: +Scalar features are updated from iteration :math:`t`` to :math:`t+1` via: .. math:: \begin{align} @@ -113,12 +113,25 @@ where :math:`\mathrm{FC(\cdot)}` denotes a linear transformation upon the messag :caption: config/encoder/gear_net_edge.yaml + +:py:class:`CDConv ` (``cdconv``) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +CDConv is an SE(3) invariant architecture that uses independent learnable weights for sequential displacement, whilst directly encoding geometric displacements. + +As a result of the downsampling procedures, this architecture is only suitable for graph-level prediction tasks. + +.. literalinclude:: ../../../proteinworkshop/config/encoder/cdconv.yaml + :language: yaml + :caption: config/encoder/cdconv.yaml + + Vector-Equivariant Encoders ============================= .. mdinclude:: ../../../README.md - :start-line: 306 - :end-line: 312 + :start-line: 330 + :end-line: 336 :py:class:`EGNN ` (``egnn``) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -170,8 +183,8 @@ Tensor-Equivariant Encoders ============================= .. mdinclude:: ../../../README.md - :start-line: 314 - :end-line: 319 + :start-line: 338 + :end-line: 343 :py:class:`Tensor Field Networks ` (``tfn``) @@ -200,7 +213,7 @@ where the weights :math:`\vw` of the tensor product are computed via a learnt ra MACE (Batatia et al., 2022) is a higher order E(3) or SE(3) equivariant GNN originally developed for molecular dynamics simulations. MACE provides an efficient approach to computing high body order equivariant features in the Tensor Field Network framework via Atomic Cluster Expansion: -They first aggregate neighbourhood features analogous to the node update equation for TFN above (the :math:`A` functions in Batatia et al. (2022) (eq.9)) and then take :math:`k-1` repeated self-tensor products of these neighbourhood features. +They first aggregate neighbourhood features analogous to the node update equation for TFN above (the :math:`A` functions in Batatia et al. (2022) (eq.9)) and then take :math:`k-1` repeated self-tensor products of these neighbourhood features. In our formalism, this corresponds to: .. math:: @@ -214,6 +227,25 @@ In our formalism, this corresponds to: :caption: config/encoder/mace.yaml +Sequence-Based Encoders +============================= + +.. mdinclude:: ../../../README.md + :start-line: 345 + :end-line: 349 + + +:py:class:`Evolutionary Scale Modeling ` (``esm``) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Evolutionary Scale Modeling is a series of Transformer-based protein sequence encoders (Vaswani et al., 2017) that has been successfully used in protein structure prediction (Lin et al., 2023), protein design (Verkuil et al., 2022), and beyond. +This model class has commonly been used as a baseline for protein-related representation learning tasks, and we included it in our benchmark for this reason. + +.. literalinclude:: ../../../proteinworkshop/config/encoder/esm.yaml + :language: yaml + :caption: config/encoder/esm.yaml + + Decoder Models ============================= diff --git a/docs/source/index.rst b/docs/source/index.rst index 1ec868be..638b7dfa 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -42,6 +42,7 @@ Welcome to Protein Workshop's documentation! configs/task configs/features configs/transforms + configs/metrics framework ml_components @@ -57,9 +58,9 @@ Welcome to Protein Workshop's documentation! modules/proteinworkshop.tasks modules/proteinworkshop.features modules/proteinworkshop.utils - modules/protein_workshop.constants + modules/proteinworkshop.constants modules/proteinworkshop.types - + modules/proteinworkshop.metrics Indices and tables ================== diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 8410b28b..ba9256ab 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -5,5 +5,5 @@ Installation :doc:`/configs/framework_components/env` .. mdinclude:: ../../README.md - :start-line: 64 - :end-line: 109 + :start-line: 66 + :end-line: 108 diff --git a/docs/source/modules/proteinworkshop.metrics.rst b/docs/source/modules/proteinworkshop.metrics.rst index e69de29b..9a5a409e 100644 --- a/docs/source/modules/proteinworkshop.metrics.rst +++ b/docs/source/modules/proteinworkshop.metrics.rst @@ -0,0 +1,4 @@ +protein_workshop.metrics +------------------------- + +Stay tuned! diff --git a/docs/source/modules/proteinworkshop.models.rst b/docs/source/modules/proteinworkshop.models.rst index 0d57c20e..d7f8e156 100644 --- a/docs/source/modules/proteinworkshop.models.rst +++ b/docs/source/modules/proteinworkshop.models.rst @@ -5,7 +5,7 @@ To switch between different encoder architectures, simply change the ``encoder`` .. code-block:: bash workshop train encoder= dataset=cath task=inverse_folding trainer=cpu - # or + # or python proteinworkshop/train.py encoder= dataset=cath task=inverse_folding trainer=cpu # or trainer=gpu Where ```` is given by bracketed name in the listing below. For example, the encoder name for SchNet is ``schnet``. @@ -65,6 +65,12 @@ GearNet (``gear_net``, ``gear_net_edge``) :undoc-members: :show-inheritance: +CDConv (``cdconv``) +^^^^^^^^^^^^^^^^^^^^^ +.. automodule:: proteinworkshop.models.graph_encoders.cdconv + :members: + :undoc-members: + :show-inheritance: Vector-Equivariant Encoders ============================= @@ -112,10 +118,21 @@ Multi-Atomic Cluster Expansion (``mace``) :show-inheritance: +Sequence-Based Encoders +============================= + +Evolutionary Scale Modeling (``esm``) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. automodule:: proteinworkshop.models.graph_encoders.esm_embeddings + :members: + :undoc-members: + :show-inheritance: + + Decoders ============================= .. automodule:: proteinworkshop.models.decoders.mlp_decoder :members: :undoc-members: - :show-inheritance: \ No newline at end of file + :show-inheritance: diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index aa49ef93..6cc38c63 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -2,8 +2,8 @@ Quickstart --------------------- .. mdinclude:: ../../README.md - :start-line: 130 - :end-line: 289 + :start-line: 129 + :end-line: 313 .. toctree:: :maxdepth: 3 diff --git a/docs/source/quickstart_component/downstream_component.html b/docs/source/quickstart_component/downstream_component.html index eb7fb504..b475b3e5 100644 --- a/docs/source/quickstart_component/downstream_component.html +++ b/docs/source/quickstart_component/downstream_component.html @@ -87,11 +87,13 @@ var modelList = [ ['schnet', "SchNet"], ['dimenetpp', 'DimeNet'], + ['cdconv', "CDConv"], ['egnn', 'EGNN'], ['gvp', "GVP"], ['gcpnet', 'GCPNet'], ['tfn', 'TFN'], - ['mace', 'MACE'] + ['mace', 'MACE'], + ['esm', 'ESM'] ]; var taskList = [ @@ -183,4 +185,4 @@ $("#datasets").children().get(6).click(); $("#features").children().get(2).click(); $("#auxtask").children().get(0).click(); - \ No newline at end of file + diff --git a/docs/source/quickstart_component/pretrain_component.html b/docs/source/quickstart_component/pretrain_component.html index f5b6aa04..f93dc168 100644 --- a/docs/source/quickstart_component/pretrain_component.html +++ b/docs/source/quickstart_component/pretrain_component.html @@ -80,11 +80,13 @@ var modelList = [ ['schnet', "SchNet"], ['dimenetpp', 'DimeNet'], + ['cdconv', "CDConv"], ['egnn', 'EGNN'], ['gvp', "GVP"], ['gcpnet', 'GCPNet'], ['tfn', 'TFN'], - ['mace', 'MACE'] + ['mace', 'MACE'], + ['esm', 'ESM'] ]; var taskList = [ @@ -151,4 +153,4 @@ $("#features").children().get(2).click(); $("#auxtask").children().get(0).click(); - \ No newline at end of file + diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst index 2c24f7ae..e412a5a0 100644 --- a/docs/source/tutorials.rst +++ b/docs/source/tutorials.rst @@ -2,8 +2,8 @@ Tutorials --------------------- .. mdinclude:: ../../README.md - :start-line: 119 - :end-line: 128 + :start-line: 118 + :end-line: 127 Training a New Model @@ -45,7 +45,7 @@ Evaluating a pre-trained Model from graphein.protein.tensor.data import ProteinBatch from proteinworkshop.models.utils import get_aggregation from jaxtyping import jaxtyped - from beartype import beartype + from beartype import beartype as typechecker class IdentityModel(nn.Module): @@ -58,8 +58,7 @@ Evaluating a pre-trained Model """This property describes the required attributes of the input batch.""" return {"x", "batch"} - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> Dict[str, torch.Tensor]: """ This method does the forward pass of the model. diff --git a/notebooks/adding_new_task_tutorial.ipynb b/notebooks/adding_new_task_tutorial.ipynb index d9cf4622..4b82ff9a 100644 --- a/notebooks/adding_new_task_tutorial.ipynb +++ b/notebooks/adding_new_task_tutorial.ipynb @@ -67,7 +67,7 @@ " def required_attributes(self) -> Set[str]:\n", " return {\"residue_type\"}\n", "\n", - " @beartype\n", + " @jaxtyped(typechecker=typechecker)\n", " def __call__(self, x: Union[Data, Protein]) -> Union[Data, Protein]:\n", " x.residue_type_uncorrupted = copy.deepcopy(x.residue_type)\n", " # Get indices of residues to corrupt\n", diff --git a/poetry.lock b/poetry.lock index da9646fc..b7f9d840 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,112 +1,100 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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True # Whether to load the entire dataset into memory num_classes: 23 # Number of classes diff --git a/proteinworkshop/config/dataset/ec_reaction.yaml b/proteinworkshop/config/dataset/ec_reaction.yaml index 36a3bcc1..d67271e4 100644 --- a/proteinworkshop/config/dataset/ec_reaction.yaml +++ b/proteinworkshop/config/dataset/ec_reaction.yaml @@ -10,4 +10,5 @@ datamodule: shuffle_labels: False # Whether to shuffle labels for permutation testing transforms: ${transforms} overwrite: False + in_memory: True num_classes: 384 diff --git a/proteinworkshop/config/dataset/fold_family.yaml b/proteinworkshop/config/dataset/fold_family.yaml index 1060774b..6a10a515 100644 --- a/proteinworkshop/config/dataset/fold_family.yaml +++ b/proteinworkshop/config/dataset/fold_family.yaml @@ -9,4 +9,5 @@ datamodule: shuffle_labels: False # Whether to shuffle labels for permutation testing transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files + in_memory: True # Whether to load the entire dataset into memory num_classes: 1195 # Number of classes diff --git a/proteinworkshop/config/dataset/fold_fold.yaml b/proteinworkshop/config/dataset/fold_fold.yaml index 8d0afe79..c7d62620 100644 --- a/proteinworkshop/config/dataset/fold_fold.yaml +++ b/proteinworkshop/config/dataset/fold_fold.yaml @@ -9,4 +9,5 @@ datamodule: shuffle_labels: False # Whether to shuffle labels for permutation testing transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files + in_memory: True # Whether to load the entire dataset into memory num_classes: 1195 # Number of classes diff --git a/proteinworkshop/config/dataset/fold_superfamily.yaml b/proteinworkshop/config/dataset/fold_superfamily.yaml index e53d2b00..b92f92b4 100644 --- a/proteinworkshop/config/dataset/fold_superfamily.yaml +++ b/proteinworkshop/config/dataset/fold_superfamily.yaml @@ -9,4 +9,5 @@ datamodule: shuffle_labels: False # Whether to shuffle labels for permutation testing transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files + in_memory: True # Whether to load the entire dataset into memory num_classes: 1195 # Number of classes diff --git a/proteinworkshop/config/dataset/go-bp.yaml b/proteinworkshop/config/dataset/go-bp.yaml index 447960df..9d5d8a8a 100644 --- a/proteinworkshop/config/dataset/go-bp.yaml +++ b/proteinworkshop/config/dataset/go-bp.yaml @@ -11,4 +11,5 @@ datamodule: split: "BP" # Split of the dataset to use (`BP`, `MF`, `CC`) transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files -num_classes: 2 # Number of classes + in_memory: True +num_classes: 1943 # Number of classes diff --git a/proteinworkshop/config/dataset/go-cc.yaml b/proteinworkshop/config/dataset/go-cc.yaml index d60e99ae..4f787ee5 100644 --- a/proteinworkshop/config/dataset/go-cc.yaml +++ b/proteinworkshop/config/dataset/go-cc.yaml @@ -11,4 +11,5 @@ datamodule: split: "CC" # Split of the dataset to use (`BP`, `MF`, `CC`) transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files -num_classes: 2 # Number of classes + in_memory: True +num_classes: 320 # Number of classes diff --git a/proteinworkshop/config/dataset/go-mf.yaml b/proteinworkshop/config/dataset/go-mf.yaml index 34b19835..2b333e16 100644 --- a/proteinworkshop/config/dataset/go-mf.yaml +++ b/proteinworkshop/config/dataset/go-mf.yaml @@ -11,4 +11,5 @@ datamodule: split: "MF" # Split of the dataset to use (`BP`, `MF`, `CC`) transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files -num_classes: 2 # Number of classes + in_memory: True +num_classes: 489 # Number of classes diff --git a/proteinworkshop/config/dataset/igfold_jaffe.yaml b/proteinworkshop/config/dataset/igfold_jaffe.yaml new file mode 100644 index 00000000..697b3219 --- /dev/null +++ b/proteinworkshop/config/dataset/igfold_jaffe.yaml @@ -0,0 +1,12 @@ +datamodule: + _target_: proteinworkshop.datasets.igfold.IgFoldDataModule + path: ${env.paths.data}/igfold_jaffe/ # Directory where the dataset is stored + dataset_name: "paired_oas" + format: "pdb" # Format of the raw PDB/MMTF files + train_val_test: [0.8, 0.1, 0.1] + batch_size: 32 # Batch size for dataloader + dataset_fraction: 1.0 # Fraction of the dataset to use + pin_memory: True # Pin memory for dataloader + num_workers: 8 # Number of workers for dataloader + transforms: ${transforms} +num_classes: 2 diff --git a/proteinworkshop/config/dataset/igfold_paired_oas.yaml b/proteinworkshop/config/dataset/igfold_paired_oas.yaml new file mode 100644 index 00000000..2c8e3247 --- /dev/null +++ b/proteinworkshop/config/dataset/igfold_paired_oas.yaml @@ -0,0 +1,12 @@ +datamodule: + _target_: proteinworkshop.datasets.igfold.IgFoldDataModule + path: ${env.paths.data}/igfold_paired_oas/ # Directory where the dataset is stored + dataset_name: "paired_oas" + format: "pdb" # Format of the raw PDB/MMTF files + train_val_test: [0.8, 0.1, 0.1] + batch_size: 32 # Batch size for dataloader + dataset_fraction: 1.0 # Fraction of the dataset to use + pin_memory: True # Pin memory for dataloader + num_workers: 8 # Number of workers for dataloader + transforms: ${transforms} +num_classes: 2 diff --git a/proteinworkshop/config/encoder/cdconv.yaml b/proteinworkshop/config/encoder/cdconv.yaml new file mode 100644 index 00000000..00eb21e4 --- /dev/null +++ b/proteinworkshop/config/encoder/cdconv.yaml @@ -0,0 +1,11 @@ +_target_: "proteinworkshop.models.graph_encoders.cdconv.CDConvModel" +geometric_radius: 4.0 +sequential_kernel_size: 5 +kernel_channels: [24] +channels: [256, 512, 1024, 2048] +base_width: 64.0 +embedding_dim: 16 +batch_norm: True +dropout: 0.2 +bias: False +readout: "sum" diff --git a/proteinworkshop/config/explain.yaml b/proteinworkshop/config/explain.yaml new file mode 100644 index 00000000..5921a4ea --- /dev/null +++ b/proteinworkshop/config/explain.yaml @@ -0,0 +1,42 @@ +# @package _global_ + +# === 1. Set config parameters === +name: "" # default name for the experiment, "" means logger (eg. wandb) will generate a unique name +seed: 52 # seed for random number generators in pytorch, numpy and python.random +num_workers: 16 # number of subprocesses to use for data loading. + +# === 2. Specify defaults here. Defaults will be overwritten by equivalently named options in this file === +defaults: + - env: default + - dataset: fold_fold + - features: ca + - encoder: egnn + - decoder: default + - transforms: none + - callbacks: default + - trainer: gpu + - extras: default + - hydra: default + - metrics: default + - task: inverse_folding + - logger: wandb + - finetune: full_model + # debugging config (enable through command line, e.g. `python train.py debug=default) + - debug: null + # optional hardware config for machine/user specific settings + # (optional since it doesn't need to exist and is excluded from version control) + #- optional hardware: cpu + - _self_ # see: https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order/. Adding _self_ at bottom means values in this file override defaults. + +task_name: "visualise" + +compile: True + +# simply provide checkpoint path and output directory to embed dataset and write attributed PDB files +ckpt_path: /home/jamasba/scratch/workshop_ckpts/runs/2023-09-18_23-46-54/checkpoints/epoch_038.ckpt +output_dir: ./explanations + +explain: + n_steps: 50 # Number of steps to perform Integrated Gradients for + output: "graph_label" # Name of the model output to attribute + split: ["train", "val", "test"] # Dataloaders to perform attribution for diff --git a/proteinworkshop/config/finetune.yaml b/proteinworkshop/config/finetune.yaml index bd6fb6d5..988233ab 100644 --- a/proteinworkshop/config/finetune.yaml +++ b/proteinworkshop/config/finetune.yaml @@ -9,7 +9,7 @@ num_workers: 16 # number of subprocesses to use for data loading. defaults: - env: default - dataset: cath - - features: ca_base + - features: ca_seq - encoder: egnn - decoder: default - transforms: none @@ -25,6 +25,7 @@ defaults: - finetune: default # Specifies finetuning config. See: proteinworkshop/config/finetune/ # debugging config (enable through command line, e.g. `python train.py debug=default) - debug: null + - optional hparams: ${encoder}_${features} - _self_ # see: https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order/. Adding _self_ at bottom means values in this file override defaults. task_name: "finetune" diff --git a/proteinworkshop/config/finetune/full_model.yaml b/proteinworkshop/config/finetune/full_model.yaml new file mode 100644 index 00000000..a8814112 --- /dev/null +++ b/proteinworkshop/config/finetune/full_model.yaml @@ -0,0 +1,8 @@ +# Controls the default configuration for the finetuning process. +encoder: + load_weights: True # Whether or not to load the weights of the encoder from the pretrained model. + freeze: False # Whether or not to freeze the weights of the encoder. + +decoder: + load_weights: True # Whether or not to load the weights of the decoder(s) from the pretrained model. + freeze: False # Whether or not to freeze the weights of the decoder(s). diff --git a/proteinworkshop/config/hparams/egnn_ca_angles.yaml b/proteinworkshop/config/hparams/egnn_ca_angles.yaml new file mode 100644 index 00000000..e28bc240 --- /dev/null +++ b/proteinworkshop/config/hparams/egnn_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/egnn_ca_base.yaml b/proteinworkshop/config/hparams/egnn_ca_base.yaml new file mode 100644 index 00000000..6620943f --- /dev/null +++ b/proteinworkshop/config/hparams/egnn_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/egnn_ca_bb.yaml b/proteinworkshop/config/hparams/egnn_ca_bb.yaml new file mode 100644 index 00000000..6620943f --- /dev/null +++ b/proteinworkshop/config/hparams/egnn_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/egnn_ca_sc.yaml b/proteinworkshop/config/hparams/egnn_ca_sc.yaml new file mode 100644 index 00000000..e3a108c4 --- /dev/null +++ b/proteinworkshop/config/hparams/egnn_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/egnn_ca_seq.yaml b/proteinworkshop/config/hparams/egnn_ca_seq.yaml new file mode 100644 index 00000000..6620943f --- /dev/null +++ b/proteinworkshop/config/hparams/egnn_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/esm_ca_angles.yaml b/proteinworkshop/config/hparams/esm_ca_angles.yaml new file mode 100644 index 00000000..e301ceb0 --- /dev/null +++ b/proteinworkshop/config/hparams/esm_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 diff --git a/proteinworkshop/config/hparams/esm_ca_base.yaml b/proteinworkshop/config/hparams/esm_ca_base.yaml new file mode 100644 index 00000000..e301ceb0 --- /dev/null +++ b/proteinworkshop/config/hparams/esm_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 diff --git a/proteinworkshop/config/hparams/esm_ca_bb.yaml b/proteinworkshop/config/hparams/esm_ca_bb.yaml new file mode 100644 index 00000000..e301ceb0 --- /dev/null +++ b/proteinworkshop/config/hparams/esm_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 diff --git a/proteinworkshop/config/hparams/esm_ca_sc.yaml b/proteinworkshop/config/hparams/esm_ca_sc.yaml new file mode 100644 index 00000000..e301ceb0 --- /dev/null +++ b/proteinworkshop/config/hparams/esm_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 diff --git a/proteinworkshop/config/hparams/esm_ca_seq.yaml b/proteinworkshop/config/hparams/esm_ca_seq.yaml new file mode 100644 index 00000000..e301ceb0 --- /dev/null +++ b/proteinworkshop/config/hparams/esm_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 diff --git a/proteinworkshop/config/hparams/gcpnet_ca_angles.yaml b/proteinworkshop/config/hparams/gcpnet_ca_angles.yaml new file mode 100644 index 00000000..e28bc240 --- /dev/null +++ b/proteinworkshop/config/hparams/gcpnet_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gcpnet_ca_base.yaml b/proteinworkshop/config/hparams/gcpnet_ca_base.yaml new file mode 100644 index 00000000..039828cb --- /dev/null +++ b/proteinworkshop/config/hparams/gcpnet_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gcpnet_ca_bb.yaml b/proteinworkshop/config/hparams/gcpnet_ca_bb.yaml new file mode 100644 index 00000000..e28bc240 --- /dev/null +++ b/proteinworkshop/config/hparams/gcpnet_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gcpnet_ca_sc.yaml b/proteinworkshop/config/hparams/gcpnet_ca_sc.yaml new file mode 100644 index 00000000..90f43dca --- /dev/null +++ b/proteinworkshop/config/hparams/gcpnet_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gcpnet_ca_seq.yaml b/proteinworkshop/config/hparams/gcpnet_ca_seq.yaml new file mode 100644 index 00000000..039828cb --- /dev/null +++ b/proteinworkshop/config/hparams/gcpnet_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gear_net_edge_ca_angles.yaml b/proteinworkshop/config/hparams/gear_net_edge_ca_angles.yaml new file mode 100644 index 00000000..6620943f --- /dev/null +++ b/proteinworkshop/config/hparams/gear_net_edge_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gear_net_edge_ca_base.yaml b/proteinworkshop/config/hparams/gear_net_edge_ca_base.yaml new file mode 100644 index 00000000..f466e78b --- /dev/null +++ b/proteinworkshop/config/hparams/gear_net_edge_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gear_net_edge_ca_bb.yaml b/proteinworkshop/config/hparams/gear_net_edge_ca_bb.yaml new file mode 100644 index 00000000..e3a108c4 --- /dev/null +++ b/proteinworkshop/config/hparams/gear_net_edge_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gear_net_edge_ca_sc.yaml b/proteinworkshop/config/hparams/gear_net_edge_ca_sc.yaml new file mode 100644 index 00000000..35fe8156 --- /dev/null +++ b/proteinworkshop/config/hparams/gear_net_edge_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.1 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gear_net_edge_ca_seq.yaml b/proteinworkshop/config/hparams/gear_net_edge_ca_seq.yaml new file mode 100644 index 00000000..e3a108c4 --- /dev/null +++ b/proteinworkshop/config/hparams/gear_net_edge_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/mace_ca_angles.yaml b/proteinworkshop/config/hparams/mace_ca_angles.yaml new file mode 100644 index 00000000..caddde5e --- /dev/null +++ b/proteinworkshop/config/hparams/mace_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0010 + decoder_dropout: 0.1 diff --git a/proteinworkshop/config/hparams/mace_ca_base.yaml b/proteinworkshop/config/hparams/mace_ca_base.yaml new file mode 100644 index 00000000..8a4aff61 --- /dev/null +++ b/proteinworkshop/config/hparams/mace_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0010 + decoder_dropout: 0.0 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/mace_ca_bb.yaml b/proteinworkshop/config/hparams/mace_ca_bb.yaml new file mode 100644 index 00000000..039828cb --- /dev/null +++ b/proteinworkshop/config/hparams/mace_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/mace_ca_sc.yaml b/proteinworkshop/config/hparams/mace_ca_sc.yaml new file mode 100644 index 00000000..039828cb --- /dev/null +++ b/proteinworkshop/config/hparams/mace_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/mace_ca_seq.yaml b/proteinworkshop/config/hparams/mace_ca_seq.yaml new file mode 100644 index 00000000..90f43dca --- /dev/null +++ b/proteinworkshop/config/hparams/mace_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/schnet_ca_angles.yaml b/proteinworkshop/config/hparams/schnet_ca_angles.yaml new file mode 100644 index 00000000..4fa85834 --- /dev/null +++ b/proteinworkshop/config/hparams/schnet_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.1 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/schnet_ca_base.yaml b/proteinworkshop/config/hparams/schnet_ca_base.yaml new file mode 100644 index 00000000..e28bc240 --- /dev/null +++ b/proteinworkshop/config/hparams/schnet_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/schnet_ca_bb.yaml b/proteinworkshop/config/hparams/schnet_ca_bb.yaml new file mode 100644 index 00000000..f466e78b --- /dev/null +++ b/proteinworkshop/config/hparams/schnet_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/schnet_ca_sc.yaml b/proteinworkshop/config/hparams/schnet_ca_sc.yaml new file mode 100644 index 00000000..e28bc240 --- /dev/null +++ b/proteinworkshop/config/hparams/schnet_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/schnet_ca_seq.yaml b/proteinworkshop/config/hparams/schnet_ca_seq.yaml new file mode 100644 index 00000000..90f43dca --- /dev/null +++ b/proteinworkshop/config/hparams/schnet_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/tfn_ca_angles.yaml b/proteinworkshop/config/hparams/tfn_ca_angles.yaml new file mode 100644 index 00000000..f466e78b --- /dev/null +++ b/proteinworkshop/config/hparams/tfn_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/tfn_ca_base.yaml b/proteinworkshop/config/hparams/tfn_ca_base.yaml new file mode 100644 index 00000000..039828cb --- /dev/null +++ b/proteinworkshop/config/hparams/tfn_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/tfn_ca_bb.yaml b/proteinworkshop/config/hparams/tfn_ca_bb.yaml new file mode 100644 index 00000000..039828cb --- /dev/null +++ b/proteinworkshop/config/hparams/tfn_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/tfn_ca_sc.yaml b/proteinworkshop/config/hparams/tfn_ca_sc.yaml new file mode 100644 index 00000000..f466e78b --- /dev/null +++ b/proteinworkshop/config/hparams/tfn_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/tfn_ca_seq.yaml b/proteinworkshop/config/hparams/tfn_ca_seq.yaml new file mode 100644 index 00000000..e3a108c4 --- /dev/null +++ b/proteinworkshop/config/hparams/tfn_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/sweeps/baseline_antibody_developability.yaml b/proteinworkshop/config/sweeps/baseline_antibody_developability.yaml new file mode 100644 index 00000000..75a16ca5 --- /dev/null +++ b/proteinworkshop/config/sweeps/baseline_antibody_developability.yaml @@ -0,0 +1,64 @@ +program: proteinworkshop/train.py +method: grid +name: baseline_antibody_developability +metric: # Does not matter, as we are using sweep to run the experiment. + goal: minimize + name: val/loss/total + +parameters: + task: + values: [binary_graph_classification] + + dataset: + value: [antibody_developability] + + dataset.datamodule.batch_size: + value: 8 + + encoder: + values: [schnet, gear_net_edge, egnn, gcpnet, tfn, mace] + + features: + values: [ca_base, ca_seq, ca_angles, ca_bb, ca_sc] + + scheduler: + values: [plateau] + + logger: + value: wandb + + trainer: + value: gpu + + extras.enforce_tags: + value: False + + +aux_task: + #values: [none, nn_sequence, nn_structure_torsion, nn_structure_r3] + values: [none, nn_sequence, nn_structure_r3] + + trainer.max_epochs: + value: 600 + + name: + value: "${hydra:runtime.choices.encoder}_${hydra:runtime.choices.features}_${hydra:runtime.choices.aux_task}_seed_${seed}" + + test: + value: True + + optimiser.optimizer.lr: + value: ${hparams.hparams.lr} + + decoder.graph_label.dropout: + value: ${hparams.hparams.decoder_dropout} + + #seed: + # values: [13, 42, 121] + +command: + - ${env} + - HYDRA_FULL_ERROR=1 + - WANDB_START_METHOD=thread + - python + - ${program} + - ${args_no_hyphens} diff --git a/proteinworkshop/config/sweeps/baseline_ec_reaction.yaml b/proteinworkshop/config/sweeps/baseline_ec_reaction.yaml new file mode 100644 index 00000000..cfb98ac5 --- /dev/null +++ b/proteinworkshop/config/sweeps/baseline_ec_reaction.yaml @@ -0,0 +1,64 @@ +program: proteinworkshop/train.py +method: grid +name: baseline_ec_reaction +metric: # Does not matter, as we are using sweep to run the experiment. + goal: minimize + name: val/loss/total + +parameters: + task: + values: [multiclass_graph_classification] + + dataset: + values: [ec_reaction] + + dataset.datamodule.batch_size: + value: 8 + + encoder: + values: [schnet, gear_net_edge, egnn, gcpnet, tfn, mace] + + + features: + values: [ca_base, ca_seq, ca_angles, ca_bb, ca_sc] + + scheduler: + value: plateau + + extras.enforce_tags: + value: False + + +aux_task: + values: [none, nn_sequence, nn_structure_r3] #nn_structure_torsion, nn_structure_r3] + + trainer.max_epochs: + value: 300 + + test: + value: True + + trainer: + value: gpu + + logger: + value: wandb + + name: + value: "${hydra:runtime.choices.encoder}_${hydra:runtime.choices.features}_${hydra:runtime.choices.aux_task}_seed_${seed}" + + optimiser.optimizer.lr: + value: ${hparams.hparams.lr} + + decoder.graph_label.dropout: + value: ${hparams.hparams.decoder_dropout} + + #seed: + # values: [13, 42, 99] + +command: + - ${env} + - HYDRA_FULL_ERROR=1 + - WANDB_START_METHOD=thread + - python + - ${program} + - ${args_no_hyphens} diff --git a/proteinworkshop/config/sweeps/baseline_inverse_folding.yaml b/proteinworkshop/config/sweeps/baseline_inverse_folding.yaml index 45a67481..574e09ef 100644 --- a/proteinworkshop/config/sweeps/baseline_inverse_folding.yaml +++ b/proteinworkshop/config/sweeps/baseline_inverse_folding.yaml @@ -7,19 +7,16 @@ metric: # Does not matter, as we are using sweep to run the experiment. parameters: task: - values: [inverse_folding] + value: inverse_folding dataset: - values: [cath] + value: cath encoder: - values: [schnet, dimenet_plus_plus, egnn, gcpnet, gear_net_edge] - - optimiser.optimizer.lr: - values: [0.0001] + values: [schnet, egnn, gcpnet, gear_net_edge, tfn, mace] features: - values: [ca_base, ca_seq, ca_angles, ca_bb] + values: [ca_seq, ca_angles, ca_bb] scheduler: value: plateau @@ -28,14 +25,32 @@ parameters: value: False +aux_task: - values: [none, nn_sequence, nn_structure_torsion, nn_structure_r3] + values: [none, nn_structure_torsion, nn_structure_r3] trainer.max_epochs: - value: 150 + value: 250 test: value: True + trainer: + value: gpu + + logger: + value: wandb + + seed: + values: [13, 42, 121] + + name: + value: "${hydra:runtime.choices.encoder}_${hydra:runtime.choices.features}_${hydra:runtime.choices.aux_task}_seed_${seed}" + + optimiser.optimizer.lr: + value: ${hparams.hparams.lr} + + decoder.residue_type.dropout: + value: ${hparams.hparams.decoder_dropout} + command: - ${env} - HYDRA_FULL_ERROR=1 diff --git a/proteinworkshop/config/sweeps/baseline_ppi.yaml b/proteinworkshop/config/sweeps/baseline_ppi.yaml index f3346d94..8c931ad5 100644 --- a/proteinworkshop/config/sweeps/baseline_ppi.yaml +++ b/proteinworkshop/config/sweeps/baseline_ppi.yaml @@ -9,15 +9,14 @@ parameters: task: values: [ppi_site_prediction] - dataset: values: [masif_site] encoder: - values: [schnet, dimenet_plus_plus, gear_net_edge, egnn, gcpnet] + values: [schnet, gear_net_edge, egnn, gcpnet, tfn, mace] - optimiser.optimizer.lr: - values: [0.0001] + dataset.datamodule.batch_size: + value: 16 features: values: [ca_base, ca_seq, ca_angles, ca_bb, ca_sc] @@ -29,10 +28,25 @@ parameters: value: False +aux_task: - values: [none, nn_sequence, nn_structure_torsion, nn_structure_r3] + values: [none, nn_sequence, nn_structure_r3] trainer.max_epochs: - value: 150 + value: 300 + + test: + value: True + + logger: + value: wandb + + name: + value: "${hydra:runtime.choices.encoder}_${hydra:runtime.choices.features}_${hydra:runtime.choices.aux_task}" + + optimiser.optimizer.lr: + value: ${hparams.hparams.lr} + + decoder.node_label.dropout: + value: ${hparams.hparams.decoder_dropout} command: - ${env} diff --git a/proteinworkshop/config/sweeps/find_hparams.yaml b/proteinworkshop/config/sweeps/find_hparams.yaml new file mode 100644 index 00000000..940bd3cd --- /dev/null +++ b/proteinworkshop/config/sweeps/find_hparams.yaml @@ -0,0 +1,54 @@ +program: proteinworkshop/train.py +method: grid +name: baseline_hyperparameter_search +metric: # Does not matter, as we are using sweep to run the experiment. + goal: minimize + name: val/loss/total + +parameters: + task: + values: [multiclass_graph_classification] + + dataset: + values: [fold_family] + + encoder: + values: [schnet, gear_net_edge, egnn, gcpnet] #, tfn] + + optimiser.optimizer.lr: + values: [0.00001, 0.0001, 0.0003, 0.001] + + decoder.graph_label.dropout: + values: [0.0, 0.1, 0.3, 0.5] + + features: + values: [ca_base, ca_seq, ca_angles, ca_bb, ca_sc] + + scheduler: + value: plateau + + extras.enforce_tags: + value: False + + #+aux_task: + # values: [none, nn_sequence, nn_structure_torsion, nn_structure_r3] + + trainer.max_epochs: + value: 300 + + test: + value: True + + logger: + value: wandb + + name: + value: "${hydra:runtime.choices.encoder}_${hydra:runtime.choices.features}_lr_${optimiser.optimizer.lr}_d_${decoder.graph_label.dropout}" + +command: + - ${env} + - HYDRA_FULL_ERROR=1 + - WANDB_START_METHOD=thread + - python + - ${program} + - ${args_no_hyphens} diff --git a/proteinworkshop/config/sweeps/pre_train_egnn.yaml b/proteinworkshop/config/sweeps/pre_train_egnn.yaml new file mode 100644 index 00000000..3594dbb1 --- /dev/null +++ b/proteinworkshop/config/sweeps/pre_train_egnn.yaml @@ -0,0 +1,55 @@ +program: proteinworkshop/train.py +method: grid +name: pretrain_egnn +metric: # Does not matter, as we are using sweep to run the experiment. + goal: minimize + name: val/loss/total + +parameters: + task: + values: + [ + inverse_folding, + sequence_denoising, + #plddt_prediction, + structure_denoising, + torsional_denoising, + ] + + dataset: + value: afdb_rep_v4 + + dataset.datamodule.num_workers: + value: 16 + + encoder: + values: [egnn] + + optimiser.optimizer.lr: + values: [0.0001] + + features: + values: [ca_angles, ca_bb] + + scheduler: + value: linear_warmup_cosine_decay + + callbacks.model_checkpoint.every_n_epochs: + value: 1 + + extras.enforce_tags: + value: False + + trainer: + value: ddp + + +trainer.max_epochs: + value: 10 + +command: + - ${env} + - HYDRA_FULL_ERROR=1 + - WANDB_START_METHOD=thread + - python + - ${program} + - ${args_no_hyphens} diff --git a/proteinworkshop/config/sweeps/pt_inverse_folding.yaml b/proteinworkshop/config/sweeps/pt_inverse_folding.yaml index b98e0e9d..6db50753 100644 --- a/proteinworkshop/config/sweeps/pt_inverse_folding.yaml +++ b/proteinworkshop/config/sweeps/pt_inverse_folding.yaml @@ -1,6 +1,6 @@ program: proteinworkshop/finetune.py method: grid -name: pre_trained_inverse_folding +name: pre_trained_inverse_folding_scaling metric: # Does not matter, as we are using sweep to run the experiment. goal: minimize name: val/loss/total @@ -13,10 +13,10 @@ parameters: values: [cath] encoder: - values: [schnet, dimenet_plus_plus, egnn, gcpnet, gear_net_edge] + values: [schnet, gcpnet, gear_net_edge, tfn, mace, egnn] optimiser.optimizer.lr: - values: [0.0001] + values: [0.0001, 0.0003, 0.00001, 0.001] features: values: [ca_angles, ca_bb] @@ -28,7 +28,7 @@ parameters: value: False trainer.max_epochs: - value: 150 + value: 250 # We don't want to freeze the encoder finetune.encoder.freeze: @@ -40,18 +40,32 @@ parameters: inverse_folding, ] + +_pt_epoch: + values: ["epoch_0", "epoch_1", "epoch_2", epoch_3", "epoch_4", "epoch_5", "epoch_6", "epoch_7", "epoch_8", "epoch_9"] + + decoder.residue_type.dropout: + value: ${hparams.hparams.decoder_dropout} + # Uses checkpoint_root/pretrain_task/encoder/features/last.ckpt ckpt_path: values: [ # Note: One needed to manually update this path to point to the one's corresponding `last.ckpt` pre-trained checkpoint - #"$USER/ProteinWorkshop/checkpoints/${_pre_train_task}/${hydra:runtime.choices.encoder}/${hydra:runtime.choices.features}/last.ckpt", - ??? + "/home/jamasba/scratch/pworkshop/ckpts/${_pre_train_task}/${hydra:runtime.choices.encoder}/${hydra:runtime.choices.features}/${_pt_epoch}.ckpt", ] +test: value: True + trainer: + value: gpu + + logger: + value: wandb + + name: + value: "${hydra:runtime.choices.encoder}_${hydra:runtime.choices.features}_${hydra:runtime.choices.aux_task}_epoch_${_pt_epoch}" + command: - ${env} - HYDRA_FULL_ERROR=1 diff --git a/proteinworkshop/config/task/binary_graph_classification.yaml b/proteinworkshop/config/task/binary_graph_classification.yaml new file mode 100644 index 00000000..3cda0b53 --- /dev/null +++ b/proteinworkshop/config/task/binary_graph_classification.yaml @@ -0,0 +1,34 @@ +# @package _global_ + +defaults: + - _self_ + - override /metrics: + - accuracy + - f1_score + - f1_max + - auprc + - rocauc + - override /decoder: + - graph_label + +callbacks: + early_stopping: + monitor: val/graph_label/auprc + mode: "max" + model_checkpoint: + monitor: val/graph_label/auprc + mode: "max" + +task: + task: "classification" + classification_type: "binary" + metric_average: "micro" + + losses: + graph_label: bce + label_smoothing: 0.0 + + output: + - "graph_label" + supervise_on: + - "graph_label" diff --git a/proteinworkshop/config/task/multilabel_graph_classification.yaml b/proteinworkshop/config/task/multilabel_graph_classification.yaml new file mode 100644 index 00000000..7f47d180 --- /dev/null +++ b/proteinworkshop/config/task/multilabel_graph_classification.yaml @@ -0,0 +1,45 @@ +# @package _global_ + +defaults: + - override /metrics: + - accuracy + - f1_score + - f1_max + - rocauc + - auprc + - override /decoder: + - graph_label + - override /transforms: + - remove_missing_ca + - multihot_label_encoding + +metrics: + accuracy: + num_labels: ${dataset.num_classes} + f1_score: + num_labels: ${dataset.num_classes} + rocauc: + num_labels: ${dataset.num_classes} + + +callbacks: + early_stopping: + monitor: val/graph_label/accuracy + mode: "max" + model_checkpoint: + monitor: val/graph_label/accuracy + mode: "max" + +task: + task: "classification" + classification_type: "multilabel" + metric_average: "micro" + + losses: + graph_label: bce + label_smoothing: 0.0 + + output: + - "graph_label" + supervise_on: + - "graph_label" diff --git a/proteinworkshop/config/task/multilabel_node_classification.yaml b/proteinworkshop/config/task/multilabel_node_classification.yaml new file mode 100644 index 00000000..75872cad --- /dev/null +++ b/proteinworkshop/config/task/multilabel_node_classification.yaml @@ -0,0 +1,42 @@ +# @package _global_ + +defaults: + - override /metrics: + - accuracy + - f1_score + - f1_max + - auprc + - rocauc + - override /decoder: + - node_label + +callbacks: + early_stopping: + monitor: val/node_label/accuracy + mode: "max" + model_checkpoint: + monitor: val/node_label/accuracy + mode: "max" + +metrics: + accuracy: + num_labels: ${dataset.num_classes} + f1_score: + num_labels: ${dataset.num_classes} + rocauc: + num_labels: ${dataset.num_classes} + + +task: + task: "classification" + classification_type: "multilabel" + metric_average: "micro" + + losses: + node_label: bce + label_smoothing: 0.0 + + output: + - "node_label" + supervise_on: + - "node_label" diff --git a/proteinworkshop/config/task/ppi_site_prediction.yaml b/proteinworkshop/config/task/ppi_site_prediction.yaml index 7ad91343..6ce75afa 100644 --- a/proteinworkshop/config/task/ppi_site_prediction.yaml +++ b/proteinworkshop/config/task/ppi_site_prediction.yaml @@ -1,6 +1,7 @@ # @package _global_ defaults: + - _self_ - override /metrics: - accuracy - f1_score @@ -27,7 +28,7 @@ callbacks: task: task: "classification" classification_type: "binary" - metric_average: "macro" + metric_average: "micro" losses: node_label: bce diff --git a/proteinworkshop/config/train.yaml b/proteinworkshop/config/train.yaml index 37025489..84979a23 100644 --- a/proteinworkshop/config/train.yaml +++ b/proteinworkshop/config/train.yaml @@ -24,6 +24,7 @@ defaults: - logger: csv # Also supported: tensorboard, wandb # debugging config (enable through command line, e.g. `python train.py debug=default) - debug: null + - optional hparams: ${encoder}_${features} - _self_ # see: https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order/. Adding _self_ at bottom means values in this file override defaults. task_name: "train" diff --git a/proteinworkshop/config/trainer/ddp.yaml b/proteinworkshop/config/trainer/ddp.yaml index 4e5238e3..3626f923 100644 --- a/proteinworkshop/config/trainer/ddp.yaml +++ b/proteinworkshop/config/trainer/ddp.yaml @@ -1,11 +1,9 @@ defaults: - default.yaml -# use "ddp_spawn" instead of "ddp", -# it's slower but normal "ddp" currently doesn't work ideally with hydra -# https://github.com/facebookresearch/hydra/issues/2070 -# https://pytorch-lightning.readthedocs.io/en/latest/accelerators/gpu_intermediate.html#distributed-data-parallel-spawn -strategy: ddp_spawn +# NOTE: https://github.com/facebookresearch/hydra/issues/2070 +# for improved Hydra + Lightning + DDP support is not merged yet +strategy: ddp_find_unused_parameters_true accelerator: gpu devices: 4 diff --git a/proteinworkshop/config/transforms/multihot_label_encoding.yaml b/proteinworkshop/config/transforms/multihot_label_encoding.yaml new file mode 100644 index 00000000..aa04ee84 --- /dev/null +++ b/proteinworkshop/config/transforms/multihot_label_encoding.yaml @@ -0,0 +1,3 @@ +multihot_label_encoding: + _target_: proteinworkshop.tasks.multihot_label_encoding.MultiHotLabelEncoding + num_classes : ${dataset.num_classes} diff --git a/proteinworkshop/configs/config.py b/proteinworkshop/configs/config.py index 716f2e42..1f60c8fc 100644 --- a/proteinworkshop/configs/config.py +++ b/proteinworkshop/configs/config.py @@ -244,19 +244,21 @@ def validate_cuda(cfg: DictConfig) -> DictConfig: # Make sure cuda config is correct # if cfg.trainer.devices <= 1: # cfg.trainer.strategy = None - - logger.debug(f"Requested GPUs: {cfg.get('trainer.devices')}.") - if isinstance(cfg.get("trainer.devices"), int): + logger.debug(f"CUDA available: {torch.cuda.is_available()}") + logger.debug(f"Requested GPUs: {cfg.trainer.get('devices')}.") + if isinstance(cfg.trainer.get("devices"), int): cfg.trainer.devices = min( torch.cuda.device_count(), cfg.trainer.devices ) logger.debug(f"GPU count set to: {cfg.trainer.devices}") requesting_multiple_device_indices = ( - isinstance(cfg.get("trainer.devices"), list) and len(cfg.get("trainer.devices")) > 1 + isinstance(cfg.trainer.get("devices"), list) + and len(cfg.trainer.get("devices")) > 1 ) requesting_multiple_devices = ( - isinstance(cfg.get("trainer.devices"), int) and cfg.get("trainer.devices") > 1 + isinstance(cfg.trainer.get("devices"), int) + and cfg.trainer.get("devices") > 1 ) if ( requesting_multiple_device_indices or requesting_multiple_devices diff --git a/proteinworkshop/datasets/antibody_developability.py b/proteinworkshop/datasets/antibody_developability.py index e5ab4e0f..b4caa94d 100644 --- a/proteinworkshop/datasets/antibody_developability.py +++ b/proteinworkshop/datasets/antibody_developability.py @@ -227,3 +227,29 @@ def test_dataloader(self) -> ProteinDataLoader: pin_memory=self.pin_memory, shuffle=False, ) + +if __name__ == "__main__": + import pathlib + + import hydra + + from proteinworkshop import constants + + cfg = omegaconf.OmegaConf.load( + constants.SRC_PATH / "config" / "dataset" / "antibody_developability.yaml" + ) + cfg.datamodule.path = pathlib.Path(constants.DATA_PATH) / "AntibodyDevelopability" # type: ignore + cfg.datamodule.pdb_dir = pathlib.Path(constants.DATA_PATH) / "pdb" # type: ignore + cfg.datamodule.transforms = [] + ds = hydra.utils.instantiate(cfg) + print(ds) + ds["datamodule"].parse_dataset() + dl = ds["datamodule"].train_dataloader() + for batch in dl: + print(batch) + dl = ds["datamodule"].val_dataloader() + for batch in dl: + print(batch) + dl = ds["datamodule"].test_dataloader() + for batch in dl: + print(batch) diff --git a/proteinworkshop/datasets/atom3d_datamodule.py b/proteinworkshop/datasets/atom3d_datamodule.py index 71133dee..3fe19eeb 100644 --- a/proteinworkshop/datasets/atom3d_datamodule.py +++ b/proteinworkshop/datasets/atom3d_datamodule.py @@ -11,7 +11,7 @@ import atom3d.datasets.datasets as da import lightning as L import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.dataloader import ProteinDataLoader from loguru import logger as log from torch.utils.data import Dataset @@ -34,7 +34,7 @@ def set_worker_sharing_strategy(worker_id: int): torch.multiprocessing.set_sharing_strategy(SHARING_STRATEGY) -@beartype +@typechecker def get_data_path( dataset: str, lba_split: int = 30, @@ -57,7 +57,7 @@ def get_data_path( return data_paths[dataset] -@beartype +@typechecker def get_test_data_path( dataset: str, lba_split: int = 30, @@ -71,7 +71,7 @@ def get_test_data_path( # default to testing PPI methods with DB5 "PPI": f"PPI/splits/{ppi_split}/data/{test_phase}" if use_dips_for_testing - else f"PPI/raw/DB5/data/", + else "PPI/raw/DB5/data/", "RES": f"RES/splits/split-by-cath-topology/data/{test_phase}", "MSP": f"MSP/splits/split-by-sequence-identity-30/data/{test_phase}", } @@ -85,7 +85,7 @@ def get_test_data_path( return data_paths[dataset] -@beartype +@typechecker def get_task_split( task: str, lba_split: int = 30, @@ -317,7 +317,7 @@ def setup(self, stage: Optional[str] = None): self.data_test, ) = self.get_datasets() - @beartype + @typechecker def get_dataloader( self, dataset: Union[da.LMDBDataset, PPIDataset, RESDataset], diff --git a/proteinworkshop/datasets/base.py b/proteinworkshop/datasets/base.py index 389db51e..1f58dbfa 100644 --- a/proteinworkshop/datasets/base.py +++ b/proteinworkshop/datasets/base.py @@ -20,7 +20,7 @@ import numpy as np import pandas as pd import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein import verbose from graphein.protein.tensor.dataloader import ProteinDataLoader from graphein.protein.tensor.io import protein_to_pyg @@ -99,7 +99,7 @@ def obsolete_pdbs(self) -> Dict[str, str]: """ return get_obsolete_mapping() - @beartype + @typechecker def compose_transforms(self, transforms: Iterable[Callable]) -> T.Compose: """Compose an iterable of Transforms into a single transform. @@ -305,6 +305,8 @@ def __init__( self.store_het = store_het self.out_names = out_names + self._processed_files = [] + # Determine whether to download raw structures if not self.overwrite and all( os.path.exists(Path(self.root) / "processed" / p) @@ -414,6 +416,8 @@ def processed_file_names(self) -> Union[str, List[str], Tuple]: :return: List of processed file names. :rtype: Union[str, List[str], Tuple] """ + if self._processed_files: + return self._processed_files if self.overwrite: return ["this_forces_a_processing_cycle"] if self.out_names is not None: @@ -448,14 +452,21 @@ def process(self): Path(self.processed_dir) / f"{pdb}.pt" ) ] - logger.info(f"Processing {len(index_pdb_tuples)} unprocessed structures") + logger.info( + f"Processing {len(index_pdb_tuples)} unprocessed structures" + ) else: - index_pdb_tuples = [(i, pdb) for i, pdb in enumerate(self.pdb_codes)] + index_pdb_tuples = [ + (i, pdb) for i, pdb in enumerate(self.pdb_codes) + ] raw_dir = Path(self.raw_dir) for index_pdb_tuple in tqdm(index_pdb_tuples): try: - i, pdb = index_pdb_tuple # NOTE: here, we unpack the tuple to get each PDB's original index in `self.pdb_codes` + ( + i, + pdb, + ) = index_pdb_tuple # NOTE: here, we unpack the tuple to get each PDB's original index in `self.pdb_codes` path = raw_dir / f"{pdb}.{self.format}" if path.exists(): path = str(path) @@ -474,9 +485,7 @@ def process(self): store_het=self.store_het, ) except Exception as e: - logger.error( - f"Error processing {pdb} {self.chains[i]}: {e}" - ) # type: ignore + logger.error(f"Error processing {pdb} {self.chains[i]}: {e}") # type: ignore raise e if self.out_names is not None: @@ -497,6 +506,7 @@ def process(self): graph.node_y = self.node_labels[i] # type: ignore torch.save(graph, Path(self.processed_dir) / fname) + self._processed_files.append(fname) logger.info("Completed processing.") def get(self, idx: int) -> Data: @@ -523,4 +533,7 @@ def _batch_format(self, x: Data) -> Data: # Set this to ensure proper batching behaviour x.x = torch.zeros(x.coords.shape[0]) # type: ignore x.amino_acid_one_hot = amino_acid_one_hot(x) + x.seq_pos = torch.arange(x.coords.shape[0]).unsqueeze( + -1 + ) # Add sequence position return x diff --git a/proteinworkshop/datasets/cath.py b/proteinworkshop/datasets/cath.py index d6277650..312721e7 100644 --- a/proteinworkshop/datasets/cath.py +++ b/proteinworkshop/datasets/cath.py @@ -14,6 +14,31 @@ class CATHDataModule(ProteinDataModule): + """Data module for CATH dataset. + + :param path: Path to store data. + :type path: str + :param batch_size: Batch size for dataloaders. + :type batch_size: int + :param format: Format to load PDB files in. + :type format: Literal["mmtf", "pdb"] + :param pdb_dir: Path to directory containing PDB files. + :type pdb_dir: str + :param pin_memory: Whether to pin memory for dataloaders. + :type pin_memory: bool + :param in_memory: Whether to load the entire dataset into memory. + :type in_memory: bool + :param num_workers: Number of workers for dataloaders. + :type num_workers: int + :param dataset_fraction: Fraction of dataset to use. + :type dataset_fraction: float + :param transforms: List of transforms to apply to dataset. + :type transforms: Optional[List[Callable]] + :param overwrite: Whether to overwrite existing data. + Defaults to ``False``. + :type overwrite: bool + """ + def __init__( self, path: str, @@ -27,30 +52,6 @@ def __init__( transforms: Optional[Iterable[Callable]] = None, overwrite: bool = False, ) -> None: - """Data module for CATH dataset. - - :param path: Path to store data. - :type path: str - :param batch_size: Batch size for dataloaders. - :type batch_size: int - :param format: Format to load PDB files in. - :type format: Literal["mmtf", "pdb"] - :param pdb_dir: Path to directory containing PDB files. - :type pdb_dir: str - :param pin_memory: Whether to pin memory for dataloaders. - :type pin_memory: bool - :param in_memory: Whether to load the entire dataset into memory. - :type in_memory: bool - :param num_workers: Number of workers for dataloaders. - :type num_workers: int - :param dataset_fraction: Fraction of dataset to use. - :type dataset_fraction: float - :param transforms: List of transforms to apply to dataset. - :type transforms: Optional[List[Callable]] - :param overwrite: Whether to overwrite existing data. - Defaults to ``False``. - :type overwrite: bool - """ super().__init__() self.data_dir = Path(path) @@ -270,7 +271,6 @@ def test_dataloader(self) -> ProteinDataLoader: import pathlib import hydra - import omegaconf from proteinworkshop import constants diff --git a/proteinworkshop/datasets/cc_pdb.py b/proteinworkshop/datasets/cc_pdb.py index 9ffd4776..f1cea7df 100644 --- a/proteinworkshop/datasets/cc_pdb.py +++ b/proteinworkshop/datasets/cc_pdb.py @@ -17,6 +17,46 @@ class CCPDBDataModule(ProteinDataModule): + """Data module for CCPDB datasets. + + :param path: Path to store data. + :type path: str + :param pdb_dir: Path to directory containing structure files. + :type pdb_dir: str + :param name: Name of dataset to use. + :type name: CCPDB_DATASET_NAMES + :param batch_size: Batch size for dataloaders. + :type batch_size: int + :param num_workers: Number of workers for dataloaders. + :type num_workers: int + :param pin_memory: Whether to pin memory for dataloaders. + :type pin_memory: bool + :param in_memory: Whether to load dataset into memory, defaults to + ``False`` + :type in_memory: bool, optional + :param format: Format of the structure files, defaults to ``"mmtf"``. + :type format: Literal[mmtf, pdb], optional + :param obsolete_strategy: How to deal with obsolete PDBs, + defaults to "drop" + :type obsolete_strategy: str, optional + :param split_strategy: How to split the data, + defaults to ``"random"`` + :type split_strategy: Literal["random", 'stratified"], optional + :param val_fraction: Fraction of the dataset to use for validation, + defaults to ``0.1`` + :type val_fraction: float, optional + :param test_fraction: Fraction of the dataset to use for testing, + defaults to ``0.1``. + :type test_fraction: float, optional + :param transforms: List of transforms to apply to each example, + defaults to ``None``. + :type transforms: Optional[List[Callable]], optional + :param overwrite: Whether to overwrite existing data, defaults to + ``False`` + :type overwrite: bool, optional + :raises ValueError: If train, val, and test fractions do not sum to 1. + """ + def __init__( self, path: str, @@ -35,45 +75,6 @@ def __init__( transforms: Optional[List[Callable]] = None, overwrite: bool = False, ): - """Data module for CCPDB datasets. - - :param path: Path to store data. - :type path: str - :param pdb_dir: Path to directory containing structure files. - :type pdb_dir: str - :param name: Name of dataset to use. - :type name: CCPDB_DATASET_NAMES - :param batch_size: Batch size for dataloaders. - :type batch_size: int - :param num_workers: Number of workers for dataloaders. - :type num_workers: int - :param pin_memory: Whether to pin memory for dataloaders. - :type pin_memory: bool - :param in_memory: Whether to load dataset into memory, defaults to - ``False`` - :type in_memory: bool, optional - :param format: Format of the structure files, defaults to ``"mmtf"``. - :type format: Literal[mmtf, pdb], optional - :param obsolete_strategy: How to deal with obsolete PDBs, - defaults to "drop" - :type obsolete_strategy: str, optional - :param split_strategy: How to split the data, - defaults to ``"random"`` - :type split_strategy: Literal["random", 'stratified"], optional - :param val_fraction: Fraction of the dataset to use for validation, - defaults to ``0.1`` - :type val_fraction: float, optional - :param test_fraction: Fraction of the dataset to use for testing, - defaults to ``0.1``. - :type test_fraction: float, optional - :param transforms: List of transforms to apply to each example, - defaults to ``None``. - :type transforms: Optional[List[Callable]], optional - :param overwrite: Whether to overwrite existing data, defaults to - ``False`` - :type overwrite: bool, optional - :raises ValueError: If train, val, and test fractions do not sum to 1. - """ super().__init__() self.root = pathlib.Path(path) if not os.path.exists(self.root): @@ -264,7 +265,7 @@ def test_dataloader(self) -> ProteinDataLoader: num_workers = 4 pin_memory = True - dataset = CCPDBDataset( + dataset = CCPDBDataModule( path, pdb_dir, name, batch_size, num_workers, pin_memory ) dataset.download() diff --git a/proteinworkshop/datasets/components/atom3d_dataset.py b/proteinworkshop/datasets/components/atom3d_dataset.py index e2a7d27a..57c7cdc1 100644 --- a/proteinworkshop/datasets/components/atom3d_dataset.py +++ b/proteinworkshop/datasets/components/atom3d_dataset.py @@ -7,7 +7,7 @@ import numpy as np import pandas as pd import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import Protein from graphein.protein.tensor.io import protein_to_pyg from loguru import logger as log @@ -28,7 +28,7 @@ NUM_ATOM_TYPES = len(_atom_types_dict) -@beartype +@typechecker def _element_mapping(x: str) -> int: return _atom_types_dict.get(x, 8) diff --git a/proteinworkshop/datasets/components/ppi_dataset.py b/proteinworkshop/datasets/components/ppi_dataset.py index bc3985be..431dc4e2 100644 --- a/proteinworkshop/datasets/components/ppi_dataset.py +++ b/proteinworkshop/datasets/components/ppi_dataset.py @@ -7,7 +7,7 @@ import scipy import torch from atom3d.datasets import LMDBDataset -from beartype import beartype +from beartype import beartype as typechecker from torch.utils.data import IterableDataset from torch_geometric.data import Data @@ -23,14 +23,14 @@ ] -@beartype +@typechecker def get_res(df: pd.DataFrame) -> pd.DataFrame: """Get all residues.""" # Adapted from: https://github.com/drorlab/atom3d/blob/master/examples/ppi/dataset/neighbors.py return df[PPI_DF_INDEX_COLUMNS].drop_duplicates() -@beartype +@typechecker def _get_idx_to_res_mapping( df: pd.DataFrame, ) -> Tuple[pd.DataFrame, pd.Series]: @@ -43,7 +43,7 @@ def _get_idx_to_res_mapping( return idx_to_res, res_to_idx -@beartype +@typechecker def get_subunits( ensemble: pd.DataFrame, ) -> Tuple[ @@ -79,7 +79,7 @@ def get_subunits( return names, (bdf0, bdf1, udf0, udf1) -@beartype +@typechecker def get_negatives( neighbors, df0: pd.DataFrame, df1: pd.DataFrame ) -> pd.DataFrame: @@ -170,7 +170,7 @@ def __iter__(self): ) return gen - @beartype + @typechecker def _df_to_graph( self, struct_df: pd.DataFrame, chain_res: Iterable, label: float ) -> Optional[Data]: @@ -208,7 +208,7 @@ def _df_to_graph( return data - @beartype + @typechecker def _dataset_generator( self, indices: List[int], shuffle: bool = True ) -> Generator[Tuple[Data, Data], None, None]: @@ -247,7 +247,7 @@ def _dataset_generator( continue yield graph1, graph2 - @beartype + @typechecker def _create_labels( self, positives: pd.DataFrame, diff --git a/proteinworkshop/datasets/components/res_dataset.py b/proteinworkshop/datasets/components/res_dataset.py index 91daf682..638dbaf7 100644 --- a/proteinworkshop/datasets/components/res_dataset.py +++ b/proteinworkshop/datasets/components/res_dataset.py @@ -8,7 +8,7 @@ from proteinworkshop.datasets.components.atom3d_dataset import BaseTransform -_amino_acids = lambda x: { +_amino_acids = lambda x: { # noqa: E731 "ALA": 0, "ARG": 1, "ASN": 2, diff --git a/proteinworkshop/datasets/deep_sea_proteins.py b/proteinworkshop/datasets/deep_sea_proteins.py index 365b604d..d0cfef51 100644 --- a/proteinworkshop/datasets/deep_sea_proteins.py +++ b/proteinworkshop/datasets/deep_sea_proteins.py @@ -17,6 +17,34 @@ class DeepSeaProteinsDataModule(ProteinDataModule): + """Data module for Deep Sea Proteins dataset. + + :param path: Path to store data. + :type path: os.PathLike + :param pdb_dir: Path to directory containing PDB files. + :type pdb_dir: os.PathLike + :param validation_fold: Name of validation fold to use. + :type validation_fold: int + :param batch_size: Batch size for dataloaders. + :type batch_size: int + :param in_memory: Whether to load the entire dataset into memory, defaults to False + :type in_memory: bool, optional + :param pin_memory: Whether to pin dataloader memory, defaults to True + :type pin_memory: bool, optional + :param num_workers: Number of dataloader workers, defaults to 16 + :type num_workers: int, optional + :param obsolete_strategy: Strategy to deal with obsolete PDbs, + defaults to "drop" + :type obsolete_strategy: str, optional + :param format: Format of the structure files, defaults to "mmtf" + :type format: Literal[mmtf, pdb], optional + :param transforms: Transforms to apply, defaults to None + :type transforms: Optional[Iterable[Callable]], optional + :param overwrite: Whether to overwrite existing data, defaults to + ``False`` + :type overwrite: bool, optional + """ + def __init__( self, path: os.PathLike, @@ -31,33 +59,6 @@ def __init__( transforms: Optional[Iterable[Callable]] = None, overwrite: bool = False, ): - """Data module for Deep Sea Proteins dataset. - - :param path: Path to store data. - :type path: os.PathLike - :param pdb_dir: Path to directory containing PDB files. - :type pdb_dir: os.PathLike - :param validation_fold: Name of validation fold to use. - :type validation_fold: int - :param batch_size: Batch size for dataloaders. - :type batch_size: int - :param in_memory: Whether to load the entire dataset into memory, defaults to False - :type in_memory: bool, optional - :param pin_memory: Whether to pin dataloader memory, defaults to True - :type pin_memory: bool, optional - :param num_workers: Number of dataloader workers, defaults to 16 - :type num_workers: int, optional - :param obsolete_strategy: Strategy to deal with obsolete PDbs, - defaults to "drop" - :type obsolete_strategy: str, optional - :param format: Format of the structure files, defaults to "mmtf" - :type format: Literal[mmtf, pdb], optional - :param transforms: Transforms to apply, defaults to None - :type transforms: Optional[Iterable[Callable]], optional - :param overwrite: Whether to overwrite existing data, defaults to - ``False`` - :type overwrite: bool, optional - """ super().__init__() self.data_dir = pathlib.Path(path) if not os.path.exists(self.data_dir): @@ -256,7 +257,6 @@ def test_dataloader(self) -> ProteinDataLoader: if __name__ == "__main__": import hydra - import omegaconf from proteinworkshop import constants diff --git a/proteinworkshop/datasets/ec_reaction.py b/proteinworkshop/datasets/ec_reaction.py index cbb8694b..c4cd2e6e 100644 --- a/proteinworkshop/datasets/ec_reaction.py +++ b/proteinworkshop/datasets/ec_reaction.py @@ -8,7 +8,6 @@ import wget from graphein.protein.tensor.dataloader import ProteinDataLoader from loguru import logger -from torch_geometric.data import Dataset from proteinworkshop.datasets.base import ProteinDataModule, ProteinDataset @@ -93,7 +92,9 @@ def parse_labels(self) -> Dict[str, str]: ) return dict(class_map.values) - def _get_dataset(self, split: str) -> Dataset: + def _get_dataset( + self, split: Literal["training", "validation", "testing"] + ) -> ProteinDataset: df = self.parse_dataset(split) return ProteinDataset( root=str(self.data_dir), @@ -107,20 +108,18 @@ def _get_dataset(self, split: str) -> Dataset: in_memory=self.in_memory, ) - def train_dataset(self) -> Dataset: + def train_dataset(self) -> ProteinDataset: return self._get_dataset("training") - def val_dataset(self) -> Dataset: + def val_dataset(self) -> ProteinDataset: return self._get_dataset("validation") - def test_dataset(self) -> Dataset: + def test_dataset(self) -> ProteinDataset: return self._get_dataset("testing") def train_dataloader(self) -> ProteinDataLoader: - if not hasattr(self, "train_ds"): - self.train_ds = self.train_dataset() return ProteinDataLoader( - self.train_ds, + self.train_dataset(), batch_size=self.batch_size, shuffle=True, pin_memory=self.pin_memory, @@ -128,10 +127,8 @@ def train_dataloader(self) -> ProteinDataLoader: ) def val_dataloader(self) -> ProteinDataLoader: - if not hasattr(self, "val_ds"): - self.val_ds = self.val_dataset() return ProteinDataLoader( - self.val_ds, + self.val_dataset(), batch_size=self.batch_size, shuffle=False, pin_memory=self.pin_memory, @@ -139,10 +136,8 @@ def val_dataloader(self) -> ProteinDataLoader: ) def test_dataloader(self) -> ProteinDataLoader: - if not hasattr(self, "test_ds"): - self.test_ds = self.test_dataset() return ProteinDataLoader( - self.test_ds, + self.test_dataset(), batch_size=self.batch_size, shuffle=False, pin_memory=self.pin_memory, @@ -206,18 +201,20 @@ def parse_dataset(self, split: str) -> pd.DataFrame: import pathlib import hydra - import omegaconf from proteinworkshop import constants cfg = omegaconf.OmegaConf.load( - constants.SRC_PATH / "config" / "dataset" / "ec.yaml" + constants.SRC_PATH / "config" / "dataset" / "ec_reaction.yaml" ) - cfg.datamodule.path = pathlib.Path(constants.DATA_PATH) / "EnzymeCommission" # type: ignore + cfg.datamodule.path = pathlib.Path(constants.DATA_PATH) / "ECReaction" # type: ignore cfg.datamodule.pdb_dir = pathlib.Path(constants.DATA_PATH) / "pdb" # type: ignore + cfg.datamodule.transforms = [] ds = hydra.utils.instantiate(cfg) print(ds) dl = ds["datamodule"].val_dataloader() - print(ds["datamodule"].val_ds[1]) + for batch in dl: + print(batch) + dl = ds["datamodule"].test_dataloader() for batch in dl: print(batch) diff --git a/proteinworkshop/datasets/fold_classification.py b/proteinworkshop/datasets/fold_classification.py index 5f5d623e..403da603 100644 --- a/proteinworkshop/datasets/fold_classification.py +++ b/proteinworkshop/datasets/fold_classification.py @@ -265,7 +265,6 @@ def exclude_pdbs(self): if __name__ == "__main__": import hydra - import omegaconf from proteinworkshop import constants diff --git a/proteinworkshop/datasets/go.py b/proteinworkshop/datasets/go.py index f1a40fd3..43004b48 100644 --- a/proteinworkshop/datasets/go.py +++ b/proteinworkshop/datasets/go.py @@ -8,10 +8,10 @@ import pandas as pd import torch import wget +from graphein.protein.tensor.data import Protein +from graphein.protein.tensor.dataloader import ProteinDataLoader from loguru import logger as log from sklearn.preprocessing import LabelEncoder -from torch_geometric.data import Dataset -from torch_geometric.loader import DataLoader from tqdm import tqdm from proteinworkshop.datasets.base import ProteinDataModule, ProteinDataset @@ -129,7 +129,9 @@ def parse_labels(self) -> Dict[str, torch.Tensor]: log.info(f"Encoded {len(labels)} labels for task {self.split}.") return labels - def _get_dataset(self, split: str) -> Dataset: + def _get_dataset( + self, split: Literal["training", "validation", "testing"] + ) -> ProteinDataset: df = self.parse_dataset(split) log.info("Initialising Graphein dataset...") return ProteinDataset( @@ -139,47 +141,43 @@ def _get_dataset(self, split: str) -> Dataset: chains=list(df.chain), graph_labels=list(list(df.label)), overwrite=self.overwrite, - transform=self.transform, + transform=self.labeller + if self.transform is None + else self.compose_transforms([self.labeller] + [self.transform]), format=self.format, in_memory=self.in_memory, ) - def train_dataset(self) -> Dataset: + def train_dataset(self) -> ProteinDataset: return self._get_dataset("training") - def val_dataset(self) -> Dataset: + def val_dataset(self) -> ProteinDataset: return self._get_dataset("validation") - def test_dataset(self) -> Dataset: + def test_dataset(self) -> ProteinDataset: return self._get_dataset("testing") - def train_dataloader(self) -> DataLoader: - if not hasattr(self, "train_ds"): - self.train_ds = self.train_dataset() - return DataLoader( - self.train_ds, + def train_dataloader(self) -> ProteinDataLoader: + return ProteinDataLoader( + self.train_dataset(), batch_size=self.batch_size, shuffle=True, pin_memory=self.pin_memory, num_workers=self.num_workers, ) - def val_dataloader(self) -> DataLoader: - if not hasattr(self, "val_ds"): - self.val_ds = self.val_dataset() - return DataLoader( - self.val_ds, + def val_dataloader(self) -> ProteinDataLoader: + return ProteinDataLoader( + self.val_dataset(), batch_size=self.batch_size, shuffle=False, pin_memory=self.pin_memory, num_workers=self.num_workers, ) - def test_dataloader(self) -> DataLoader: - if not hasattr(self, "test_ds"): - self.test_ds = self.test_dataset() - return DataLoader( - self.test_ds, + def test_dataloader(self) -> ProteinDataLoader: + return ProteinDataLoader( + self.test_dataset(), batch_size=self.batch_size, shuffle=False, pin_memory=self.pin_memory, @@ -206,7 +204,9 @@ def download(self): def exclude_pdbs(self): pass - def parse_dataset(self, split: str) -> pd.DataFrame: + def parse_dataset( + self, split: Literal["training", "validation", "testing"] + ) -> pd.DataFrame: # sourcery skip: remove-unnecessary-else, swap-if-else-branches, switch """ Parses the raw dataset files to Pandas DataFrames. @@ -261,20 +261,40 @@ def parse_dataset(self, split: str) -> pd.DataFrame: data["label"] = data["label"].sample(frac=1).values # logger.info(f"Found {len(data)} examples in {split} after removing nonstandard proteins") + self.labeller = GOLabeller(data) return data.sample(frac=1) # Shuffle dataset for batches +class GOLabeller: + """ + This labeller applies the graph labels to each example as a transform. + + This is required as chains can be used across tasks (e.g. CC, BP or MF) with + different labels. + """ + + def __init__(self, label_df: pd.DataFrame): + self.labels = label_df + + def __call__(self, data: Protein) -> Protein: + pdb, chain = data.id.split("_") + label = self.labels.loc[ + (self.labels.pdb == pdb) & (self.labels.chain == chain) + ].label.item() + data.graph_y = label + return data + + if __name__ == "__main__": import pathlib import hydra - import omegaconf from proteinworkshop import constants log.info("Imported libs") cfg = omegaconf.OmegaConf.load( - constants.SRC_PATH / "config" / "dataset" / "go-cc.yaml" + constants.SRC_PATH / "config" / "dataset" / "go-bp.yaml" ) # cfg = omegaconf.OmegaConf.load(constants.SRC_PATH / "config" / "dataset" / "go-mf.yaml") # cfg = omegaconf.OmegaConf.load(constants.SRC_PATH / "config" / "dataset" / "go-bp.yaml") @@ -286,8 +306,14 @@ def parse_dataset(self, split: str) -> pd.DataFrame: ds = hydra.utils.instantiate(cfg) print(ds) + # labels = ds["datamodule"].parse_labels() ds.datamodule.setup() + dl = ds["datamodule"].train_dataloader() + for batch in dl: + print(batch) dl = ds["datamodule"].val_dataloader() - print(ds["datamodule"].val_ds[1]) + for batch in dl: + print(batch) + dl = ds["datamodule"].test_dataloader() for batch in dl: print(batch) diff --git a/proteinworkshop/datasets/igfold.py b/proteinworkshop/datasets/igfold.py new file mode 100644 index 00000000..aafde620 --- /dev/null +++ b/proteinworkshop/datasets/igfold.py @@ -0,0 +1,302 @@ +"""DataModule for IgFold antibody prediction corpuses.""" +import glob +import os +import pathlib +import random +import tarfile +from typing import Callable, List, Literal, Optional + +import omegaconf +import wget +from graphein.protein.tensor.dataloader import ProteinDataLoader +from loguru import logger +from sklearn.model_selection import train_test_split + +from proteinworkshop.datasets.base import ProteinDataModule, ProteinDataset + +IgFoldDatasetNames = Literal["paired_oas", "jaffe"] + + +class IgFoldDataModule(ProteinDataModule): + """ + Datamodule for IgFold predictions for pOAS and Jaffe Antibody Datasets. + + :param path: Path to store data + :type path: os.PathLike + :param dataset_name: Name of dataset to use (``"paired_oas"`` or + ``"jaffe"``). + :type dataset_name: IgFoldDatasetNames + :param batch_size: Batch size for dataloader. + :type batch_size: int + :param train_val_test: Split sizes for dataset partitions. + :type train_val_test: List[float] + :param format: Format of the structure files. + :type format: Literal["pdb", "mmtf"] + :param in_memory: Whether to load the entire dataset into memory. + :type in_memory: bool + :param pin_memory: Whether to pin memory for the dataloader. + :type pin_memory: bool + :param num_workers: Number of processes to use for the dataloader. + :type num_workers: int + :param dataset_fraction: Fraction of the dataset to use. Defaults to + ``1.0``. + :type dataset_fraction: float + :param transforms: List of transforms to apply to the data. + :type transforms: Optional[List[Callable]] + """ + + def __init__( + self, + path: os.PathLike, + dataset_name: IgFoldDatasetNames, + batch_size: int, + train_val_test: List[float] = [0.8, 0.1, 0.1], + format: Literal["pdb"] = "pdb", + in_memory: bool = False, + pin_memory: bool = True, + num_workers: int = 16, + dataset_fraction: float = 1.0, + transforms: Optional[List[Callable]] = None, + ): + if dataset_name == "paired_oas": + self.DATASET_URL = "https://data.graylab.jhu.edu/OAS_paired.tar.gz" + self.TAR_FILENAME = "OAS_paired.tar.gz" + elif dataset_name == "jaffe": + self.DATASET_URL = "https://data.graylab.jhu.edu/Jaffe2022.tar.gz" + self.TAR_FILENAME = "Jaffe2022.tar.gz" + else: + raise ValueError( + f"Dataset {dataset_name} not supported. Must be one of 'jaffe' / 'paired_oas" + ) + self.UNCOMPRESSED_DIR_NAME = "predictions_flat" + # Dataset args + assert sum(train_val_test) == 1, "Split sizes are invalid" + self.train_val_test = train_val_test + self.in_memory = in_memory + self.format = format + self.dataset_fraction = dataset_fraction + + # Paths + self.data_dir: pathlib.Path = pathlib.Path(path) + if not os.path.exists(self.data_dir): + os.makedirs(self.data_dir, exist_ok=True) + self.structure_dir = self.data_dir / "structures" + + # Dataloader args + self.batch_size = batch_size + self.pin_memory = pin_memory + self.num_workers = num_workers + + if transforms is not None: + self.transform = self.compose_transforms( + omegaconf.OmegaConf.to_container( + transforms, resolve=True + ) # type: ignore + ) + else: + self.transform = None + + def setup(self, stage: Optional[str] = None): + """Perform datamodule setup tasks (downloads structure data). + + :param stage: Model training/val/test stage. Defaults to ``None`` + :type stage: Optional[str] + """ + self.download() + + def download(self): + """Download structure data to ``self.data_dir / self.TAR_FILENAME``.""" + if not os.path.exists(self.data_dir / self.TAR_FILENAME): + logger.info(f"Downloading IgFold structures to {self.data_dir}") + wget.download( + self.DATASET_URL, out=str(self.data_dir / self.TAR_FILENAME) + ) + else: + logger.info(f"IgFold structures found in {self.data_dir}") + + if not os.path.exists(self.structure_dir): + logger.info(f"Extracting tarfile to {self.structure_dir}") + tar = tarfile.open(str(self.data_dir / self.TAR_FILENAME)) + tar.extractall(str(self.data_dir)) + tar.close() + os.rename( + self.data_dir / self.UNCOMPRESSED_DIR_NAME, self.structure_dir + ) + + def parse_dataset( + self, split: Literal["train", "val", "test"] + ) -> List[str]: + """ + Parse dataset for a given ``split``. + + :param split: Name of split + :type split: Literal["train", "val", "test"] + :returns: List of filenames for a given ``split``. + :rtype: List[str] + """ + # If we've already split, return the split data + if hasattr(self, f"{split}_pdbs"): + return getattr(self, f"{split}_pdbs") + + structure_files = glob.glob( + str(self.structure_dir / f"*.{self.format}"), recursive=True + ) + logger.info( + f"Found {len(structure_files)} pdbs in: {self.structure_dir}" + ) + + # Selection subset + structure_files = random.sample( + structure_files, int(self.dataset_fraction * len(structure_files)) + ) + + structure_files = [f.split("/")[-1] for f in structure_files] + structure_files = [f.replace(".pdb", "") for f in structure_files] + + train_size, val_size, test_size = self.train_val_test + + # Split dataset + logger.info( + f"Splitting {len(structure_files)} structures into {train_size}, {val_size}, {test_size} split" + ) + train, val = train_test_split( + structure_files, test_size=val_size + test_size + ) + val, test = train_test_split( + val, test_size=test_size / (val_size + test_size) + ) + logger.info( + f"Split sizes: {len(train)} train, {len(val)} val, {len(test)} test" + ) + + self.train_pdbs = train + self.val_pdbs = val + self.test_pdbs = test + + return getattr(self, f"{split}_pdbs") + + def parse_labels(self): + """Parse labels. + + This is a no-op for this dataset as it has no labels. + """ + pass + + def exclude_pdbs(self): + """ + Identify PDBs to exclude. + + This is a no-op for this dataset as there are no PDBs to exclude.. + """ + pass + + def _get_dataset( + self, split: Literal["train", "val", "test"] + ) -> ProteinDataset: + """ + Retrieve dataset for a given split. + + :param split: Name of split to retrieve. + :type split: Literal["train", "val", "test"] + :returns: ProteinDataset for a given ``split``. + :rtype: ProteinDataset + """ + pdb_codes = self.parse_dataset(split) + logger.info(f"Initialising Graphein dataset for {split}...") + + return ProteinDataset( + pdb_codes=pdb_codes, + root=str(self.data_dir), + pdb_dir=str(self.structure_dir), + overwrite=False, + transform=self.transform, + format=self.format, + in_memory=self.in_memory, + ) + + def train_dataset(self) -> ProteinDataset: + """Return the train dataset. + + :returns: Train dataset + :rtype: ProteinDataset + """ + return self._get_dataset("train") + + def val_dataset(self) -> ProteinDataset: + """Return the val dataset. + + :returns: Val dataset + :rtype: ProteinDataset + """ + return self._get_dataset("val") + + def test_dataset(self) -> ProteinDataset: + """Return the test dataset. + + :returns: Test dataset + :rtype: ProteinDataset + """ + return self._get_dataset("test") + + def train_dataloader(self) -> ProteinDataLoader: + """Return the train dataloader. + + :returns: Train dataloader + :rtype: ProteinDataLoader + """ + return ProteinDataLoader( + self.train_dataset(), + batch_size=self.batch_size, + shuffle=True, + pin_memory=self.pin_memory, + num_workers=self.num_workers, + ) + + def val_dataloader(self) -> ProteinDataLoader: + """Return the val dataloader. + + :returns: val dataloader + :rtype: ProteinDataLoader + """ + return ProteinDataLoader( + self.val_dataset(), + batch_size=self.batch_size, + shuffle=False, + pin_memory=self.pin_memory, + num_workers=self.num_workers, + ) + + def test_dataloader(self) -> ProteinDataLoader: + """Return the test dataloader. + + :returns: Test dataloader + :rtype: ProteinDataLoader + """ + return ProteinDataLoader( + self.test_dataset(), + batch_size=self.batch_size, + shuffle=False, + pin_memory=self.pin_memory, + num_workers=self.num_workers, + ) + + +if __name__ == "__main__": + import hydra + + from proteinworkshop import constants + + cfg = omegaconf.OmegaConf.load( + constants.HYDRA_CONFIG_PATH / "dataset" / "igfold_paired_oas.yaml" + ) + cfg.datamodule.path = str( + pathlib.Path(constants.DATA_PATH) / "igfold_paired_oas" + ) + cfg.datamodule.transforms = None + + datamodule = hydra.utils.instantiate(cfg.datamodule) + datamodule.download() + dl = datamodule.test_dataloader() + for i in dl: + print(i) + break diff --git a/proteinworkshop/datasets/masif_site.py b/proteinworkshop/datasets/masif_site.py index 7b890914..30d9d2e9 100644 --- a/proteinworkshop/datasets/masif_site.py +++ b/proteinworkshop/datasets/masif_site.py @@ -1,6 +1,6 @@ import os import pathlib -from typing import Callable, Iterable, Literal, Optional +from typing import Callable, Iterable, List, Literal, Optional import omegaconf import wget @@ -61,8 +61,8 @@ def __init__( def setup(self, stage: Optional[str] = None): self.download() - def exclude_pdbs(self): - pass + def exclude_pdbs(self) -> List[str]: + return ["1EXB_ABDC", "3LVK_AC"] def download(self): if not os.path.exists(self.path / "training.txt"): @@ -161,6 +161,9 @@ def parse_dataset(self): test = f.readlines() test = [x.strip() for x in test] + train = [x for x in train if x not in self.exclude_pdbs()] + test = [x for x in test if x not in self.exclude_pdbs()] + # Train / val split train, val = train_test_split(train, test_size=self.val_fraction) @@ -197,15 +200,28 @@ def parse_dataset(self): from proteinworkshop import constants - config = omegaconf.OmegaConf.load( - "../proteinworkshop/config/dataset/masif_site.yaml" + cfg = omegaconf.OmegaConf.load( + constants.SRC_PATH / "config" / "dataset" / "masif_site.yaml" ) - config.datamodule.path = pathlib.Path(constants.DATA_PATH) / "MasifSite" # type: ignore - config.datamodule.pdb_dir = pathlib.Path(constants.DATA_PATH) / "pdb" # type: ignore - config.datamodule.transforms = None - print(config) - - dm = hydra.utils.instantiate(config) - print(dm["datamodule"]) - dm["datamodule"].setup() - dm["datamodule"].test_dataloader() + cfg.datamodule.path = pathlib.Path(constants.DATA_PATH) / "masif_site" # type: ignore + cfg.datamodule.pdb_dir = pathlib.Path(constants.DATA_PATH) / "pdb" # type: ignore + cfg.datamodule.transforms = [] + ds = hydra.utils.instantiate(cfg) + print(ds) + ds["datamodule"].setup() + ds["datamodule"].parse_dataset() + import torch + + dl = ds["datamodule"].train_dataloader() + for i, batch in enumerate(dl): + print(batch) + bad_seq = torch.argwhere(batch["amino_acid_one_hot"][:, -2] == 1) + + if bad_seq.sum() > 10: + break + # dl = ds["datamodule"].val_dataloader() + # for batch in dl: + # print(batch) + # dl = ds["datamodule"].test_dataloader() + # for batch in dl: + # print(batch) diff --git a/proteinworkshop/datasets/pdb_dataset.py b/proteinworkshop/datasets/pdb_dataset.py index 847a01f1..c29cea41 100644 --- a/proteinworkshop/datasets/pdb_dataset.py +++ b/proteinworkshop/datasets/pdb_dataset.py @@ -225,9 +225,6 @@ def test_dataset(self) -> Dataset: if __name__ == "__main__": import pathlib - import hydra - import omegaconf - from proteinworkshop import constants cfg = omegaconf.OmegaConf.load( diff --git a/proteinworkshop/datasets/sequence.py b/proteinworkshop/datasets/sequence.py index e4f88107..860bb9fb 100644 --- a/proteinworkshop/datasets/sequence.py +++ b/proteinworkshop/datasets/sequence.py @@ -14,7 +14,7 @@ try: import esm -except: +except ImportError: logger.warning( "ESM not installed. If you are using a sequence dataset this will be required to fold structures. See: https://github.com/facebookresearch/esm#quickstart" ) @@ -24,6 +24,9 @@ class SequenceDataset(Dataset): + """Dataset class for working with Sequence Datasets. Provides utilities + for batch folding and embedding with ESM(Fold).""" + def __init__( self, fasta_file: Optional[str] = None, @@ -39,8 +42,6 @@ def __init__( use_embeddings: bool = True, use_structure: bool = True, ): - """Dataset class for working with Sequence Datasets. Provides utilities - for batch folding and embedding with ESM(Fold).""" self.root = root if root is not None else os.getcwd() if not os.path.exists(self.root): os.makedirs(self.root) diff --git a/proteinworkshop/datasets/utils.py b/proteinworkshop/datasets/utils.py index 81b06e06..1d0f73d4 100644 --- a/proteinworkshop/datasets/utils.py +++ b/proteinworkshop/datasets/utils.py @@ -10,6 +10,7 @@ from typing import List, Optional import biotite.database.rcsb as rcsb +import torch import torch.nn.functional as F from graphein.protein.tensor.data import ProteinBatch, get_random_protein from tqdm import tqdm @@ -136,6 +137,8 @@ def create_example_batch(n: int = 4) -> ProteinBatch: batch.pos = batch.coords[:, 1, :] batch.x = F.one_hot(batch.residue_type, num_classes=23).float() + batch.graph_y = torch.randint(0, 2, (n, 1)) + batch.x_vector_attr = orientations(batch.pos) batch.edge_attr = pos_emb(batch.edge_index, 9) batch.edge_vector_attr = _normalize( diff --git a/proteinworkshop/explain.py b/proteinworkshop/explain.py new file mode 100644 index 00000000..522ed11a --- /dev/null +++ b/proteinworkshop/explain.py @@ -0,0 +1,193 @@ +import collections +import functools +import os +import pathlib +import sys +from typing import List, Union + +import hydra +import lightning as L +import omegaconf +import torch +from captum.attr import IntegratedGradients +from graphein.protein.tensor.data import ProteinBatch +from graphein.protein.tensor.io import to_dataframe +from loguru import logger as log +from torch_geometric.data import Batch +from torch_geometric.utils import unbatch +from tqdm import tqdm + +from proteinworkshop import ( + constants, + register_custom_omegaconf_resolvers, + utils, +) +from proteinworkshop.configs import config +from proteinworkshop.models.base import BenchMarkModel + + +def explain(cfg: omegaconf.DictConfig): + assert cfg.ckpt_path, "No checkpoint path provided." + assert ( + cfg.output_dir + ), "No output directory for attributed PDB files provided." + + cfg = config.validate_config(cfg) + + L.seed_everything(cfg.seed) + + log.info("Instantiating datamodule:... ") + datamodule: L.LightningDataModule = hydra.utils.instantiate( + cfg.dataset.datamodule + ) + + log.info("Instantiating model:... ") + model: L.LightningModule = BenchMarkModel(cfg) + + # Initialize lazy layers for parameter counts + # This is also required for the model to be able to load weights + # Otherwise lazy layers will have their parameters reset + # https://pytorch.org/docs/stable/generated/torch.nn.modules.lazy.LazyModuleMixin.html#torch.nn.modules.lazy.LazyModuleMixin + log.info("Initializing lazy layers...") + with torch.no_grad(): + datamodule.setup() # type: ignore + batch = next(iter(datamodule.val_dataloader())) + log.info(f"Unfeaturized batch: {batch}") + batch = model.featurise(batch) + log.info(f"Featurized batch: {batch}") + out = model.forward(batch) + log.info(f"Model output: {out}") + del batch, out + + # Load weights + # We only want to load weights + log.info(f"Loading weights from checkpoint {cfg.ckpt_path}...") + state_dict = torch.load(cfg.ckpt_path)["state_dict"] + + if cfg.finetune.encoder.load_weights: + encoder_weights = collections.OrderedDict() + for k, v in state_dict.items(): + if k.startswith("encoder"): + encoder_weights[k.replace("encoder.", "")] = v + # log.info(f"Loading encoder weights: {encoder_weights}") + err = model.encoder.load_state_dict(encoder_weights, strict=False) + log.warning(f"Error loading encoder weights: {err}") + + if cfg.finetune.decoder.load_weights: + decoder_weights = collections.OrderedDict() + for k, v in state_dict.items(): + if k.startswith("decoder"): + decoder_weights[k.replace("decoder.", "")] = v + # log.info(f"Loading decoder weights: {decoder_weights}") + err = model.decoder.load_state_dict(decoder_weights, strict=False) + log.warning(f"Error loading decoder weights: {err}") + else: + model.decoder = None + + OUTPUT = cfg.explain.output + output_dir = pathlib.Path(cfg.output_dir) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + # Wrap forward function and pass to captum + def _forward_wrapper( + model: L.LightningModule, + node_feats: torch.Tensor, + batch: Union[Batch, ProteinBatch], + output: str, + ): + """Wrapper function around forward pass. + + Sets node features to the provided node features and returns the + model output for the specified output. + + The node feature update is necessary to set the interpolated features + from IntegratedGradients. + """ + batch.edge_index = batch.edge_index[:2, :] # Handle GearNet edge case + batch.x = node_feats # Update node features + return model.forward(batch)[output] + + fwd = functools.partial(_forward_wrapper, model.cuda(), output=OUTPUT) + ig = IntegratedGradients(fwd) + + dataloaders = {} + if "train" in cfg.explain.split: + dataloaders["train"] = datamodule.train_dataloader() + if "val" in cfg.explain.split: + dataloaders["val"] = datamodule.val_dataloader() + if "test" in cfg.explain.split: + dataloaders["test"] = datamodule.test_dataloader() + + # Iterate over batches and perform attribution + for split, dataloader in dataloaders.items(): + log.info(f"Performing attribution for split: {split}") + + for batch in tqdm(dataloader): + batch = batch.cuda() + batch = model.featurise(batch) + node_features = batch.x + attribution = ig.attribute( + node_features, + baselines=torch.ones_like(batch.x), + additional_forward_args=batch, + target=model.get_labels(batch)[OUTPUT].long(), + internal_batch_size=cfg.dataset.datamodule.batch_size, + n_steps=cfg.explain.n_steps, + ) + attribution = attribution.sum(-1) + + # Unbatch and write each protein to disk + batch = batch.cpu() + batch_items = batch.to_data_list() + attribution_scores = unbatch(attribution, batch.batch) + for elem, score in tqdm(zip(batch_items, attribution_scores)): + # Scale score between 0-100 + score = score - score.min() + score = score / score.max() + score = score * 100 + + df = to_dataframe( + x=elem.coords, + residue_types=elem.residues, + chains=elem.chains, + insertions=None, + b_factors=score.cpu(), # Write attribution score in B factor column + occupancy=None, + charge=None, + alt_loc=None, + segment_id=None, + biopandas=True, + ) + output_path = output_dir / f"{elem.id}.pdb" + df.to_pdb(str(output_path)) + + +# Load hydra config from yaml files and command line arguments. +@hydra.main( + version_base="1.3", + config_path=str(constants.HYDRA_CONFIG_PATH), + config_name="explain", +) +def _main(cfg: omegaconf.DictConfig) -> None: + """Load and validate the hydra config.""" + utils.extras(cfg) + cfg = config.validate_config(cfg) + explain(cfg) + + +def _script_main(args: List[str]) -> None: + """ + Provides an entry point for the script dispatcher. + + Sets the sys.argv to the provided args and calls the main train function. + """ + sys.argv = args + register_custom_omegaconf_resolvers() + _main() + + +if __name__ == "__main__": + # pylint: disable=no-value-for-parameter + register_custom_omegaconf_resolvers() + _main() # type: ignore diff --git a/proteinworkshop/features/edge_features.py b/proteinworkshop/features/edge_features.py index f7fcbc62..e7877750 100644 --- a/proteinworkshop/features/edge_features.py +++ b/proteinworkshop/features/edge_features.py @@ -3,7 +3,7 @@ import numpy as np import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.types import CoordTensor, EdgeTensor from jaxtyping import jaxtyped from omegaconf import ListConfig @@ -20,8 +20,7 @@ """List of edge features that can be computed.""" -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_scalar_edge_features( x: Union[Data, Batch], features: Union[List[str], ListConfig] ) -> torch.Tensor: @@ -55,8 +54,7 @@ def compute_scalar_edge_features( return torch.cat(feats, dim=1) -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_vector_edge_features( x: Union[Data, Batch], features: Union[List[str], ListConfig] ) -> Union[Data, Batch]: @@ -71,8 +69,7 @@ def compute_vector_edge_features( return x -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_edge_distance( pos: CoordTensor, edge_index: EdgeTensor ) -> torch.Tensor: @@ -91,8 +88,7 @@ def compute_edge_distance( ) -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def pos_emb(edge_index: EdgeTensor, num_pos_emb: int = 16): # From https://github.com/jingraham/neurips19-graph-protein-design d = edge_index[0] - edge_index[1] diff --git a/proteinworkshop/features/edges.py b/proteinworkshop/features/edges.py index 48861c44..4c888892 100644 --- a/proteinworkshop/features/edges.py +++ b/proteinworkshop/features/edges.py @@ -2,16 +2,15 @@ import functools from typing import List, Literal, Optional, Tuple, Union -import beartype import graphein.protein.tensor.edges as gp import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import Protein, ProteinBatch from omegaconf import ListConfig from torch_geometric.data import Batch, Data -@beartype +@typechecker def compute_edges( x: Union[Data, Batch, Protein, ProteinBatch], edge_types: Union[ListConfig, List[str]], @@ -82,7 +81,7 @@ def compute_edges( return edges, indxs -@beartype +@typechecker def sequence_edges( b: Union[Data, Batch, Protein, ProteinBatch], chains: Optional[torch.Tensor] = None, diff --git a/proteinworkshop/features/factory.py b/proteinworkshop/features/factory.py index 6a31884d..9cc94ec0 100644 --- a/proteinworkshop/features/factory.py +++ b/proteinworkshop/features/factory.py @@ -1,14 +1,13 @@ -from typing import List, Union +from typing import List, Literal, Union import torch import torch.nn as nn -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch, get_random_batch from jaxtyping import jaxtyped from loguru import logger from torch_geometric.data import Batch from torch_geometric.nn.encoding import PositionalEncoding -from torch_geometric.utils import unbatch from proteinworkshop.features.edge_features import ( compute_scalar_edge_features, @@ -27,44 +26,44 @@ VectorNodeFeature, ) +StructureRepresentation = Literal["ca", "ca_bb", "full_atom"] + class ProteinFeaturiser(nn.Module): """ - Configurable protein featuriser. + Initialise a protein featuriser. + + :param representation: Representation to use for the protein. + One of ``"ca", "ca_bb", "full_atom"``. + :type representation: StructureRepresentation + :param scalar_node_features: List of scalar-values node features to + compute. Options: ``"amino_acid_one_hot", + "sequence_positional_encoding", "alpha", "kappa", "dihedrals" + "sidechain_torsions"``. + :type scalar_node_features: List[ScalarNodeFeature] + :param vector_node_features: List of vector-valued node features to + compute. # TODO types + :type vector_node_features: List[VectorNodeFeature] + :param edge_types: List of edge types to compute. + Options: # TODO types + :type edge_types: List[str] + :param scalar_edge_features: List of scalar-valued edge features to + compute. # TODO types + :type scalar_edge_features: List[ScalarEdgeFeature] + :param vector_edge_features: List of vector-valued edge features to + compute. # TODO types + :type vector_edge_features: List[VectorEdgeFeature] """ def __init__( self, - representation: str, + representation: StructureRepresentation, scalar_node_features: List[ScalarNodeFeature], vector_node_features: List[VectorNodeFeature], edge_types: List[str], scalar_edge_features: List[ScalarEdgeFeature], vector_edge_features: List[VectorEdgeFeature], ): - """Initialise a protein featuriser. - - :param representation: Representation to use for the protein. - One of ``"ca", "ca_bb", "full_atom"``. # TODO types - :type representation: str - :param scalar_node_features: List of scalar-values node features to - compute. Options: ``"amino_acid_one_hot", - "sequence_positional_encoding", "alpha", "kappa", "dihedrals" - "sidechain_torsions"``. # TODO types - :type scalar_node_features: List[ScalarNodeFeature] - :param vector_node_features: List of vector-valued node features to - compute. # TODO types - :type vector_node_features: List[VectorNodeFeature] - :param edge_types: List of edge types to compute. - Options: # TODO types - :type edge_types: List[str] - :param scalar_edge_features: List of scalar-valued edge features to - compute. # TODO types - :type scalar_edge_features: List[ScalarEdgeFeature] - :param vector_edge_features: List of vector-valued edge features to - compute. # TODO types - :type vector_edge_features: List[VectorEdgeFeature] - """ super(ProteinFeaturiser, self).__init__() self.representation = representation self.scalar_node_features = scalar_node_features @@ -76,8 +75,7 @@ def __init__( if "sequence_positional_encoding" in self.scalar_node_features: self.positional_encoding = PositionalEncoding(16) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, batch: Union[Batch, ProteinBatch] ) -> Union[Batch, ProteinBatch]: @@ -85,24 +83,17 @@ def forward( if self.scalar_node_features: concat_nf = False if hasattr(self, "positional_encoding"): - lin_range = torch.arange( - 0, batch.coords.shape[0], device=batch.coords.device - ) - idx = batch._slice_dict["coords"] - slices = unbatch(lin_range, batch.batch) - slices = [s - idx[i] for i, s in enumerate(slices)] - slices = torch.cat(slices, dim=0) - batch.x = self.positional_encoding(slices) + batch.x = self.positional_encoding(batch.seq_pos) # This is necessary to concat node features with the positional encoding concat_nf = True - - scalar_features = compute_scalar_node_features( - batch, self.scalar_node_features - ) - if concat_nf: - batch.x = torch.cat([batch.x, scalar_features], dim=-1) - else: - batch.x = scalar_features + if self.scalar_node_features != ["sequence_positional_encoding"]: + scalar_features = compute_scalar_node_features( + batch, self.scalar_node_features + ) + if concat_nf: + batch.x = torch.cat([batch.x, scalar_features], dim=-1) + else: + batch.x = scalar_features batch.x = torch.nan_to_num( batch.x, nan=0.0, posinf=0.0, neginf=0.0 ) @@ -148,7 +139,6 @@ def __repr__(self) -> str: if __name__ == "__main__": import hydra import omegaconf - from loguru import logger from proteinworkshop import constants diff --git a/proteinworkshop/features/node_features.py b/proteinworkshop/features/node_features.py index dd4c669b..07013fac 100644 --- a/proteinworkshop/features/node_features.py +++ b/proteinworkshop/features/node_features.py @@ -3,7 +3,7 @@ import torch import torch.nn.functional as F -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.angles import ( alpha, dihedrals, @@ -25,8 +25,7 @@ from .utils import _normalize -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_scalar_node_features( x: Union[Batch, Data, Protein, ProteinBatch], node_features: Union[ListConfig, List[ScalarNodeFeature]], @@ -86,8 +85,7 @@ def compute_scalar_node_features( return torch.cat(feats, dim=1) if feats else x.x -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_vector_node_features( x: Union[Batch, Data, Protein, ProteinBatch], vector_features: Union[ListConfig, List[str]], @@ -114,8 +112,7 @@ def compute_vector_node_features( return x -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_surface_feat( coords: Union[CoordTensor, AtomTensor], k: int, sigma: List[float] ): @@ -150,8 +147,7 @@ def compute_surface_feat( return torch.cat(feat, dim=1) -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def orientations( X: Union[CoordTensor, AtomTensor], ca_idx: int = 1 ) -> OrientationTensor: diff --git a/proteinworkshop/features/representation.py b/proteinworkshop/features/representation.py index a8fed802..3cd7b573 100644 --- a/proteinworkshop/features/representation.py +++ b/proteinworkshop/features/representation.py @@ -2,7 +2,7 @@ from typing import Literal, Tuple import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.types import AtomTensor, CoordTensor from jaxtyping import jaxtyped from torch_geometric.data import Batch, Data @@ -11,8 +11,7 @@ from proteinworkshop.configs.config import ExperimentConfigurationError -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def get_full_atom_coords( atom_tensor: AtomTensor, fill_value: float = 1e-5 ) -> Tuple[CoordTensor, torch.Tensor, torch.Tensor]: @@ -40,8 +39,7 @@ def get_full_atom_coords( return coords, residue_index, atom_type -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def transform_representation( x: Batch, representation_type: Literal["CA", "BB", "FA", "BB_SC", "CA_SC"] ) -> Batch: @@ -87,7 +85,7 @@ def transform_representation( ) -@beartype +@typechecker def _ca_to_fa_repr(x: Data) -> Data: """Converts CA representation to full atom representation.""" coords, residue_index, atom_type = get_full_atom_coords(x.coords) @@ -101,7 +99,7 @@ def _ca_to_fa_repr(x: Data) -> Data: return x -@beartype +@typechecker def _ca_to_bb_repr(x: Data) -> Data: """Converts CA representation to backbone representation.""" x.pos = x.coords[:, :4, :].reshape(-1, 3) @@ -117,7 +115,7 @@ def _ca_to_bb_repr(x: Data) -> Data: return x -@beartype +@typechecker def ca_to_bb_repr(batch: Batch) -> Batch: # sourcery skip: assign-if-exp """ Converts a batch of CA representations to backbone representations. I.e. @@ -180,7 +178,7 @@ def ca_to_bb_repr(batch: Batch) -> Batch: # sourcery skip: assign-if-exp return batch -@beartype +@typechecker def ca_to_bb_sc_repr(batch: Batch) -> Batch: """Converts a batch of CA representations to backbone + sidechain representations.""" # Get centroids @@ -190,7 +188,7 @@ def ca_to_bb_sc_repr(batch: Batch) -> Batch: return ca_to_fa_repr(batch) -@beartype +@typechecker def ca_to_ca_sc_repr(batch: Batch) -> Batch: """Converts a batch of CA representations to C + sidechain representations.""" # Get centroids @@ -200,7 +198,7 @@ def ca_to_ca_sc_repr(batch: Batch) -> Batch: return batch -@beartype +@typechecker def coarsen_sidechain(x: Data, aggr: str = "mean") -> CoordTensor: """Returns tensor of sidechain centroids: L x 3""" # sourcery skip: remove-unnecessary-else, swap-if-else-branches @@ -216,7 +214,7 @@ def coarsen_sidechain(x: Data, aggr: str = "mean") -> CoordTensor: return sc_points -@beartype +@typechecker def ca_to_fa_repr(batch: Batch) -> Batch: # sourcery skip: assign-if-exp """Converts a batch of CA representations to full atom representations.""" if "sidechain_torsion" in batch.keys: diff --git a/proteinworkshop/features/sequence_features.py b/proteinworkshop/features/sequence_features.py index 58815f89..e012107c 100644 --- a/proteinworkshop/features/sequence_features.py +++ b/proteinworkshop/features/sequence_features.py @@ -3,11 +3,11 @@ import torch import torch.nn.functional as F -from beartype import beartype +from beartype import beartype as typechecker from torch_geometric.data import Batch, Data -@beartype +@typechecker def amino_acid_one_hot( x: Union[Batch, Data], num_classes: int = 23 ) -> torch.Tensor: diff --git a/proteinworkshop/features/utils.py b/proteinworkshop/features/utils.py index 2d6b7190..e2f4e1c3 100644 --- a/proteinworkshop/features/utils.py +++ b/proteinworkshop/features/utils.py @@ -1,10 +1,9 @@ import torch -from beartype import beartype +from beartype import beartype as typechecker from jaxtyping import jaxtyped -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def _normalize(tensor: torch.Tensor, dim: int = -1) -> torch.Tensor: """ Safely normalize a Tensor. Adapted from: diff --git a/proteinworkshop/metrics/__init__.py b/proteinworkshop/metrics/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/proteinworkshop/metrics/auprc.py b/proteinworkshop/metrics/auprc.py index 32698069..2acf9c01 100644 --- a/proteinworkshop/metrics/auprc.py +++ b/proteinworkshop/metrics/auprc.py @@ -1,20 +1,18 @@ """Implementation of the AUPRC metric in ``torchmetrics``.""" -from typing import Any import torch from torchmetrics import Metric class AUPRC(Metric): - """Class for AUPRC metric.""" + """Class for AUPRC metric. - def __init__(self, compute_on_cpu: bool = True) -> None: - """Initialises the AUPRC metric. - - :param compute_on_cpu: Whether to compute the metric on CPU, + :param compute_on_cpu: Whether to compute the metric on CPU, defaults to ``True``. - :type compute_on_cpu: bool, optional - """ + :type compute_on_cpu: bool, optional + """ + + def __init__(self, compute_on_cpu: bool = True) -> None: super().__init__() self.add_state("preds", default=[], dist_reduce_fx="cat") self.add_state("targets", default=[], dist_reduce_fx="cat") @@ -28,8 +26,8 @@ def update(self, preds: torch.Tensor, target: torch.Tensor) -> None: :param target: Tensor of targets :type target: torch.Tensor """ - self.preds.append(preds) - self.targets.append(target) + self.preds.append(preds.detach()) + self.targets.append(target.detach()) def compute(self) -> torch.Tensor: """Computes the AUPRC metric. diff --git a/proteinworkshop/metrics/f1_max.py b/proteinworkshop/metrics/f1_max.py index f7f4f665..61681c6b 100644 --- a/proteinworkshop/metrics/f1_max.py +++ b/proteinworkshop/metrics/f1_max.py @@ -3,6 +3,8 @@ import torch import torch.nn.functional as F +from jaxtyping import Float +from torch import Tensor from torchmetrics import Metric @@ -26,8 +28,8 @@ def __init__(self, num_classes: int, compute_on_cpu: bool = True) -> None: self.compute_on_cpu = compute_on_cpu def update(self, preds: torch.Tensor, target: torch.Tensor) -> None: - self.preds.append(preds) - self.targets.append(target) + self.preds.append(preds.detach()) + self.targets.append(target.detach()) def compute(self) -> Any: """Computes the F1Max metric. @@ -38,9 +40,18 @@ def compute(self) -> Any: :return: F1Max metric value :rtype: Any """ - return self.f1_max(torch.cat(self.preds), torch.cat(self.targets)) + if self.preds[0].ndim == 2: + # return self.f1_max(torch.stack(self.preds), torch.stack(self.targets)) + return self.f1_max(torch.cat(self.preds), torch.cat(self.targets)) + return self.f1_max( + torch.cat(self.preds, dim=0), torch.cat(self.targets, dim=0) + ) - def f1_max(self, pred, target): + def f1_max( + self, + pred: Float[Tensor, "batch classes"], + target: Float[Tensor, "batch classes"], + ): """ F1 score with the optimal threshold. This function first enumerates all possible thresholds for deciding @@ -52,9 +63,13 @@ def f1_max(self, pred, target): target (Tensor): binary targets of shape :math:`(B, N)` """ pred = torch.softmax(pred, dim=1) - target = F.one_hot(target.long(), num_classes=pred.shape[1]).float() + + if target.ndim == 1: + target = F.one_hot( + target.long(), num_classes=pred.shape[1] + ).float() order = pred.argsort(descending=True, dim=1) - target = target.gather(1, order) + target = target.gather(1, order).int() precision = target.cumsum(1) / torch.ones_like(target).cumsum(1) recall = target.cumsum(1) / (target.sum(1, keepdim=True) + 1e-10) is_start = torch.zeros_like(target).bool() diff --git a/proteinworkshop/model_io.py b/proteinworkshop/model_io.py new file mode 100644 index 00000000..168e236a --- /dev/null +++ b/proteinworkshop/model_io.py @@ -0,0 +1,124 @@ +"""Utilities for loading models.""" +import collections +import os +import pathlib +from typing import Any, Dict, Tuple, Union + +import omegaconf +import torch +from loguru import logger + +import wandb +from proteinworkshop.models.base import BenchMarkModel + + +def load_model_from_checkpoint( + ckpt_path: Union[str, os.PathLike], + load_state: bool = False, + load_weights: bool = True, +) -> Tuple[BenchMarkModel, omegaconf.DictConfig]: + """Loads a model from a checkpoint. + + :param ckpt_path: Path to the checkpoint. + :type ckpt_path: Union[str, os.PathLike] + :param load_weights: Whether to load the weights from the checkpoint. + :type load_weights: bool + :return: The model. + """ + config = load_config_from_checkpoint(ckpt_path) + model = BenchMarkModel(config) + + if load_state: + return model.load_from_checkpoint(ckpt_path), config + + if load_weights: + ckpt = torch.load(ckpt_path) + return load_weights(model, ckpt["state_dict"]), config + else: + return model, config + + +def load_config_from_checkpoint( + ckpt_path: Union[str, os.PathLike] +) -> omegaconf.DictConfig: + """Loads a config from a checkpoint. + + :param ckpt_path: Path to the checkpoint. + :type ckpt_path: Union[str, os.PathLike] + :return: The config. + """ + ckpt = torch.load(ckpt_path) + config = ckpt["hyper_parameters"]["cfg"] + config = omegaconf.OmegaConf.create(config) + return config + + +def load_model_from_wandb( + run_id: str, + entity: str, + project: str, + version: str = "v0", + load_state: bool = False, + load_weights: bool = True, +) -> Dict[str, Any]: + """Loads a model from wandb.""" + api = wandb.Api() + run_name = f"{entity}/{project}/{run_id}" + run = api.run(run_name) + + # Download config + files = run.files() + downloaded = False + for file in files: + if file.name == "config.yaml": + file.download() + logger.info(f"Download config for run {run_id}") + downloaded = True + + if not downloaded: + logger.error(f"Failed to download config for {run_name}") + + # TODO: Automatically select best model + ckpt_ref = f"{entity}/{project}/model-{run_id}:{version}" + artifact = api.artifact(ckpt_ref, type="model") + artifact_dir = artifact.download() + + return load_model_from_checkpoint( + pathlib.Path(artifact_dir) / "model.ckpt" + ) + + +def load_weights( + model: BenchMarkModel, state_dict: Dict[str, Any] +) -> BenchMarkModel: + """Load weights from a state dict into a model. + + :param model: The model. + :type model: BenchMarkModel + :param state_dict: The state dict containing weights. + :type state_dict: Dict[str, Any] + :return: The model with loaded weights. + """ + encoder_weights = collections.OrderedDict() + for k, v in state_dict.items(): + if k.startswith("encoder"): + encoder_weights[k.replace("encoder.", "")] = v + err = model.encoder.load_state_dict(encoder_weights, strict=False) + logger.warning(f"Error loading encoder weights: {err}") + + decoder_weights = collections.OrderedDict() + for k, v in state_dict.items(): + if k.startswith("decoder"): + decoder_weights[k.replace("decoder.", "")] = v + err = model.decoder.load_state_dict(decoder_weights, strict=False) + logger.warning(f"Error loading decoder weights: {err}") + + logger.info("Freezing encoder!") + for param in model.encoder.parameters(): + param.requires_grad = False + + logger.info("Freezing decoder!") + for param in model.decoder.parameters(): + param.requires_grad = False + + return model diff --git a/proteinworkshop/models/base.py b/proteinworkshop/models/base.py index 7a1490e0..3c0149d0 100644 --- a/proteinworkshop/models/base.py +++ b/proteinworkshop/models/base.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn import torch.nn.functional as F -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.angles import dihedrals from graphein.protein.tensor.data import ProteinBatch, get_random_protein from loguru import logger @@ -121,8 +121,11 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: ).float() elif output == "graph_label": labels["graph_label"] = batch.graph_y - if isinstance( - self.losses["graph_label"], torch.nn.BCEWithLogitsLoss + if ( + isinstance( + self.losses["graph_label"], torch.nn.BCEWithLogitsLoss + ) + and batch.graph_y.ndim == 1 ): labels["graph_label"] = F.one_hot( labels["graph_label"], @@ -172,7 +175,7 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: return Label(labels) - @beartype + @typechecker def compute_loss( self, y_hat: ModelOutput, y: Label ) -> Dict[str, torch.Tensor]: @@ -304,6 +307,7 @@ def configure_metrics(self): "auprc", "accuracy", "f1_max", + "rocauc", } REGRESSION_METRICS: Set[str] = {"mse", "mae", "r2", "rmse"} CONTINUOUS_OUTPUTS: Set[str] = { @@ -321,6 +325,8 @@ def configure_metrics(self): for stage in {"train", "val", "test"}: metric = hydra.utils.instantiate(metric_conf) if output == "residue_type": + if metric_name not in {"accuracy", "perplexity"}: + continue metric.num_classes = 23 metric.task = "multiclass" @@ -345,7 +351,7 @@ def configure_metrics(self): metric_names.append(f"{metric_name}") setattr(self, "metric_names", metric_names) - @beartype + @typechecker def log_metrics( self, loss, y_hat: ModelOutput, y: Label, stage: str, batch: Batch ): @@ -365,9 +371,8 @@ def log_metrics( :param batch: Batch of data :type batch: Batch """ - # Log loss - for k, v in loss.items(): - self.log(f"{stage}/loss/{k}", v, prog_bar=True, logger=True) + # Log losses + log_dict = {f"{stage}/loss/{k}": v for k, v in loss.items()} # Log metrics for m in self.metric_names: @@ -388,14 +393,11 @@ def log_metrics( val = metric(pred, target) except RuntimeError: val = metric(pred, target.unsqueeze(-1)) - self.log( - f"{stage}/{output}/{m}", - val, - prog_bar=True, - logger=True, - ) + log_dict[f"{stage}/{output}/{m}"] = val + except (ValueError, RuntimeError): continue + self.log_dict(log_dict, prog_bar=True) class BenchMarkModel(BaseModel): @@ -456,10 +458,13 @@ def _create_example_batch(self) -> ProteinBatch: ] for p in proteins: setattr(p, "x", torch.zeros(p.coords.shape[0])) + setattr( + p, "seq_pos", torch.arange(p.coords.shape[0]).unsqueeze(-1) + ) batch = ProteinBatch.from_data_list(proteins) return self.featurise(batch) - @beartype + @typechecker def forward(self, batch: Union[Batch, ProteinBatch]) -> ModelOutput: """ Implements the forward pass of the model. @@ -500,7 +505,7 @@ def forward(self, batch: Union[Batch, ProteinBatch]) -> ModelOutput: return self.compute_output(output, batch) - @beartype + @typechecker def transform_encoder_output( self, output: EncoderOutput, batch ) -> EncoderOutput: @@ -529,7 +534,7 @@ def transform_encoder_output( return output - @beartype + @typechecker def compute_output(self, output: ModelOutput, batch: Batch) -> ModelOutput: """ Computes output from model output. @@ -561,7 +566,7 @@ def compute_output(self, output: ModelOutput, batch: Batch) -> ModelOutput: return output - @beartype + @typechecker def _do_step( self, batch: Batch, diff --git a/proteinworkshop/models/graph_encoders/cdconv.py b/proteinworkshop/models/graph_encoders/cdconv.py new file mode 100644 index 00000000..d99bfeeb --- /dev/null +++ b/proteinworkshop/models/graph_encoders/cdconv.py @@ -0,0 +1,149 @@ +""" +CDConv implementation adapted from the MIT-licensed source: +https://github.com/hehefan/Continuous-Discrete-Convolution/tree/main +""" + +from typing import List, Set, Union + +import omegaconf +import torch +import torch.nn as nn +import torch.nn.functional as F +from graphein.protein.tensor.data import ProteinBatch +from torch_geometric.data import Batch + +from proteinworkshop.models.graph_encoders.layers.cdconv import ( + AvgPooling, + BasicBlock, +) +from proteinworkshop.models.utils import get_aggregation +from proteinworkshop.types import EncoderOutput + + +class CDConvModel(nn.Module): + def __init__( + self, + geometric_radius: float, + sequential_kernel_size: float, + kernel_channels: List[int], + channels: List[int], + base_width: float = 16.0, + embedding_dim: int = 16, + batch_norm: bool = True, + dropout: float = 0.2, + bias: bool = False, + readout: str = "sum", + ) -> nn.Module: + super().__init__() + + geometric_radii = [ + geometric_radius * i for i in range(1, len(channels) + 1) + ] + assert len(geometric_radii) == len( + channels + ), "Model: 'geometric_radii' and 'channels' should have the same number of elements!" + + self.embedding = torch.nn.LazyLinear(embedding_dim) + + self.local_mean_pool = AvgPooling() + + if isinstance(kernel_channels, omegaconf.ListConfig): + kernel_channels = list(kernel_channels) + + layers = [] + in_channels = embedding_dim + for i, radius in enumerate(geometric_radii): + layers.append( + BasicBlock( + r=radius, + l=sequential_kernel_size, + kernel_channels=kernel_channels, + in_channels=in_channels, + out_channels=channels[i], + base_width=base_width, + batch_norm=batch_norm, + dropout=dropout, + bias=bias, + ) + ) + layers.append( + BasicBlock( + r=radius, + l=sequential_kernel_size, + kernel_channels=kernel_channels, + in_channels=channels[i], + out_channels=channels[i], + base_width=base_width, + batch_norm=batch_norm, + dropout=dropout, + bias=bias, + ) + ) + in_channels = channels[i] + + self.layers = nn.Sequential(*layers) + self.readout = get_aggregation(readout) + + @property + def required_batch_attributes(self) -> Set[str]: + return {"x", "pos", "seq_pos", "batch"} + + @staticmethod + def orientation(pos): + u = F.normalize(pos[1:, :] - pos[:-1, :], p=2, dim=1) + u1 = u[1:, :] + u2 = u[:-1, :] + b = F.normalize(u2 - u1, p=2, dim=1) + n = F.normalize(torch.cross(u2, u1), p=2, dim=1) + o = F.normalize(torch.cross(b, n), p=2, dim=1) + ori = torch.stack([b, n, o], dim=1) + return torch.cat( + [torch.unsqueeze(ori[0], 0), ori, torch.unsqueeze(ori[-1], 0)], + axis=0, + ) + + def forward(self, data: Union[ProteinBatch, Batch]) -> EncoderOutput: + x, pos, seq, batch = ( + self.embedding(data.x), + data.pos, + data.seq_pos, + data.batch, + ) + + ori = self.orientation(pos) + + for i, layer in enumerate(self.layers): + x = layer(x, pos, seq, ori, batch) + if i == len(self.layers) - 1: + x = self.readout(x, batch) + elif i % 2 == 1: + x, pos, seq, ori, batch = self.local_mean_pool( + x, pos, seq, ori, batch + ) + + return EncoderOutput({"graph_embedding": x, "node_embedding": None}) + + +if __name__ == "__main__": + import hydra + + from proteinworkshop import constants + + cfg = omegaconf.OmegaConf.load( + constants.SRC_PATH / "config" / "encoder" / "cdconv.yaml" + ) + enc = hydra.utils.instantiate(cfg) + print(enc) + + from graphein.protein.tensor.data import get_random_protein + + protein = get_random_protein() + protein.seq_pos = torch.arange(protein.coords.shape[0]) + + batch = ProteinBatch().from_data_list([protein] * 4) + batch.x = torch.randn((batch.coords.shape[0], 16)) + batch.pos = batch.coords[:, 1, :] + print(batch) + print(enc.orientation(batch.pos).shape) + + # enc(batch) diff --git a/proteinworkshop/models/graph_encoders/components/radial.py b/proteinworkshop/models/graph_encoders/components/radial.py index 8337b08b..1474930f 100644 --- a/proteinworkshop/models/graph_encoders/components/radial.py +++ b/proteinworkshop/models/graph_encoders/components/radial.py @@ -6,7 +6,7 @@ import numpy as np import torch -from beartype import beartype +from beartype import beartype as typechecker from jaxtyping import Float, jaxtyped @@ -95,10 +95,9 @@ def __repr__(self) -> str: return f"{self.__class__.__name__}(p={self.p}, r_max={self.r_max})" -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_rbf( - distances: Float[torch.Tensor, "num_edges"], + distances: Float[torch.Tensor, " num_edges"], min_distance: float = 0.0, max_distance: float = 10.0, num_rbf: int = 8, diff --git a/proteinworkshop/models/graph_encoders/components/rigid_utils.py b/proteinworkshop/models/graph_encoders/components/rigid_utils.py new file mode 100644 index 00000000..26580dbf --- /dev/null +++ b/proteinworkshop/models/graph_encoders/components/rigid_utils.py @@ -0,0 +1,1378 @@ +from typing import Any, Callable, Optional, Sequence, Tuple + +import numpy as np +import torch + + +def get_rigid_from_three_points(x1, x2, x3, eps=1e-8): + # CA -> C vector + v1 = x3 - x2 + + # Robust normalization + denom = np.sqrt(np.sum(np.square(v1), axis=-1) + eps) + e1 = np.stack( + [v1[..., i, :] / denom[..., i] for i in range(v1.shape[1])], axis=1 + ) + + # CA -> N vector + v2 = x1 - x2 + + # Project v2 in direction of e1 and eliminate that contribution from v2 to get a vector orthogonal to e1 + proj = np.stack( + [ + e1[:, i, :] * np.sum(e1 * v2, axis=-1)[:, i] + for i in range(e1.shape[1]) + ], + axis=1, + ) + u2 = v2 - proj + + # Robust normalization + denom = np.sqrt(np.sum(np.square(u2), axis=-1) + eps) + e2 = np.stack( + [u2[..., i, :] / denom[..., i] for i in range(u2.shape[1])], axis=1 + ) + + # Get third axis + e3 = np.cross(e1, e2, axis=-1) + + # Make rotation mat + R = np.stack([e1, e2, e3], axis=-1) + + # Get translation (CA coords) + t = x2 + + # Construct rigid tuple + rigid = (R, t) + + return rigid + + +_quat_elements = ["a", "b", "c", "d"] +_qtr_keys = [l1 + l2 for l1 in _quat_elements for l2 in _quat_elements] +_qtr_ind_dict = {key: ind for ind, key in enumerate(_qtr_keys)} + + +def identity_rot_mats( + batch_dims: Tuple[int], + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, +) -> torch.Tensor: + rots = torch.eye( + 3, dtype=dtype, device=device, requires_grad=requires_grad + ) + rots = rots.view(*((1,) * len(batch_dims)), 3, 3) + rots = rots.expand(*batch_dims, -1, -1) + + return rots + + +def identity_quats( + batch_dims: Tuple[int], + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, +) -> torch.Tensor: + quat = torch.zeros( + (*batch_dims, 4), + dtype=dtype, + device=device, + requires_grad=requires_grad, + ) + + with torch.no_grad(): + quat[..., 0] = 1 + + return quat + + +def _to_mat(pairs): + mat = np.zeros((4, 4)) + for pair in pairs: + key, value = pair + ind = _qtr_ind_dict[key] + mat[ind // 4][ind % 4] = value + + return mat + + +_QTR_MAT = np.zeros((4, 4, 3, 3)) +_QTR_MAT[..., 0, 0] = _to_mat([("aa", 1), ("bb", 1), ("cc", -1), ("dd", -1)]) +_QTR_MAT[..., 0, 1] = _to_mat([("bc", 2), ("ad", -2)]) +_QTR_MAT[..., 0, 2] = _to_mat([("bd", 2), ("ac", 2)]) +_QTR_MAT[..., 1, 0] = _to_mat([("bc", 2), ("ad", 2)]) +_QTR_MAT[..., 1, 1] = _to_mat([("aa", 1), ("bb", -1), ("cc", 1), ("dd", -1)]) +_QTR_MAT[..., 1, 2] = _to_mat([("cd", 2), ("ab", -2)]) +_QTR_MAT[..., 2, 0] = _to_mat([("bd", 2), ("ac", -2)]) +_QTR_MAT[..., 2, 1] = _to_mat([("cd", 2), ("ab", 2)]) +_QTR_MAT[..., 2, 2] = _to_mat([("aa", 1), ("bb", -1), ("cc", -1), ("dd", 1)]) + + +def quat_to_rot(quat: torch.Tensor) -> torch.Tensor: + """ + Converts a quaternion to a rotation matrix. + Args: + quat: [*, 4] quaternions + Returns: + [*, 3, 3] rotation matrices + """ + # [*, 4, 4] + quat = quat[..., None] * quat[..., None, :] + + # [4, 4, 3, 3] + mat = quat.new_tensor(_QTR_MAT, requires_grad=False) + + # [*, 4, 4, 3, 3] + shaped_qtr_mat = mat.view((1,) * len(quat.shape[:-2]) + mat.shape) + quat = quat[..., None, None] * shaped_qtr_mat + + # [*, 3, 3] + return torch.sum(quat, dim=(-3, -4)) + + +def rot_to_quat( + rot: torch.Tensor, +): + if rot.shape[-2:] != (3, 3): + raise ValueError("Input rotation is incorrectly shaped") + + rot = [[rot[..., i, j] for j in range(3)] for i in range(3)] + [[xx, xy, xz], [yx, yy, yz], [zx, zy, zz]] = rot + + k = [ + [ + xx + yy + zz, + zy - yz, + xz - zx, + yx - xy, + ], + [ + zy - yz, + xx - yy - zz, + xy + yx, + xz + zx, + ], + [ + xz - zx, + xy + yx, + yy - xx - zz, + yz + zy, + ], + [ + yx - xy, + xz + zx, + yz + zy, + zz - xx - yy, + ], + ] + + k = (1.0 / 3.0) * torch.stack([torch.stack(t, dim=-1) for t in k], dim=-2) + + _, vectors = torch.linalg.eigh(k) + return vectors[..., -1] + + +_QUAT_MULTIPLY = np.zeros((4, 4, 4)) +_QUAT_MULTIPLY[:, :, 0] = [ + [1, 0, 0, 0], + [0, -1, 0, 0], + [0, 0, -1, 0], + [0, 0, 0, -1], +] + +_QUAT_MULTIPLY[:, :, 1] = [ + [0, 1, 0, 0], + [1, 0, 0, 0], + [0, 0, 0, 1], + [0, 0, -1, 0], +] + +_QUAT_MULTIPLY[:, :, 2] = [ + [0, 0, 1, 0], + [0, 0, 0, -1], + [1, 0, 0, 0], + [0, 1, 0, 0], +] + +_QUAT_MULTIPLY[:, :, 3] = [ + [0, 0, 0, 1], + [0, 0, 1, 0], + [0, -1, 0, 0], + [1, 0, 0, 0], +] + +_QUAT_MULTIPLY_BY_VEC = _QUAT_MULTIPLY[:, 1:, :] + + +def quat_multiply_by_vec(quat, vec): + """Multiply a quaternion by a pure-vector quaternion.""" + mat = quat.new_tensor(_QUAT_MULTIPLY_BY_VEC) + reshaped_mat = mat.view((1,) * len(quat.shape[:-1]) + mat.shape) + return torch.sum( + reshaped_mat * quat[..., :, None, None] * vec[..., None, :, None], + dim=(-3, -2), + ) + + +def rot_matmul(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + """ + Performs matrix multiplication of two rotation matrix tensors. Written + out by hand to avoid AMP downcasting. + Args: + a: [*, 3, 3] left multiplicand + b: [*, 3, 3] right multiplicand + Returns: + The product ab + """ + + def row_mul(i): + return torch.stack( + [ + a[..., i, 0] * b[..., 0, 0] + + a[..., i, 1] * b[..., 1, 0] + + a[..., i, 2] * b[..., 2, 0], + a[..., i, 0] * b[..., 0, 1] + + a[..., i, 1] * b[..., 1, 1] + + a[..., i, 2] * b[..., 2, 1], + a[..., i, 0] * b[..., 0, 2] + + a[..., i, 1] * b[..., 1, 2] + + a[..., i, 2] * b[..., 2, 2], + ], + dim=-1, + ) + + return torch.stack( + [ + row_mul(0), + row_mul(1), + row_mul(2), + ], + dim=-2, + ) + + +def quat_multiply(quat1, quat2): + """Multiply a quaternion by another quaternion.""" + mat = quat1.new_tensor(_QUAT_MULTIPLY) + reshaped_mat = mat.view((1,) * len(quat1.shape[:-1]) + mat.shape) + return torch.sum( + reshaped_mat * quat1[..., :, None, None] * quat2[..., None, :, None], + dim=(-3, -2), + ) + + +def rot_vec_mul(r: torch.Tensor, t: torch.Tensor) -> torch.Tensor: + """ + Applies a rotation to a vector. Written out by hand to avoid transfer + to avoid AMP downcasting. + Args: + r: [*, 3, 3] rotation matrices + t: [*, 3] coordinate tensors + Returns: + [*, 3] rotated coordinates + """ + x, y, z = torch.unbind(t, dim=-1) + return torch.stack( + [ + r[..., 0, 0] * x + r[..., 0, 1] * y + r[..., 0, 2] * z, + r[..., 1, 0] * x + r[..., 1, 1] * y + r[..., 1, 2] * z, + r[..., 2, 0] * x + r[..., 2, 1] * y + r[..., 2, 2] * z, + ], + dim=-1, + ) + + +def invert_rot_mat(rot_mat: torch.Tensor): + return rot_mat.transpose(-1, -2) + + +def invert_quat(quat: torch.Tensor): + quat_prime = quat.clone() + quat_prime[..., 1:] *= -1 + inv = quat_prime / torch.sum(quat**2, dim=-1, keepdim=True) + return inv + + +def identity_trans( + batch_dims: Tuple[int], + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, +) -> torch.Tensor: + trans = torch.zeros( + (*batch_dims, 3), + dtype=dtype, + device=device, + requires_grad=requires_grad, + ) + return trans + + +class Rotation: + """ + A 3D rotation. Depending on how the object is initialized, the + rotation is represented by either a rotation matrix or a + quaternion, though both formats are made available by helper functions. + To simplify gradient computation, the underlying format of the + rotation cannot be changed in-place. Like Rigid, the class is designed + to mimic the behavior of a torch Tensor, almost as if each Rotation + object were a tensor of rotations, in one format or another. + + :param rot_mats: A ``[*, 3, 3]`` rotation matrix tensor. Mutually exclusive + with quats. Defaults to ``None``. + :type rot_mats: Optional[torch.Tensor] + :param quats: A [*, 4] quaternion. Mutually exclusive with rot_mats. If + normalize_quats is not ``True``, must be a unit quaternion. Defaults to + ``None``. + :type quats: Optional[torch.Tensor] + :param normalize_quats: If quats is specified, whether to normalize quats. + Defaults to ``True``. + :type normalize_quats: bool, optional + """ + + def __init__( + self, + rot_mats: Optional[torch.Tensor] = None, + quats: Optional[torch.Tensor] = None, + normalize_quats: bool = True, + ): + if (rot_mats is None and quats is None) or ( + rot_mats is not None and quats is not None + ): + raise ValueError("Exactly one input argument must be specified") + + if (rot_mats is not None and rot_mats.shape[-2:] != (3, 3)) or ( + quats is not None and quats.shape[-1] != 4 + ): + raise ValueError( + "Incorrectly shaped rotation matrix or quaternion" + ) + + # Force full-precision + if quats is not None: + quats = quats.to(dtype=torch.float32) + if rot_mats is not None: + rot_mats = rot_mats.to(dtype=torch.float32) + + if quats is not None and normalize_quats: + quats = quats / torch.linalg.norm(quats, dim=-1, keepdim=True) + + self._rot_mats = rot_mats + self._quats = quats + + @staticmethod + def identity( + shape, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, + fmt: str = "quat", + ): + """ + Returns an identity Rotation. + Args: + shape: + The "shape" of the resulting Rotation object. See documentation + for the shape property + dtype: + The torch dtype for the rotation + device: + The torch device for the new rotation + requires_grad: + Whether the underlying tensors in the new rotation object + should require gradient computation + fmt: + One of "quat" or "rot_mat". Determines the underlying format + of the new object's rotation + Returns: + A new identity rotation + """ + if fmt == "rot_mat": + rot_mats = identity_rot_mats( + shape, + dtype, + device, + requires_grad, + ) + return Rotation(rot_mats=rot_mats, quats=None) + elif fmt == "quat": + quats = identity_quats(shape, dtype, device, requires_grad) + return Rotation(rot_mats=None, quats=quats, normalize_quats=False) + else: + raise ValueError(f"Invalid format: f{fmt}") + + # Magic methods + + def __getitem__(self, index: Any): + """ + Allows torch-style indexing over the virtual shape of the rotation + object. See documentation for the shape property. + Args: + index: + A torch index. E.g. (1, 3, 2), or (slice(None,)) + Returns: + The indexed rotation + """ + if type(index) != tuple: + index = (index,) + + if self._rot_mats is not None: + rot_mats = self._rot_mats[index + (slice(None), slice(None))] + return Rotation(rot_mats=rot_mats) + elif self._quats is not None: + quats = self._quats[index + (slice(None),)] + return Rotation(quats=quats, normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + def __mul__( + self, + right: torch.Tensor, + ): + """ + Pointwise left multiplication of the rotation with a tensor. Can be + used to e.g. mask the Rotation. + Args: + right: + The tensor multiplicand + Returns: + The product + """ + if not (isinstance(right, torch.Tensor)): + raise TypeError("The other multiplicand must be a Tensor") + + if self._rot_mats is not None: + rot_mats = self._rot_mats * right[..., None, None] + return Rotation(rot_mats=rot_mats, quats=None) + elif self._quats is not None: + quats = self._quats * right[..., None] + return Rotation(rot_mats=None, quats=quats, normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + def __rmul__( + self, + left: torch.Tensor, + ): + """ + Reverse pointwise multiplication of the rotation with a tensor. + Args: + left: + The left multiplicand + Returns: + The product + """ + return self.__mul__(left) + + # Properties + + @property + def shape(self) -> torch.Size: + """ + Returns the virtual shape of the rotation object. This shape is + defined as the batch dimensions of the underlying rotation matrix + or quaternion. If the Rotation was initialized with a [10, 3, 3] + rotation matrix tensor, for example, the resulting shape would be + [10]. + + Returns: + The virtual shape of the rotation object + """ + s = None + if self._quats is not None: + s = self._quats.shape[:-1] + else: + s = self._rot_mats.shape[:-2] + + return s + + @property + def dtype(self) -> torch.dtype: + """ + Returns the dtype of the underlying rotation. + Returns: + The dtype of the underlying rotation + """ + if self._rot_mats is not None: + return self._rot_mats.dtype + elif self._quats is not None: + return self._quats.dtype + else: + raise ValueError("Both rotations are None") + + @property + def device(self) -> torch.device: + """ + The device of the underlying rotation + Returns: + The device of the underlying rotation + """ + if self._rot_mats is not None: + return self._rot_mats.device + elif self._quats is not None: + return self._quats.device + else: + raise ValueError("Both rotations are None") + + @property + def requires_grad(self) -> bool: + """ + Returns the requires_grad property of the underlying rotation + Returns: + The requires_grad property of the underlying tensor + """ + if self._rot_mats is not None: + return self._rot_mats.requires_grad + elif self._quats is not None: + return self._quats.requires_grad + else: + raise ValueError("Both rotations are None") + + def get_rot_mats(self) -> torch.Tensor: + """ + Returns the underlying rotation as a rotation matrix tensor. + Returns: + The rotation as a rotation matrix tensor + """ + rot_mats = self._rot_mats + if rot_mats is None: + if self._quats is None: + raise ValueError("Both rotations are None") + else: + rot_mats = quat_to_rot(self._quats) + + return rot_mats + + def get_quats(self) -> torch.Tensor: + """ + Returns the underlying rotation as a quaternion tensor. + Depending on whether the Rotation was initialized with a + quaternion, this function may call torch.linalg.eigh. + Returns: + The rotation as a quaternion tensor. + """ + quats = self._quats + if quats is None: + if self._rot_mats is None: + raise ValueError("Both rotations are None") + else: + quats = rot_to_quat(self._rot_mats) + + return quats + + def get_cur_rot(self) -> torch.Tensor: + """ + Return the underlying rotation in its current form + Returns: + The stored rotation + """ + if self._rot_mats is not None: + return self._rot_mats + elif self._quats is not None: + return self._quats + else: + raise ValueError("Both rotations are None") + + # Rotation functions + + def compose_q_update_vec( + self, q_update_vec: torch.Tensor, normalize_quats: bool = True + ): + """ + Returns a new quaternion Rotation after updating the current + object's underlying rotation with a quaternion update, formatted + as a [*, 3] tensor whose final three columns represent x, y, z such + that (1, x, y, z) is the desired (not necessarily unit) quaternion + update. + Args: + q_update_vec: + A [*, 3] quaternion update tensor + normalize_quats: + Whether to normalize the output quaternion + Returns: + An updated Rotation + """ + quats = self.get_quats() + new_quats = quats + quat_multiply_by_vec(quats, q_update_vec) + return Rotation( + rot_mats=None, + quats=new_quats, + normalize_quats=normalize_quats, + ) + + def compose_r(self, r): + """ + Compose the rotation matrices of the current Rotation object with + those of another. + Args: + r: + An update rotation object + Returns: + An updated rotation object + """ + r1 = self.get_rot_mats() + r2 = r.get_rot_mats() + new_rot_mats = rot_matmul(r1, r2) + return Rotation(rot_mats=new_rot_mats, quats=None) + + def compose_q(self, r, normalize_quats: bool = True): + """ + Compose the quaternions of the current Rotation object with those + of another. + Depending on whether either Rotation was initialized with + quaternions, this function may call torch.linalg.eigh. + Args: + r: + An update rotation object + Returns: + An updated rotation object + """ + q1 = self.get_quats() + q2 = r.get_quats() + new_quats = quat_multiply(q1, q2) + return Rotation( + rot_mats=None, quats=new_quats, normalize_quats=normalize_quats + ) + + def apply(self, pts: torch.Tensor) -> torch.Tensor: + """ + Apply the current Rotation as a rotation matrix to a set of 3D + coordinates. + Args: + pts: + A [*, 3] set of points + Returns: + [*, 3] rotated points + """ + rot_mats = self.get_rot_mats() + return rot_vec_mul(rot_mats, pts) + + def invert_apply(self, pts: torch.Tensor) -> torch.Tensor: + """ + The inverse of the apply() method. + Args: + pts: + A [*, 3] set of points + Returns: + [*, 3] inverse-rotated points + """ + rot_mats = self.get_rot_mats() + inv_rot_mats = invert_rot_mat(rot_mats) + return rot_vec_mul(inv_rot_mats, pts) + + def invert(self): + """ + Returns the inverse of the current Rotation. + Returns: + The inverse of the current Rotation + """ + if self._rot_mats is not None: + return Rotation( + rot_mats=invert_rot_mat(self._rot_mats), quats=None + ) + elif self._quats is not None: + return Rotation( + rot_mats=None, + quats=invert_quat(self._quats), + normalize_quats=False, + ) + else: + raise ValueError("Both rotations are None") + + # "Tensor" stuff + + def unsqueeze( + self, + dim: int, + ): + """ + Analogous to torch.unsqueeze. The dimension is relative to the + shape of the Rotation object. + + Args: + dim: A positive or negative dimension index. + Returns: + The unsqueezed Rotation. + """ + if dim >= len(self.shape): + raise ValueError("Invalid dimension") + + if self._rot_mats is not None: + rot_mats = self._rot_mats.unsqueeze(dim if dim >= 0 else dim - 2) + return Rotation(rot_mats=rot_mats, quats=None) + elif self._quats is not None: + quats = self._quats.unsqueeze(dim if dim >= 0 else dim - 1) + return Rotation(rot_mats=None, quats=quats, normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + @staticmethod + def cat( + rs: Sequence[Any], + dim: int, + ): + """ + Concatenates rotations along one of the batch dimensions. Analogous + to torch.cat(). + Note that the output of this operation is always a rotation matrix, + regardless of the format of input rotations. + Args: + rs: + A list of rotation objects + dim: + The dimension along which the rotations should be + concatenated + Returns: + A concatenated Rotation object in rotation matrix format + """ + rot_mats = [r.get_rot_mats() for r in rs] + rot_mats = torch.cat(rot_mats, dim=dim if dim >= 0 else dim - 2) + + return Rotation(rot_mats=rot_mats, quats=None) + + def map_tensor_fn(self, fn: Callable[[torch.Tensor], torch.Tensor]): + """ + Apply a Tensor -> Tensor function to underlying rotation tensors, + mapping over the rotation dimension(s). Can be used e.g. to sum out + a one-hot batch dimension. + Args: + fn: + A Tensor -> Tensor function to be mapped over the Rotation + Returns: + The transformed Rotation object + """ + if self._rot_mats is not None: + rot_mats = self._rot_mats.view(self._rot_mats.shape[:-2] + (9,)) + rot_mats = torch.stack( + list(map(fn, torch.unbind(rot_mats, dim=-1))), dim=-1 + ) + rot_mats = rot_mats.view(rot_mats.shape[:-1] + (3, 3)) + return Rotation(rot_mats=rot_mats, quats=None) + elif self._quats is not None: + quats = torch.stack( + list(map(fn, torch.unbind(self._quats, dim=-1))), dim=-1 + ) + return Rotation(rot_mats=None, quats=quats, normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + def cuda(self): + """ + Analogous to the cuda() method of torch Tensors + Returns: + A copy of the Rotation in CUDA memory + """ + if self._rot_mats is not None: + return Rotation(rot_mats=self._rot_mats.cuda(), quats=None) + elif self._quats is not None: + return Rotation( + rot_mats=None, quats=self._quats.cuda(), normalize_quats=False + ) + else: + raise ValueError("Both rotations are None") + + def to(self, device: Optional[torch.device], dtype: Optional[torch.dtype]): + """ + Analogous to the to() method of torch Tensors + Args: + device: + A torch device + dtype: + A torch dtype + Returns: + A copy of the Rotation using the new device and dtype + """ + if self._rot_mats is not None: + return Rotation( + rot_mats=self._rot_mats.to(device=device, dtype=dtype), + quats=None, + ) + elif self._quats is not None: + return Rotation( + rot_mats=None, + quats=self._quats.to(device=device, dtype=dtype), + normalize_quats=False, + ) + else: + raise ValueError("Both rotations are None") + + def detach(self): + """ + Returns a copy of the Rotation whose underlying Tensor has been + detached from its torch graph. + Returns: + A copy of the Rotation whose underlying Tensor has been detached + from its torch graph + """ + if self._rot_mats is not None: + return Rotation(rot_mats=self._rot_mats.detach(), quats=None) + elif self._quats is not None: + return Rotation( + rot_mats=None, + quats=self._quats.detach(), + normalize_quats=False, + ) + else: + raise ValueError("Both rotations are None") + + +class Rigid: + """ + A class representing a rigid transformation. Little more than a wrapper + around two objects: a Rotation object and a [*, 3] translation + Designed to behave approximately like a single torch tensor with the + shape of the shared batch dimensions of its component parts. + + :param rots: A [*, 3, 3] rotation tensor + :type rots: Optional[Rotation] + :param trans: A corresponding [*, 3] translation tensor + :type trans: Optional[torch.Tensor] + """ + + def __init__( + self, + rots: Optional[Rotation], + trans: Optional[torch.Tensor], + ): + # (we need device, dtype, etc. from at least one input) + + batch_dims, dtype, device, requires_grad = None, None, None, None + if trans is not None: + batch_dims = trans.shape[:-1] + dtype = trans.dtype + device = trans.device + requires_grad = trans.requires_grad + elif rots is not None: + batch_dims = rots.shape + dtype = rots.dtype + device = rots.device + requires_grad = rots.requires_grad + else: + raise ValueError("At least one input argument must be specified") + + if rots is None: + rots = Rotation.identity( + batch_dims, + dtype, + device, + requires_grad, + ) + elif trans is None: + trans = identity_trans( + batch_dims, + dtype, + device, + requires_grad, + ) + + if (rots.shape != trans.shape[:-1]) or (rots.device != trans.device): + raise ValueError("Rots and trans incompatible") + + # Force full precision. Happens to the rotations automatically. + trans = trans.to(dtype=torch.float32) + + self._rots = rots + self._trans = trans + + @staticmethod + def identity( + shape: Tuple[int], + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, + fmt: str = "quat", + ): + """ + Constructs an identity transformation. + Args: + shape: + The desired shape + dtype: + The dtype of both internal tensors + device: + The device of both internal tensors + requires_grad: + Whether grad should be enabled for the internal tensors + Returns: + The identity transformation + """ + return Rigid( + Rotation.identity(shape, dtype, device, requires_grad, fmt=fmt), + identity_trans(shape, dtype, device, requires_grad), + ) + + def __getitem__( + self, + index: Any, + ): + """ + Indexes the affine transformation with PyTorch-style indices. + The index is applied to the shared dimensions of both the rotation + and the translation. + E.g.:: + r = Rotation(rot_mats=torch.rand(10, 10, 3, 3), quats=None) + t = Rigid(r, torch.rand(10, 10, 3)) + indexed = t[3, 4:6] + assert(indexed.shape == (2,)) + assert(indexed.get_rots().shape == (2,)) + assert(indexed.get_trans().shape == (2, 3)) + Args: + index: A standard torch tensor index. E.g. 8, (10, None, 3), + or (3, slice(0, 1, None)) + Returns: + The indexed tensor + """ + if type(index) != tuple: + index = (index,) + + return Rigid( + self._rots[index], + self._trans[index + (slice(None),)], + ) + + def __mul__( + self, + right: torch.Tensor, + ): + """ + Pointwise left multiplication of the transformation with a tensor. + Can be used to e.g. mask the Rigid. + Args: + right: + The tensor multiplicand + Returns: + The product + """ + if not (isinstance(right, torch.Tensor)): + raise TypeError("The other multiplicand must be a Tensor") + + new_rots = self._rots * right + new_trans = self._trans * right[..., None] + + return Rigid(new_rots, new_trans) + + def __rmul__( + self, + left: torch.Tensor, + ): + """ + Reverse pointwise multiplication of the transformation with a + tensor. + Args: + left: + The left multiplicand + Returns: + The product + """ + return self.__mul__(left) + + @property + def shape(self) -> torch.Size: + """ + Returns the shape of the shared dimensions of the rotation and + the translation. + + Returns: + The shape of the transformation + """ + s = self._trans.shape[:-1] + return s + + @property + def device(self) -> torch.device: + """ + Returns the device on which the Rigid's tensors are located. + Returns: + The device on which the Rigid's tensors are located + """ + return self._trans.device + + def get_rots(self) -> Rotation: + """ + Getter for the rotation. + Returns: + The rotation object + """ + return self._rots + + def get_trans(self) -> torch.Tensor: + """ + Getter for the translation. + Returns: + The stored translation + """ + return self._trans + + def compose_q_update_vec( + self, + q_update_vec: torch.Tensor, + ): + """ + Composes the transformation with a quaternion update vector of + shape [*, 6], where the final 6 columns represent the x, y, and + z values of a quaternion of form (1, x, y, z) followed by a 3D + translation. + Args: + q_vec: The quaternion update vector. + Returns: + The composed transformation. + """ + q_vec, t_vec = q_update_vec[..., :3], q_update_vec[..., 3:] + new_rots = self._rots.compose_q_update_vec(q_vec) + + trans_update = self._rots.apply(t_vec) + new_translation = self._trans + trans_update + + return Rigid(new_rots, new_translation) + + def compose( + self, + r, + ): + """ + Composes the current rigid object with another. + Args: + r: + Another Rigid object + Returns: + The composition of the two transformations + """ + new_rot = self._rots.compose_r(r._rots) + new_trans = self._rots.apply(r._trans) + self._trans + return Rigid(new_rot, new_trans) + + def apply( + self, + pts: torch.Tensor, + ) -> torch.Tensor: + """ + Applies the transformation to a coordinate tensor. + Args: + pts: A [*, 3] coordinate tensor. + Returns: + The transformed points. + """ + rotated = self._rots.apply(pts) + return rotated + self._trans + + def invert_apply(self, pts: torch.Tensor) -> torch.Tensor: + """ + Applies the inverse of the transformation to a coordinate tensor. + Args: + pts: A [*, 3] coordinate tensor + Returns: + The transformed points. + """ + pts = pts - self._trans + return self._rots.invert_apply(pts) + + def invert(self): + """ + Inverts the transformation. + Returns: + The inverse transformation. + """ + rot_inv = self._rots.invert() + trn_inv = rot_inv.apply(self._trans) + + return Rigid(rot_inv, -1 * trn_inv) + + def map_tensor_fn(self, fn: Callable[[torch.Tensor], torch.Tensor]): + """ + Apply a Tensor -> Tensor function to underlying translation and + rotation tensors, mapping over the translation/rotation dimensions + respectively. + Args: + fn: + A Tensor -> Tensor function to be mapped over the Rigid + Returns: + The transformed Rigid object + """ + new_rots = self._rots.map_tensor_fn(fn) + new_trans = torch.stack( + list(map(fn, torch.unbind(self._trans, dim=-1))), dim=-1 + ) + + return Rigid(new_rots, new_trans) + + def to_tensor_4x4(self) -> torch.Tensor: + """ + Converts a transformation to a homogenous transformation tensor. + Returns: + A [*, 4, 4] homogenous transformation tensor + """ + tensor = self._trans.new_zeros((*self.shape, 4, 4)) + tensor[..., :3, :3] = self._rots.get_rot_mats() + tensor[..., :3, 3] = self._trans + tensor[..., 3, 3] = 1 + return tensor + + @staticmethod + def from_tensor_4x4(t: torch.Tensor): + """ + Constructs a transformation from a homogenous transformation + tensor. + Args: + t: [*, 4, 4] homogenous transformation tensor + Returns: + T object with shape [*] + """ + if t.shape[-2:] != (4, 4): + raise ValueError("Incorrectly shaped input tensor") + + rots = Rotation(rot_mats=t[..., :3, :3], quats=None) + trans = t[..., :3, 3] + + return Rigid(rots, trans) + + def to_tensor_7(self) -> torch.Tensor: + """ + Converts a transformation to a tensor with 7 final columns, four + for the quaternion followed by three for the translation. + Returns: + A [*, 7] tensor representation of the transformation + """ + tensor = self._trans.new_zeros((*self.shape, 7)) + tensor[..., :4] = self._rots.get_quats() + tensor[..., 4:] = self._trans + + return tensor + + @staticmethod + def from_tensor_7( + t: torch.Tensor, + normalize_quats: bool = False, + ): + if t.shape[-1] != 7: + raise ValueError("Incorrectly shaped input tensor") + + quats, trans = t[..., :4], t[..., 4:] + + rots = Rotation( + rot_mats=None, quats=quats, normalize_quats=normalize_quats + ) + + return Rigid(rots, trans) + + @staticmethod + def from_3_points( + p_neg_x_axis: torch.Tensor, + origin: torch.Tensor, + p_xy_plane: torch.Tensor, + eps: float = 1e-8, + fixed=True, + ): + """ + Implements algorithm 21. Constructs transformations from sets of 3 + points using the Gram-Schmidt algorithm. + Args: + p_neg_x_axis: [*, 3] coordinates + origin: [*, 3] coordinates used as frame origins + p_xy_plane: [*, 3] coordinates + eps: Small epsilon value + Returns: + A transformation object of shape [*] + """ + + # THIS FUNCTION IS WRONG! IT DOES NOT CORRECTLY IMPLEMENT ALGORITHM 21. + # THE MISTAKE COMES FROM CREATING e0 FROM origin AND p_neg_x_axis AND + # FROM CREATING e1 FROM p_xy_plane AND origin. e0 SHOULD USE origin AND + # p_xy_plane AND e1 SHOULD USE origin and p_neg_x_axis. + + p_neg_x_axis = torch.unbind(p_neg_x_axis, dim=-1) + origin = torch.unbind(origin, dim=-1) + p_xy_plane = torch.unbind(p_xy_plane, dim=-1) + + if fixed: + e0 = [c1 - c2 for c1, c2 in zip(p_xy_plane, origin)] + e1 = [c1 - c2 for c1, c2 in zip(p_neg_x_axis, origin)] + else: + e0 = [c1 - c2 for c1, c2 in zip(origin, p_neg_x_axis)] + e1 = [c1 - c2 for c1, c2 in zip(p_xy_plane, origin)] + + denom = torch.sqrt(sum((c * c for c in e0)) + eps) + e0 = [c / denom for c in e0] + dot = sum((c1 * c2 for c1, c2 in zip(e0, e1))) + e1 = [c2 - c1 * dot for c1, c2 in zip(e0, e1)] + denom = torch.sqrt(sum((c * c for c in e1)) + eps) + e1 = [c / denom for c in e1] + e2 = [ + e0[1] * e1[2] - e0[2] * e1[1], + e0[2] * e1[0] - e0[0] * e1[2], + e0[0] * e1[1] - e0[1] * e1[0], + ] + + rots = torch.stack([c for tup in zip(e0, e1, e2) for c in tup], dim=-1) + rots = rots.reshape(rots.shape[:-1] + (3, 3)) + + rot_obj = Rotation(rot_mats=rots, quats=None) + + return Rigid(rot_obj, torch.stack(origin, dim=-1)) + + def unsqueeze( + self, + dim: int, + ): + """ + Analogous to torch.unsqueeze. The dimension is relative to the + shared dimensions of the rotation/translation. + + Args: + dim: A positive or negative dimension index. + Returns: + The unsqueezed transformation. + """ + if dim >= len(self.shape): + raise ValueError("Invalid dimension") + rots = self._rots.unsqueeze(dim) + trans = self._trans.unsqueeze(dim if dim >= 0 else dim - 1) + + return Rigid(rots, trans) + + @staticmethod + def cat( + ts: Sequence[Any], + dim: int, + ): + """ + Concatenates transformations along a new dimension. + Args: + ts: + A list of T objects + dim: + The dimension along which the transformations should be + concatenated + Returns: + A concatenated transformation object + """ + rots = Rotation.cat([t._rots for t in ts], dim) + trans = torch.cat( + [t._trans for t in ts], dim=dim if dim >= 0 else dim - 1 + ) + + return Rigid(rots, trans) + + def apply_rot_fn(self, fn: Callable[[Rotation], Rotation]): + """ + Applies a Rotation -> Rotation function to the stored rotation + object. + Args: + fn: A function of type Rotation -> Rotation + Returns: + A transformation object with a transformed rotation. + """ + return Rigid(fn(self._rots), self._trans) + + def apply_trans_fn(self, fn: Callable[[torch.Tensor], torch.Tensor]): + """ + Applies a Tensor -> Tensor function to the stored translation. + Args: + fn: + A function of type Tensor -> Tensor to be applied to the + translation + Returns: + A transformation object with a transformed translation. + """ + return Rigid(self._rots, fn(self._trans)) + + def scale_translation(self, trans_scale_factor: float): + """ + Scales the translation by a constant factor. + Args: + trans_scale_factor: + The constant factor + Returns: + A transformation object with a scaled translation. + """ + fn = lambda t: t * trans_scale_factor # noqa: E731 + return self.apply_trans_fn(fn) + + def stop_rot_gradient(self): + """ + Detaches the underlying rotation object + Returns: + A transformation object with detached rotations + """ + fn = lambda r: r.detach() # noqa: E731 + return self.apply_rot_fn(fn) + + @staticmethod + def make_transform_from_reference(n_xyz, ca_xyz, c_xyz, eps=1e-20): + """ + Returns a transformation object from reference coordinates. + + Note that this method does not take care of symmetries. If you + provide the atom positions in the non-standard way, the N atom will + end up not at [-0.527250, 1.359329, 0.0] but instead at + [-0.527250, -1.359329, 0.0]. You need to take care of such cases in + your code. + + Args: + n_xyz: A [*, 3] tensor of nitrogen xyz coordinates. + ca_xyz: A [*, 3] tensor of carbon alpha xyz coordinates. + c_xyz: A [*, 3] tensor of carbon xyz coordinates. + Returns: + A transformation object. After applying the translation and + rotation to the reference backbone, the coordinates will + approximately equal to the input coordinates. + """ + translation = -1 * ca_xyz + n_xyz = n_xyz + translation + c_xyz = c_xyz + translation + + c_x, c_y, c_z = [c_xyz[..., i] for i in range(3)] + norm = torch.sqrt(eps + c_x**2 + c_y**2) + sin_c1 = -c_y / norm + cos_c1 = c_x / norm + + c1_rots = sin_c1.new_zeros((*sin_c1.shape, 3, 3)) + c1_rots[..., 0, 0] = cos_c1 + c1_rots[..., 0, 1] = -1 * sin_c1 + c1_rots[..., 1, 0] = sin_c1 + c1_rots[..., 1, 1] = cos_c1 + c1_rots[..., 2, 2] = 1 + + norm = torch.sqrt(eps + c_x**2 + c_y**2 + c_z**2) + sin_c2 = c_z / norm + cos_c2 = torch.sqrt(c_x**2 + c_y**2) / norm + + c2_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3)) + c2_rots[..., 0, 0] = cos_c2 + c2_rots[..., 0, 2] = sin_c2 + c2_rots[..., 1, 1] = 1 + c2_rots[..., 2, 0] = -1 * sin_c2 + c2_rots[..., 2, 2] = cos_c2 + + c_rots = rot_matmul(c2_rots, c1_rots) + n_xyz = rot_vec_mul(c_rots, n_xyz) + + _, n_y, n_z = [n_xyz[..., i] for i in range(3)] + norm = torch.sqrt(eps + n_y**2 + n_z**2) + sin_n = -n_z / norm + cos_n = n_y / norm + + n_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3)) + n_rots[..., 0, 0] = 1 + n_rots[..., 1, 1] = cos_n + n_rots[..., 1, 2] = -1 * sin_n + n_rots[..., 2, 1] = sin_n + n_rots[..., 2, 2] = cos_n + + rots = rot_matmul(n_rots, c_rots) + + rots = rots.transpose(-1, -2) + translation = -1 * translation + + rot_obj = Rotation(rot_mats=rots, quats=None) + + return Rigid(rot_obj, translation) + + def cuda(self): + """ + Moves the transformation object to GPU memory + + Returns: + A version of the transformation on GPU + """ + return Rigid(self._rots.cuda(), self._trans.cuda()) diff --git a/proteinworkshop/models/graph_encoders/components/wrappers.py b/proteinworkshop/models/graph_encoders/components/wrappers.py index d12a1acb..3caae133 100644 --- a/proteinworkshop/models/graph_encoders/components/wrappers.py +++ b/proteinworkshop/models/graph_encoders/components/wrappers.py @@ -1,5 +1,5 @@ import torch -from beartype import beartype +from beartype import beartype as typechecker from jaxtyping import Bool, jaxtyped @@ -80,9 +80,8 @@ def repeat(self, n, c: int = 1, y: int = 1): def clone(self): return ScalarVector(self.scalar.clone(), self.vector.clone()) - @jaxtyped - @beartype - def mask(self, node_mask: Bool[torch.Tensor, "n_nodes"]): + @jaxtyped(typechecker=typechecker) + def mask(self, node_mask: Bool[torch.Tensor, " n_nodes"]): return ScalarVector( self.scalar * node_mask[:, None], self.vector * node_mask[:, None, None], diff --git a/proteinworkshop/models/graph_encoders/esm_embeddings.py b/proteinworkshop/models/graph_encoders/esm_embeddings.py index 882d1be2..8ab18118 100644 --- a/proteinworkshop/models/graph_encoders/esm_embeddings.py +++ b/proteinworkshop/models/graph_encoders/esm_embeddings.py @@ -1,23 +1,24 @@ """Modified from TorchDrug.""" -import esm import os -import torch +from typing import Dict, List, Optional, Set, Tuple, Union -from beartype import beartype -from torch_geometric.data import Batch -from torch_geometric.utils import to_dense_batch -from tqdm import tqdm -from typing import Dict, List, Optional, Union, Set, Tuple -from graphein.protein.tensor.data import ProteinBatch +import torch +from beartype import beartype as typechecker from graphein.protein.resi_atoms import RESI_THREE_TO_1 +from graphein.protein.tensor.data import ProteinBatch from loguru import logger from six.moves.urllib.request import urlretrieve -from proteinworkshop.models.utils import get_aggregation from torch import nn +from torch_geometric.data import Batch +from torch_geometric.utils import to_dense_batch +from tqdm import tqdm + +import esm +from proteinworkshop.models.utils import get_aggregation from proteinworkshop.types import EncoderOutput -@beartype +@typechecker def _compute_md5(file_name: str, chunk_size: int = 65536) -> str: """ Compute MD5 of the file. @@ -36,9 +37,12 @@ def _compute_md5(file_name: str, chunk_size: int = 65536) -> str: return md5.hexdigest() -@beartype +@typechecker def _download( - url: str, path: str, save_file: Optional[str] = None, md5: Optional[str] = None + url: str, + path: str, + save_file: Optional[str] = None, + md5: Optional[str] = None, ): """ Download a file from the specified url. @@ -55,7 +59,9 @@ def _download( save_file = save_file[: save_file.find("?")] save_file = os.path.join(path, save_file) - if not os.path.exists(save_file) or (md5 is not None and _compute_md5(save_file) != md5): + if not os.path.exists(save_file) or ( + md5 is not None and _compute_md5(save_file) != md5 + ): logger.info(f"Downloading {url} to {save_file}") urlretrieve(url, save_file) return save_file @@ -129,14 +135,14 @@ class EvolutionaryScaleModeling(nn.Module): max_input_length = 1024 - 2 def __init__( - self, - path: Union[str, os.PathLike], - model: str = "ESM-2-650M", - readout: str = "mean", - mlp_post_embed: bool = True, - dropout: float = 0.1, - finetune: bool = False - ): + self, + path: Union[str, os.PathLike], + model: str = "ESM-2-650M", + readout: str = "mean", + mlp_post_embed: bool = True, + dropout: float = 0.1, + finetune: bool = False, + ): super(EvolutionaryScaleModeling, self).__init__() path = os.path.expanduser(path) if not os.path.exists(path): @@ -160,24 +166,29 @@ def __init__( nn.ReLU(), nn.Dropout(dropout), ) - + if not self.finetune: self.model.eval() self.readout = get_aggregation(readout) + self.residue_map = RESI_THREE_TO_1 + self.residue_map["UNK"] = "" + @property - @beartype + @typechecker def required_batch_attributes(self) -> Set[str]: """ Return the requied attributes for each batch. - + :return: set of required attributes """ return {"residues", "id", "coords", "batch"} - @beartype - def load_weight(self, path: str, model: str) -> Tuple[nn.Module, esm.data.Alphabet]: + @typechecker + def load_weight( + self, path: str, model: str + ) -> Tuple[nn.Module, esm.data.Alphabet]: """ Load ESM model weights and their corresponding alphabet. @@ -192,7 +203,9 @@ def load_weight(self, path: str, model: str) -> Tuple[nn.Module, esm.data.Alphab if model != "ESM-1v" and not model.startswith("ESM-2"): regression_model = f"{model}-regression" regression_file = _download( - self.url[regression_model], path, md5=self.md5[regression_model] + self.url[regression_model], + path, + md5=self.md5[regression_model], ) regression_data = torch.load(regression_file, map_location="cpu") else: @@ -201,15 +214,22 @@ def load_weight(self, path: str, model: str) -> Tuple[nn.Module, esm.data.Alphab return esm.pretrained.load_model_and_alphabet_core( model_name, model_data, regression_data ) - - @beartype - def esm_embed(self, batch: Union[Batch, ProteinBatch], device: Optional[Union[torch.device, str]] = None) -> torch.Tensor: + + @typechecker + def esm_embed( + self, + batch: Union[Batch, ProteinBatch], + device: Optional[Union[torch.device, str]] = None, + ) -> torch.Tensor: """ Compute residue ESM embeddings for input proteins """ device = device if device is not None else batch.coords.device - seqs = ["".join([RESI_THREE_TO_1[s] for s in seq]) for seq in batch.residues] + seqs = [ + "".join([self.residue_map[s] for s in seq]) + for seq in batch.residues + ] seqs = ["".join(seq) for seq in seqs] data = list(tuple(zip(batch.id, seqs))) @@ -220,17 +240,23 @@ def esm_embed(self, batch: Union[Batch, ProteinBatch], device: Optional[Union[to node_embedding = output["representations"][self.repr_layer] # NOTE: tokens `0` and `N` are always beginning-of-sequence and end-of-sequence tokens, # so the first (real) residue is token `1` and the last is `N - 1`. - node_embedding = node_embedding[:, 1:node_embedding.shape[1] - 1, :] + node_embedding = node_embedding[:, 1 : node_embedding.shape[1] - 1, :] _, batch_mask = to_dense_batch( - x=torch.rand(batch.coords.shape[0], self.output_dim, device=device), + x=torch.rand( + batch.coords.shape[0], self.output_dim, device=device + ), batch=batch.batch, ) node_embedding = node_embedding[batch_mask] return node_embedding - @beartype - def forward(self, batch: Union[Batch, ProteinBatch], device: Optional[Union[torch.device, str]] = None) -> EncoderOutput: + @typechecker + def forward( + self, + batch: Union[Batch, ProteinBatch], + device: Optional[Union[torch.device, str]] = None, + ) -> EncoderOutput: """ Compute the residue representations and the graph representation(s). @@ -250,19 +276,19 @@ def forward(self, batch: Union[Batch, ProteinBatch], device: Optional[Union[torc if self.mlp_post_embed: # combine ESM embeddings with node features node_embedding = self.mlp( - torch.concatenate( - [node_embedding, batch.x], - dim=-1 - ) + torch.concatenate([node_embedding, batch.x], dim=-1) ) graph_embedding = self.readout(node_embedding, batch.batch) return EncoderOutput( - {"graph_embedding": graph_embedding, - "node_embedding": node_embedding} + { + "graph_embedding": graph_embedding, + "node_embedding": node_embedding, + } ) + if __name__ == "__main__": from proteinworkshop.datasets.utils import create_example_batch diff --git a/proteinworkshop/models/graph_encoders/gcpnet.py b/proteinworkshop/models/graph_encoders/gcpnet.py index 02e85c3d..35026c3f 100644 --- a/proteinworkshop/models/graph_encoders/gcpnet.py +++ b/proteinworkshop/models/graph_encoders/gcpnet.py @@ -4,7 +4,7 @@ import hydra import torch import torch.nn as nn -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import jaxtyped from omegaconf import DictConfig @@ -87,24 +87,14 @@ def __init__( self.predict_node_rep = module_cfg.predict_node_rep # Feature dimensionalities - edge_input_dims = ScalarVector( - model_cfg.e_input_dim, model_cfg.xi_input_dim - ) - node_input_dims = ScalarVector( - model_cfg.h_input_dim, model_cfg.chi_input_dim - ) - self.edge_dims = ScalarVector( - model_cfg.e_hidden_dim, model_cfg.xi_hidden_dim - ) - self.node_dims = ScalarVector( - model_cfg.h_hidden_dim, model_cfg.chi_hidden_dim - ) + edge_input_dims = ScalarVector(model_cfg.e_input_dim, model_cfg.xi_input_dim) + node_input_dims = ScalarVector(model_cfg.h_input_dim, model_cfg.chi_input_dim) + self.edge_dims = ScalarVector(model_cfg.e_hidden_dim, model_cfg.xi_hidden_dim) + self.node_dims = ScalarVector(model_cfg.h_hidden_dim, model_cfg.chi_hidden_dim) # Position-wise operations self.centralize = partial(centralize, key="pos") - self.localize = partial( - localize, norm_pos_diff=module_cfg.norm_pos_diff - ) + self.localize = partial(localize, norm_pos_diff=module_cfg.norm_pos_diff) self.decentralize = partial(decentralize, key="pos") # Input embeddings @@ -156,11 +146,10 @@ def __init__( def required_batch_attributes(self) -> List[str]: return ["edge_index", "pos", "x", "batch"] - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: """Implements the forward pass of the GCPNet encoder. - + Returns the node embedding and graph embedding in a dictionary. :param batch: Batch of data to encode. @@ -174,9 +163,7 @@ def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: :rtype: EncoderOutput """ # Centralize node positions to make them translation-invariant - pos_centroid, batch.pos = self.centralize( - batch, batch_index=batch.batch - ) + pos_centroid, batch.pos = self.centralize(batch, batch_index=batch.batch) # Install `h`, `chi`, `e`, and `xi` using corresponding features built by the `FeatureFactory` batch.h, batch.chi, batch.e, batch.xi = ( @@ -218,9 +205,7 @@ def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: _, centralized_node_pos = self.centralize( batch, batch_index=batch.batch ) - batch.f_ij = self.localize( - centralized_node_pos, batch.edge_index - ) + batch.f_ij = self.localize(centralized_node_pos, batch.edge_index) encoder_outputs["pos"] = batch.pos # (n, 3) -> (batch_size, 3) # Summarize intermediate node representations as final predictions diff --git a/proteinworkshop/models/graph_encoders/gnn.py b/proteinworkshop/models/graph_encoders/gnn.py index 4bfc3d79..3e067b87 100644 --- a/proteinworkshop/models/graph_encoders/gnn.py +++ b/proteinworkshop/models/graph_encoders/gnn.py @@ -1,7 +1,7 @@ from typing import List, Set, Union import torch.nn as nn -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import jaxtyped from torch_geometric.data import Batch @@ -166,8 +166,7 @@ def required_batch_attributes(self) -> Set[str]: else: return {"x", "edge_index", "batch"} - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: """Implements the forward pass of the GNN encoder. diff --git a/proteinworkshop/models/graph_encoders/gvp.py b/proteinworkshop/models/graph_encoders/gvp.py index 3aca7ef9..78dc26bb 100644 --- a/proteinworkshop/models/graph_encoders/gvp.py +++ b/proteinworkshop/models/graph_encoders/gvp.py @@ -2,7 +2,7 @@ import torch import torch.nn.functional as F -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import jaxtyped from torch_geometric.data import Batch @@ -129,8 +129,7 @@ def required_batch_attributes(self) -> Set[str]: """ return {"edge_index", "pos", "x", "batch"} - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: """Implements the forward pass of the GVP-GNN encoder. diff --git a/proteinworkshop/models/graph_encoders/identity.py b/proteinworkshop/models/graph_encoders/identity.py index 9aba9b1d..799ec542 100644 --- a/proteinworkshop/models/graph_encoders/identity.py +++ b/proteinworkshop/models/graph_encoders/identity.py @@ -1,7 +1,7 @@ from typing import Set, Union import torch.nn as nn -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import jaxtyped from torch_geometric.data import Batch @@ -33,8 +33,7 @@ def required_batch_attributes(self) -> Set[str]: """ return {"x", "batch"} - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: """Implements the forward pass of the IdentityModel encoder. diff --git a/proteinworkshop/models/graph_encoders/layers/cdconv.py b/proteinworkshop/models/graph_encoders/layers/cdconv.py new file mode 100644 index 00000000..a36c4f9a --- /dev/null +++ b/proteinworkshop/models/graph_encoders/layers/cdconv.py @@ -0,0 +1,394 @@ +""" +CDConv implementation adapted from the MIT-licensed source: +https://github.com/hehefan/Continuous-Discrete-Convolution/tree/main +""" + +import math +from typing import Optional + +import torch +import torch.nn as nn +import torch_sparse as sparse +from torch import Tensor +from torch_geometric.nn import radius +from torch_geometric.nn.conv import MessagePassing +from torch_geometric.typing import OptTensor +from torch_geometric.utils import add_self_loops, remove_self_loops +from torch_scatter import scatter_max, scatter_mean + + +class Linear(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + batch_norm: bool = True, + dropout: float = 0.0, + bias: bool = False, + leakyrelu_negative_slope: float = 0.1, + momentum: float = 0.2, + ) -> nn.Module: + super(Linear, self).__init__() + + module = [] + if batch_norm: + module.append(nn.BatchNorm1d(in_channels, momentum=momentum)) + module.append(nn.LeakyReLU(leakyrelu_negative_slope)) + module.append(nn.Dropout(dropout)) + module.append(nn.Linear(in_channels, out_channels, bias=bias)) + self.module = nn.Sequential(*module) + + def forward(self, x): + return self.module(x) + + +class MLP(nn.Module): + def __init__( + self, + in_channels: int, + mid_channels: int, + out_channels: int, + batch_norm: bool, + dropout: float = 0.0, + bias: bool = True, + leakyrelu_negative_slope: float = 0.2, + momentum: float = 0.2, + ) -> nn.Module: + super(MLP, self).__init__() + + module = [] + if batch_norm: + module.append(nn.BatchNorm1d(in_channels, momentum=momentum)) + module.append(nn.LeakyReLU(leakyrelu_negative_slope)) + module.append(nn.Dropout(dropout)) + if mid_channels is None: + module.append(nn.Linear(in_channels, out_channels, bias=bias)) + else: + module.append(nn.Linear(in_channels, mid_channels, bias=bias)) + if batch_norm: + if mid_channels is None: + module.append(nn.BatchNorm1d(out_channels, momentum=momentum)) + else: + module.append(nn.BatchNorm1d(mid_channels, momentum=momentum)) + module.append(nn.LeakyReLU(leakyrelu_negative_slope)) + if mid_channels is None: + module.append(nn.Dropout(dropout)) + else: + module.append(nn.Linear(mid_channels, out_channels, bias=bias)) + + self.module = nn.Sequential(*module) + + def forward(self, input): + return self.module(input) + + +class BasicBlock(nn.Module): + def __init__( + self, + r: float, + l: float, + kernel_channels: list[int], + in_channels: int, + out_channels: int, + base_width: float = 16.0, + batch_norm: bool = True, + dropout: float = 0.0, + bias: bool = False, + leakyrelu_negative_slope: float = 0.1, + momentum: float = 0.2, + ) -> nn.Module: + super(BasicBlock, self).__init__() + + if in_channels != out_channels: + self.identity = Linear( + in_channels=in_channels, + out_channels=out_channels, + batch_norm=batch_norm, + dropout=dropout, + bias=bias, + leakyrelu_negative_slope=leakyrelu_negative_slope, + momentum=momentum, + ) + else: + self.identity = nn.Sequential() + + width = int(out_channels * (base_width / 64.0)) + self.input = MLP( + in_channels=in_channels, + mid_channels=None, + out_channels=width, + batch_norm=batch_norm, + dropout=dropout, + bias=bias, + leakyrelu_negative_slope=leakyrelu_negative_slope, + momentum=momentum, + ) + self.conv = CDConv( + r=r, + l=l, + kernel_channels=kernel_channels, + in_channels=width, + out_channels=width, + ) + self.output = Linear( + in_channels=width, + out_channels=out_channels, + batch_norm=batch_norm, + dropout=dropout, + bias=bias, + leakyrelu_negative_slope=leakyrelu_negative_slope, + momentum=momentum, + ) + + def forward(self, x, pos, seq, ori, batch): + identity = self.identity(x) + x = self.input(x) + x = self.conv(x, pos, seq, ori, batch) + out = self.output(x) + identity + return out + + +class MaxPooling(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x, pos, seq, ori, batch): + idx = torch.div(seq.squeeze(1), 2, rounding_mode="floor") + idx = torch.cat([idx, idx[-1].view((1,))]) + + idx = (idx[0:-1] != idx[1:]).to(torch.float32) + idx = torch.cumsum(idx, dim=0) - idx + idx = idx.to(torch.int64) + x = scatter_max(src=x, index=idx, dim=0)[0] + pos = scatter_mean(src=pos, index=idx, dim=0) + seq = scatter_max( + src=torch.div(seq, 2, rounding_mode="floor"), index=idx, dim=0 + )[0] + ori = scatter_mean(src=ori, index=idx, dim=0) + ori = torch.nn.functional.normalize(ori, 2, -1) + batch = scatter_max(src=batch, index=idx, dim=0)[0] + + return x, pos, seq, ori, batch + + +class AvgPooling(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x, pos, seq, ori, batch): + idx = torch.div(seq.squeeze(1), 2, rounding_mode="floor") + idx = torch.cat([idx, idx[-1].view((1,))]) + + idx = (idx[0:-1] != idx[1:]).to(torch.float32) + idx = torch.cumsum(idx, dim=0) - idx + idx = idx.to(torch.int64) + x = scatter_mean(src=x, index=idx, dim=0) + pos = scatter_mean(src=pos, index=idx, dim=0) + seq = scatter_max( + src=torch.div(seq, 2, rounding_mode="floor"), index=idx, dim=0 + )[0] + ori = scatter_mean(src=ori, index=idx, dim=0) + ori = torch.nn.functional.normalize(ori, 2, -1) + batch = scatter_max(src=batch, index=idx, dim=0)[0] + + return x, pos, seq, ori, batch + + +def kaiming_uniform(tensor, size): + fan = 1 + for i in range(1, len(size)): + fan *= size[i] + gain = math.sqrt(2.0 / (1 + math.sqrt(5) ** 2)) + std = gain / math.sqrt(fan) + bound = ( + math.sqrt(3.0) * std + ) # Calculate uniform bounds from standard deviation + with torch.no_grad(): + return tensor.uniform_(-bound, bound) + + +class WeightNet(nn.Module): + def __init__(self, l: int, kernel_channels: list[int]): + super(WeightNet, self).__init__() + + self.l = l + self.kernel_channels = kernel_channels + + self.Ws = nn.ParameterList() + self.bs = nn.ParameterList() + + for i, channels in enumerate(kernel_channels): + if i == 0: + self.Ws.append( + torch.nn.Parameter(torch.empty(l, 3 + 3 + 1, channels)) + ) + self.bs.append(torch.nn.Parameter(torch.empty(l, channels))) + else: + self.Ws.append( + torch.nn.Parameter( + torch.empty(l, kernel_channels[i - 1], channels) + ) + ) + self.bs.append(torch.nn.Parameter(torch.empty(l, channels))) + + self.relu = nn.LeakyReLU(0.2) + + def reset_parameters(self): + for i, channels in enumerate(self.kernel_channels): + if i == 0: + kaiming_uniform( + self.Ws[0].data, size=[self.l, 3 + 3 + 1, channels] + ) + else: + kaiming_uniform( + self.Ws[i].data, + size=[self.l, self.kernel_channels[i - 1], channels], + ) + self.bs[i].data.fill_(0.0) + + def forward(self, input, idx): + for i in range(len(self.kernel_channels)): + W = torch.index_select(self.Ws[i], 0, idx) + b = torch.index_select(self.bs[i], 0, idx) + if i == 0: + weight = self.relu( + torch.bmm(input.unsqueeze(1), W).squeeze(1) + b + ) + else: + weight = self.relu( + torch.bmm(weight.unsqueeze(1), W).squeeze(1) + b + ) + + return weight + + +class CDConv(MessagePassing): + def __init__( + self, + r: float, + l: float, + kernel_channels: list[int], + in_channels: int, + out_channels: int, + add_self_loops: bool = True, + **kwargs, + ): + kwargs.setdefault("aggr", "sum") + super().__init__(**kwargs) + self.r = r + self.l = l + self.kernel_channels = kernel_channels + self.in_channels = in_channels + self.out_channels = out_channels + + self.WeightNet = WeightNet(l, kernel_channels) + self.W = torch.nn.Parameter( + torch.empty(kernel_channels[-1] * in_channels, out_channels) + ) + + self.add_self_loops = add_self_loops + + self.reset_parameters() + + def reset_parameters(self): + self.WeightNet.reset_parameters() + kaiming_uniform( + self.W.data, + size=[self.kernel_channels * self.in_channels, self.out_channels], + ) + + def forward( + self, + x: OptTensor, + pos: Tensor, + seq: Tensor, + ori: Tensor, + batch: Tensor, + ) -> Tensor: + row, col = radius( + pos, pos, self.r, batch, batch, max_num_neighbors=9999 + ) + edge_index = torch.stack([col, row], dim=0) + + if self.add_self_loops: + if isinstance(edge_index, Tensor): + edge_index, _ = remove_self_loops(edge_index) + edge_index, _ = add_self_loops( + edge_index, num_nodes=min(pos.size(0), pos.size(0)) + ) + + elif isinstance(edge_index, sparse.SparseTensor): + edge_index = sparse.set_diag(edge_index) + + out = self.propagate( + edge_index, + x=(x, None), + pos=(pos, pos), + seq=(seq, seq), + ori=(ori.reshape((-1, 9)), ori.reshape((-1, 9))), + size=None, + ) + out = torch.matmul(out, self.W) + + return out + + def message( + self, + x_j: Optional[Tensor], + pos_i: Tensor, + pos_j: Tensor, + seq_i: Tensor, + seq_j: Tensor, + ori_i: Tensor, + ori_j: Tensor, + ) -> Tensor: + # orientation + pos = pos_j - pos_i + distance = torch.norm(input=pos, p=2, dim=-1, keepdim=True) + pos /= distance + 1e-9 + + pos = torch.matmul( + ori_i.reshape((-1, 3, 3)), pos.unsqueeze(2) + ).squeeze(2) + ori = torch.sum( + input=ori_i.reshape((-1, 3, 3)) * ori_j.reshape((-1, 3, 3)), + dim=2, + keepdim=False, + ) + + # + normed_distance = distance / self.r + + seq = seq_j - seq_i + s = self.l // 2 + seq = torch.clamp(input=seq, min=-s, max=s) + seq_idx = (seq + s).squeeze(1).to(torch.int64) + normed_length = torch.abs(seq) / s + + # generated kernel weight: PointConv or PSTNet + delta = torch.cat([pos, ori, distance], dim=1) + kernel_weight = self.WeightNet(delta, seq_idx) + + # smooth: IEConv II + smooth = ( + 0.5 + - torch.tanh(normed_distance * normed_length * 16.0 - 14.0) * 0.5 + ) + + # convolution + msg = torch.matmul( + (kernel_weight * smooth).unsqueeze(2), x_j.unsqueeze(1) + ) + + msg = msg.reshape((-1, msg.size(1) * msg.size(2))) + + return msg + + def __repr__(self) -> str: + return ( + f"{self.__class__.__name__}(r={self.r}, " + f"l={self.l}," + f"kernel_channels={self.kernel_channels}," + f"in_channels={self.in_channels}," + f"out_channels={self.out_channels})" + ) diff --git a/proteinworkshop/models/graph_encoders/layers/gcp.py b/proteinworkshop/models/graph_encoders/layers/gcp.py index 7f823b7f..1644f13c 100644 --- a/proteinworkshop/models/graph_encoders/layers/gcp.py +++ b/proteinworkshop/models/graph_encoders/layers/gcp.py @@ -18,7 +18,7 @@ import torch import torch_scatter -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import Bool, Float, Int64, jaxtyped from omegaconf import DictConfig, OmegaConf @@ -45,8 +45,7 @@ def __init__(self, drop_rate: float): super().__init__() self.drop_rate = drop_rate - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, x: torch.Tensor) -> torch.Tensor: """ :param x: `torch.Tensor` corresponding to vector channels @@ -75,8 +74,7 @@ def __init__(self, drop_rate: float, use_gcp_dropout: bool = True): VectorDropout(drop_rate) if use_gcp_dropout else nn.Identity() ) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, x: Union[torch.Tensor, ScalarVector] ) -> Union[torch.Tensor, ScalarVector]: @@ -88,9 +86,7 @@ def forward( return x elif isinstance(x, torch.Tensor): return self.scalar_dropout(x) - return ScalarVector( - self.scalar_dropout(x[0]), self.vector_dropout(x[1]) - ) + return ScalarVector(self.scalar_dropout(x[0]), self.vector_dropout(x[1])) class GCPLayerNorm(nn.Module): @@ -110,8 +106,7 @@ def __init__( self.eps = eps @staticmethod - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def norm_vector( v: torch.Tensor, use_gcp_norm: bool = True, eps: float = 1e-8 ) -> torch.Tensor: @@ -120,14 +115,11 @@ def norm_vector( vector_norm = torch.clamp( torch.sum(torch.square(v), dim=-1, keepdim=True), min=eps ) - vector_norm = torch.sqrt( - torch.mean(vector_norm, dim=-2, keepdim=True) - ) + vector_norm = torch.sqrt(torch.mean(vector_norm, dim=-2, keepdim=True)) v_norm = v / vector_norm return v_norm - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, x: Union[torch.Tensor, ScalarVector] ) -> Union[torch.Tensor, ScalarVector]: @@ -189,9 +181,7 @@ def __init__( else max(self.vector_input_dim, self.vector_output_dim) ) - scalar_vector_frame_dim = ( - scalarization_vectorization_output_dim * 3 - ) + scalar_vector_frame_dim = scalarization_vectorization_output_dim * 3 self.vector_down = nn.Linear( self.vector_input_dim, self.hidden_dim, bias=False ) @@ -208,9 +198,7 @@ def __init__( ) if feedforward_out else nn.Linear( - self.hidden_dim - + self.scalar_input_dim - + scalar_vector_frame_dim, + self.hidden_dim + self.scalar_input_dim + scalar_vector_frame_dim, self.scalar_output_dim, ) ) @@ -240,13 +228,10 @@ def __init__( else nn.Linear(self.scalar_input_dim, self.scalar_output_dim) ) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def create_zero_vector( self, - scalar_rep: Float[ - torch.Tensor, "batch_num_entities merged_scalar_dim" - ], + scalar_rep: Float[torch.Tensor, "batch_num_entities merged_scalar_dim"], ) -> Float[torch.Tensor, "batch_num_entities o 3"]: return torch.zeros( scalar_rep.shape[0], @@ -256,8 +241,7 @@ def create_zero_vector( ) @staticmethod - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def scalarize( vector_rep: Float[torch.Tensor, "batch_num_entities 3 3"], edge_index: Int64[torch.Tensor, "2 batch_num_edges"], @@ -289,23 +273,17 @@ def scalarize( ) local_scalar_rep_i = local_scalar_rep_i.transpose(-1, -2) else: - local_scalar_rep_i = torch.matmul(frames, vector_rep_i).transpose( - -1, -2 - ) + local_scalar_rep_i = torch.matmul(frames, vector_rep_i).transpose(-1, -2) # potentially enable E(3)-equivariance and, thereby, chirality-invariance if enable_e3_equivariance: # avoid corrupting gradients with an in-place operation local_scalar_rep_i_copy = local_scalar_rep_i.clone() - local_scalar_rep_i_copy[:, :, 1] = torch.abs( - local_scalar_rep_i[:, :, 1] - ) + local_scalar_rep_i_copy[:, :, 1] = torch.abs(local_scalar_rep_i[:, :, 1]) local_scalar_rep_i = local_scalar_rep_i_copy # reshape frame-derived geometric scalars - local_scalar_rep_i = local_scalar_rep_i.reshape( - vector_rep_i.shape[0], 9 - ) + local_scalar_rep_i = local_scalar_rep_i.reshape(vector_rep_i.shape[0], 9) if node_inputs: # for node inputs, summarize all edge-wise geometric scalars using an average @@ -320,13 +298,10 @@ def scalarize( return local_scalar_rep_i - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def vectorize( self, - scalar_rep: Float[ - torch.Tensor, "batch_num_entities merged_scalar_dim" - ], + scalar_rep: Float[torch.Tensor, "batch_num_entities merged_scalar_dim"], vector_hidden_rep: Float[torch.Tensor, "batch_num_entities 3 n"], ) -> Float[torch.Tensor, "batch_num_entities o 3"]: vector_rep = self.vector_up(vector_hidden_rep) @@ -342,8 +317,7 @@ def vectorize( return vector_rep - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, s_maybe_v: Union[ @@ -462,8 +436,7 @@ def __init__( enable_e3_equivariance=cfg.enable_e3_equivariance, ) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, batch: Union[Batch, ProteinBatch] ) -> Tuple[ @@ -494,21 +467,15 @@ def forward( ) # [n_edges, 3] edge_lengths = torch.linalg.norm(edge_vectors, dim=-1) # [n_edges, 1] edge_rep = ScalarVector( - torch.cat( - (edge_rep.scalar, self.radial_embedding(edge_lengths)), dim=-1 - ), + torch.cat((edge_rep.scalar, self.radial_embedding(edge_lengths)), dim=-1), edge_rep.vector, ) edge_rep = ( - edge_rep.scalar - if not self.edge_embedding.vector_input_dim - else edge_rep + edge_rep.scalar if not self.edge_embedding.vector_input_dim else edge_rep ) node_rep = ( - node_rep.scalar - if not self.node_embedding.vector_input_dim - else node_rep + node_rep.scalar if not self.node_embedding.vector_input_dim else node_rep ) if self.pre_norm: @@ -537,7 +504,7 @@ def forward( return node_rep, edge_rep -@beartype +@typechecker def get_GCP_with_custom_cfg( input_dims: Any, output_dims: Any, cfg: DictConfig, **kwargs ): @@ -621,8 +588,7 @@ def __init__( nn.Linear(output_dims.scalar, 1), nn.Sigmoid() ) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def message( self, node_rep: ScalarVector, @@ -674,8 +640,7 @@ def message( return message_residual.flatten() - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def aggregate( self, message: Float[torch.Tensor, "batch_num_edges message_dim"], @@ -688,8 +653,7 @@ def aggregate( ) return aggregate - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, node_rep: ScalarVector, @@ -723,9 +687,7 @@ def __init__( if nonlinearities is None: nonlinearities = cfg.nonlinearities self.pre_norm = layer_cfg.pre_norm - self.predict_node_positions = getattr( - cfg, "predict_node_positions", False - ) + self.predict_node_positions = getattr(cfg, "predict_node_positions", False) self.node_positions_weight = getattr(cfg, "node_positions_weight", 1.0) self.update_positions_with_vector_sum = getattr( cfg, "update_positions_with_vector_sum", False @@ -815,8 +777,7 @@ def __init__( enable_e3_equivariance=cfg.enable_e3_equivariance, ) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def derive_x_update( self, node_rep: ScalarVector, @@ -838,8 +799,7 @@ def derive_x_update( return x_update - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, node_rep: Tuple[ diff --git a/proteinworkshop/models/graph_encoders/mace.py b/proteinworkshop/models/graph_encoders/mace.py index f1d0de6f..5b99180d 100644 --- a/proteinworkshop/models/graph_encoders/mace.py +++ b/proteinworkshop/models/graph_encoders/mace.py @@ -3,7 +3,7 @@ import e3nn import torch import torch.nn.functional as F -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import jaxtyped from torch_geometric.data import Batch @@ -206,8 +206,7 @@ def required_batch_attributes(self) -> Set[str]: """ return {"edge_index", "pos", "x", "batch"} - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: """Implements the forward pass of the MACE encoder. diff --git a/proteinworkshop/models/graph_encoders/tfn.py b/proteinworkshop/models/graph_encoders/tfn.py index d23e845c..38f8caa4 100644 --- a/proteinworkshop/models/graph_encoders/tfn.py +++ b/proteinworkshop/models/graph_encoders/tfn.py @@ -4,7 +4,7 @@ import e3nn import torch import torch.nn.functional as F -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import jaxtyped from torch_geometric.data import Batch @@ -155,8 +155,7 @@ def required_batch_attributes(self) -> Set[str]: """ return {"edge_index", "pos", "x", "batch"} - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: """Implements the forward pass of the TFN encoder. diff --git a/proteinworkshop/models/utils.py b/proteinworkshop/models/utils.py index 88bb2d45..d8f6f4fd 100644 --- a/proteinworkshop/models/utils.py +++ b/proteinworkshop/models/utils.py @@ -4,7 +4,7 @@ import torch.nn as nn import torch.nn.functional as F import torch_scatter -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import Bool, Float, Int64, jaxtyped from omegaconf import DictConfig @@ -169,17 +169,16 @@ def get_loss( raise ValueError(f"Incorrect Loss provided: {name}") -def flatten_list(l: List[List]) -> List: +def flatten_list(l: List[List]) -> List: # noqa: E741 return [item for sublist in l for item in sublist] -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def centralize( batch: Union[Batch, ProteinBatch], key: str, batch_index: torch.Tensor, - node_mask: Optional[Bool[torch.Tensor, "n_nodes"]] = None, + node_mask: Optional[Bool[torch.Tensor, " n_nodes"]] = None, ) -> Tuple[ torch.Tensor, torch.Tensor ]: # note: cannot make assumptions on output shape @@ -210,14 +209,13 @@ def centralize( return entities_centroid, entities_centered -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def decentralize( batch: Union[Batch, ProteinBatch], key: str, batch_index: torch.Tensor, entities_centroid: torch.Tensor, - node_mask: Optional[Bool[torch.Tensor, "n_nodes"]] = None, + node_mask: Optional[Bool[torch.Tensor, " n_nodes"]] = None, ) -> torch.Tensor: # note: cannot make assumptions on output shape if node_mask is not None: masked_values = torch.ones_like(batch[key]) * torch.inf @@ -230,13 +228,12 @@ def decentralize( return entities_centered -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def localize( pos: Float[torch.Tensor, "batch_num_nodes 3"], edge_index: Int64[torch.Tensor, "2 batch_num_edges"], norm_pos_diff: bool = True, - node_mask: Optional[Bool[torch.Tensor, "n_nodes"]] = None, + node_mask: Optional[Bool[torch.Tensor, " n_nodes"]] = None, ) -> Float[torch.Tensor, "batch_num_edges 3 3"]: row, col = edge_index[0], edge_index[1] @@ -311,8 +308,7 @@ def localize( return f_ij -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def safe_norm( x: torch.Tensor, dim: int = -1, @@ -326,8 +322,7 @@ def safe_norm( return norm + eps -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def is_identity( nonlinearity: Optional[Union[Callable, nn.Module]] = None ) -> bool: diff --git a/proteinworkshop/scripts/cli.py b/proteinworkshop/scripts/cli.py index c462d99c..70889348 100644 --- a/proteinworkshop/scripts/cli.py +++ b/proteinworkshop/scripts/cli.py @@ -71,6 +71,17 @@ def main(): help="Additional arguments passed to the finetune script.", ) + # ... explain sub-command + explain_parser = subparsers.add_parser( + "explain", + help="explain a model. See proteinworkshop/explain.py for more details.", + ) + explain_parser.add_argument( + "explain_args", + nargs="*", + help="Additional arguments passed to the explain script.", + ) + # ... embed sub-command embed_parser = subparsers.add_parser( "embed", @@ -136,6 +147,11 @@ def main(): finetune_main(args.finetune_args) + elif args.command == "explain": + from proteinworkshop.explain import _script_main as explain_main + + explain_main(args.explain_args) + elif args.command == "embed": from proteinworkshop.embed import _script_main as embed_main @@ -148,7 +164,8 @@ def main(): else: _valid = "\n\t".join( - ["\n\tinstall", "\tdownload", "\ttrain", "\tfinetune", "\tembed", "\tvisualise"] + ["\n\tinstall", "\tdownload", "\ttrain", "\tfinetune", "\tembed", "\tvisualise", "\texplain"] + ) if args.command is None: raise ValueError(f"Missing command. Valid commands are: {_valid}") diff --git a/proteinworkshop/scripts/download_foldcomp.py b/proteinworkshop/scripts/download_foldcomp.py index f35ae196..e62a030e 100644 --- a/proteinworkshop/scripts/download_foldcomp.py +++ b/proteinworkshop/scripts/download_foldcomp.py @@ -1,7 +1,6 @@ import argparse import os import pathlib -import shutil from typing import Optional import foldcomp diff --git a/proteinworkshop/scripts/download_processed_data.py b/proteinworkshop/scripts/download_processed_data.py index 303c3568..1fa3bb97 100644 --- a/proteinworkshop/scripts/download_processed_data.py +++ b/proteinworkshop/scripts/download_processed_data.py @@ -102,7 +102,7 @@ def download_processed_data(dataset_name: str, data_dir: Optional[str] = None): logger.info( f"Downloading {dataset_name} dataset from Zenodo Record {_ZENODO_RECORD}" ) - zenodo_url = f"https://zenodo.org/record/{_ZENODO_RECORD}/files/{fname}.tar.gz?download=1" + zenodo_url = f"https://zenodo.org/records/{_ZENODO_RECORD}/files/{fname}.tar.gz?download=1" wget.download(zenodo_url, out=str(data_dir)) else: logger.info( diff --git a/proteinworkshop/scripts/install_pyg.py b/proteinworkshop/scripts/install_pyg.py index cb3928c8..4ef97b46 100644 --- a/proteinworkshop/scripts/install_pyg.py +++ b/proteinworkshop/scripts/install_pyg.py @@ -26,6 +26,8 @@ def _install_pyg(force_reinstall: bool = False): # torch_version = "2.0.0" if cuda_version == "cu116": raise ValueError("PyTorch 2.0.0 does not support CUDA 11.6") + if torch_version.startswith("2.1"): + torch_version = "2.1.0" if torch_version.startswith("1.13"): if cuda_version == "cu118": diff --git a/proteinworkshop/tasks/multihot_label_encoding.py b/proteinworkshop/tasks/multihot_label_encoding.py new file mode 100644 index 00000000..cf72e653 --- /dev/null +++ b/proteinworkshop/tasks/multihot_label_encoding.py @@ -0,0 +1,27 @@ +from typing import Set, Union + +import torch +from graphein.protein.tensor.data import Protein +from torch_geometric import transforms as T +from torch_geometric.data import Data + + +class MultiHotLabelEncoding(T.BaseTransform): + """ + Transform to multihot encode labels for multilabel classification. + + :param num_classes: Number of classes to encode. + :type num_classes: int + """ + + def __init__(self, num_classes: int) -> None: + self.num_classes = num_classes + + def required_batch_attributes(self) -> Set[str]: + return {"graph_y"} + + def __call__(self, data: Union[Protein, Data]) -> Union[Protein, Data]: + labels = torch.zeros((1, self.num_classes)) + labels[:, data.graph_y] = 1 + data.graph_y = labels + return data diff --git a/proteinworkshop/tasks/ppi_site_prediction.py b/proteinworkshop/tasks/ppi_site_prediction.py index 839f4f01..af0f40de 100644 --- a/proteinworkshop/tasks/ppi_site_prediction.py +++ b/proteinworkshop/tasks/ppi_site_prediction.py @@ -38,12 +38,11 @@ def __call__(self, data: Union[Protein, Data]): # Map the chain labels to integers target_chains = [] + chain_strs = [res.split(":")[0] for res in data.residue_id] + chain_strs = list(np.unique(chain_strs)) + for chain in data.graph_y: - try: - target_chains.append(self.chain_map[chain]) - except KeyError: - # Sometimes chains are stored as integers - target_chains.append(int(chain) - 1) + target_chains.append(chain_strs.index(chain)) target_chains = torch.tensor(target_chains) target_indices = torch.where(torch.isin(data.chains, target_chains))[0] @@ -101,6 +100,12 @@ def __call__(self, data: Union[Protein, Data]): if data.x is not None: data.x = data.x[mask] + if hasattr(data, "seq_pos"): + data.seq_pos = data.seq_pos[mask] + + if hasattr(data, "amino_acid_one_hot"): + data.amino_acid_one_hot = data.amino_acid_one_hot[mask] + return data diff --git a/proteinworkshop/tasks/remove_missing_ca.py b/proteinworkshop/tasks/remove_missing_ca.py index 27129947..1307b893 100644 --- a/proteinworkshop/tasks/remove_missing_ca.py +++ b/proteinworkshop/tasks/remove_missing_ca.py @@ -47,6 +47,9 @@ def __call__(self, data): if hasattr(data, "amino_acid_one_hot"): data.amino_acid_one_hot = data.amino_acid_one_hot[mask] + + if hasattr(data, "seq_pos"): + data.seq_pos = data.seq_pos[mask] return data diff --git a/proteinworkshop/tasks/sequence_denoising.py b/proteinworkshop/tasks/sequence_denoising.py index e7f0e74f..f6fd1aa5 100644 --- a/proteinworkshop/tasks/sequence_denoising.py +++ b/proteinworkshop/tasks/sequence_denoising.py @@ -3,7 +3,7 @@ from typing import Literal, Set, Union import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import Protein from torch_geometric.data import Data from torch_geometric.transforms import BaseTransform @@ -41,7 +41,7 @@ def __init__( def required_attributes(self) -> Set[str]: return {"residue_type"} - @beartype + @typechecker def __call__(self, x: Union[Data, Protein]) -> Union[Data, Protein]: x.residue_type_uncorrupted = copy.deepcopy(x.residue_type) # Get indices of residues to corrupt diff --git a/proteinworkshop/tasks/structural_denoising.py b/proteinworkshop/tasks/structural_denoising.py index c1c25b42..0a03b654 100644 --- a/proteinworkshop/tasks/structural_denoising.py +++ b/proteinworkshop/tasks/structural_denoising.py @@ -3,7 +3,7 @@ from typing import Literal, Set, Union import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import Protein from torch_geometric.data import Data from torch_geometric.transforms import BaseTransform @@ -36,7 +36,7 @@ def __init__( def required_attributes(self) -> Set[str]: return {"coords"} - @beartype + @typechecker def __call__(self, x: Union[Data, Protein]) -> Union[Data, Protein]: """Adds noise to the coordinates of a protein structure. diff --git a/proteinworkshop/train.py b/proteinworkshop/train.py index 14008694..0aa41263 100644 --- a/proteinworkshop/train.py +++ b/proteinworkshop/train.py @@ -12,6 +12,7 @@ import lovely_tensors as lt import torch import torch.nn as nn +import torch_geometric from graphein.protein.tensor.dataloader import ProteinDataLoader from graphein.ml.datasets.foldcomp_dataset import FoldCompLightningDataModule from lightning.pytorch.callbacks import Callback @@ -122,8 +123,7 @@ def train_model( == "flash.core.optimizers.LinearWarmupCosineAnnealingLR" and cfg.scheduler.interval == "step" ): - if isinstance(datamodule, FoldCompLightningDataModule): - datamodule.setup() + datamodule.setup() # type: ignore num_steps = _num_training_steps( datamodule.train_dataloader(), trainer ) @@ -176,7 +176,7 @@ def train_model( if cfg.get("compile"): log.info("Compiling model!") - model = torch.compile(model) # type: ignore + model = torch_geometric.compile(model, dynamic=True) if cfg.get("task_name") == "train": log.info("Starting training!") diff --git a/pyproject.toml b/pyproject.toml index f46701f2..0ebabd58 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,7 @@ packages = [ [tool.poetry.dependencies] python = ">= 3.9, < 3.11" -graphein = "^1.7.3" +graphein = "^1.7.5" loguru = "^0.7.0" fair-esm = "^2.0.0" hydra-core = "^1.3.2" @@ -44,18 +44,19 @@ torchmetrics = "^1.2.0" lovely-tensors = "^0.1.15" psutil = "^5.9.5" tqdm = "^4.66.1" -jaxtyping = "^0.2.21" +jaxtyping = "^0.2.24" omegaconf = "^2.3.0" pytorch-lightning = "^2.0.7" lightning = "^2.0.7" python-dotenv = "^1.0.0" wget = "^3.2" opt-einsum = "^3.3.0" -torch = ">=2.0.0, !=2.0.1" +torch = {version="^2.1.2", source="torch"} pyrootutils = "^1.0.4" -torch-geometric = "^2.3.1" +torch-geometric = "^2.4.0" hydra-colorlog = "^1.2.0" foldcomp = "^0.0.5" +captum = "^0.6.0" umap-learn = "^0.5.4" matplotlib = "^3.7.2" datashader = "^0.16.0" @@ -66,6 +67,9 @@ holoviews = "^1.18.0" black = "^23.7.0" isort = "^5.12.0" poethepoet = "^0.22.0" +ruff = "^0.1.1" +pydocstyle = "^6.3.0" +pre-commit = "^3.5.0" [tool.poetry.group.test.dependencies] @@ -90,6 +94,10 @@ watermark = "^2.4.3" sphinx-codeautolink = "^0.15.0" emoji = "^2.8.0" +[[tool.poetry.source]] +name = "torch" +url = "https://download.pytorch.org/whl/cpu/" +priority = "explicit" [tool.poe.tasks] install-torch-cluster = "pip install torch_cluster -f https://data.pyg.org/whl/torch-2.0.0+cpu.html" @@ -98,6 +106,7 @@ install-torch-scatter = "pip install torch_scatter -f https://data.pyg.org/whl/t install-torch-spline-conv = "pip install torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cpu.html" install-openfold = "pip install git+https://github.com/NVIDIA/dllogger.git@0540a43971f4a8a16693a9de9de73c1072020769 && pip install git+https://github.com/aqlaboratory/openfold.git@4b41059694619831a7db195b7e0988fc4ff3a307" install-torchdrug = "pip install torchdrug" +install-flash = "pip install lightning-flash --no-dependencies" [tool.poetry.scripts] train_workshop = "proteinworkshop.train:_main" @@ -110,6 +119,14 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" # Code-formatting and code-style config +[tool.ruff] +# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default. +select = ["E", "F", "I"] # <-- Note: I suggest including the "I" for additional checks! +ignore = ["F722"] # Ignore Jaxtyping forward references: https://github.com/google/jaxtyping/blob/v0.2.12/FAQ.md + +# Assuming you're developing for Python 3.10 +target-version = "py310" + [tool.black] exclude = ''' /( diff --git a/tests/conftest.py b/tests/conftest.py index 49588c79..a6583e72 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -8,7 +8,6 @@ from hydra import compose, initialize from hydra.core.global_hydra import GlobalHydra from omegaconf import DictConfig, open_dict -from proteinworkshop import constants from proteinworkshop.datasets.utils import create_example_batch @@ -20,7 +19,9 @@ def cfg_train_global() -> DictConfig: :return: A DictConfig object containing a default Hydra configuration for training. """ - with initialize(version_base="1.3", config_path="../proteinworkshop/config/"): + with initialize( + version_base="1.3", config_path="../proteinworkshop/config/" + ): cfg = compose( config_name="train.yaml", return_hydra_config=True, @@ -42,6 +43,7 @@ def cfg_train_global() -> DictConfig: cfg.dataset.datamodule.pin_memory = False cfg.extras.print_config = False cfg.extras.enforce_tags = False + cfg.hydra.job.num = 0 return cfg @@ -53,7 +55,9 @@ def cfg_finetune_global() -> DictConfig: :return: A DictConfig containing a default Hydra configuration for evaluation. """ - with initialize(version_base="1.3", config_path="../proteinworkshop/config/"): + with initialize( + version_base="1.3", config_path="../proteinworkshop/config/" + ): cfg = compose( config_name="finetune.yaml", return_hydra_config=True, @@ -74,6 +78,7 @@ def cfg_finetune_global() -> DictConfig: cfg.extras.print_config = False cfg.extras.enforce_tags = False cfg.logger = None + cfg.hydra.job.num = 0 return cfg diff --git a/tests/dataset/test_datasets.py b/tests/dataset/test_datasets.py index 607eea80..b8b8fb47 100644 --- a/tests/dataset/test_datasets.py +++ b/tests/dataset/test_datasets.py @@ -49,27 +49,33 @@ def test_instantiate_datasets(tmp_path): def test_datasets_have_overwrite_attr(tmp_path): - for t in os.listdir(DATASET_CONFIG_DIR): - config_path = DATASET_CONFIG_DIR / t - cfg = omegaconf.OmegaConf.load(config_path) + for t in os.listdir(DATASET_CONFIG_DIR): + config_path = DATASET_CONFIG_DIR / t + cfg = omegaconf.OmegaConf.load(config_path) - if "data_dir" in cfg.datamodule: - cfg.datamodule.data_dir = tmp_path + if "data_dir" in cfg.datamodule: + cfg.datamodule.data_dir = tmp_path - if "path" in cfg.datamodule: - cfg.datamodule.path = tmp_path + if "path" in cfg.datamodule: + cfg.datamodule.path = tmp_path - if "pdb_dir" in cfg.datamodule: - cfg.datamodule.pdb_dir = tmp_path + if "pdb_dir" in cfg.datamodule: + cfg.datamodule.pdb_dir = tmp_path - if "transforms" in cfg.datamodule: - cfg.datamodule.transforms = None + if "transforms" in cfg.datamodule: + cfg.datamodule.transforms = None - if "transform" in cfg.datamodule: - cfg.datamodule.transform = None + if "transform" in cfg.datamodule: + cfg.datamodule.transform = None - if cfg.datamodule._target_ in {"graphein.ml.datasets.foldcomp_dataset.FoldCompLightningDataModule", "proteinworkshop.datasets.atom3d_datamodule.ATOM3DDataModule"}: - continue - else: - dm = instantiate(cfg.datamodule) - assert hasattr(dm, "overwrite"), f"Datamodules {dm} has no overwrite attribute" + if cfg.datamodule._target_ in { + "graphein.ml.datasets.foldcomp_dataset.FoldCompLightningDataModule", + "proteinworkshop.datasets.atom3d_datamodule.ATOM3DDataModule", + "proteinworkshop.datasets.igfold.IgFoldDataModule", + }: + continue + else: + dm = instantiate(cfg.datamodule) + assert hasattr( + dm, "overwrite" + ), f"Datamodules {dm} has no overwrite attribute" diff --git a/tests/features/test_features.py b/tests/features/test_features.py index 9eb23a45..87e924ce 100644 --- a/tests/features/test_features.py +++ b/tests/features/test_features.py @@ -1,5 +1,5 @@ import os - +import torch import omegaconf from hydra.utils import instantiate @@ -29,6 +29,9 @@ def test_feature_shapes(example_batch): cfg = omegaconf.OmegaConf.load(config_path) featuriser = instantiate(cfg) + example_batch.seq_pos = torch.arange( + example_batch.coords.shape[0], dtype=torch.long + ) out = featuriser(example_batch) out_features = out.x diff --git a/tests/task/test_instantiate_transforms.py b/tests/task/test_instantiate_transforms.py index 7fcbc63b..63db1c68 100644 --- a/tests/task/test_instantiate_transforms.py +++ b/tests/task/test_instantiate_transforms.py @@ -9,10 +9,13 @@ TRANSFORMS = os.listdir(TRANSFORM_CONFIG_DIR) -def test_instantiate_encoders(): +def test_instantiate_transforms(): for t in TRANSFORMS: config_path = TRANSFORM_CONFIG_DIR / t cfg = omegaconf.OmegaConf.load(config_path) + + if t == "multihot_label_encoding.yaml": + cfg.multihot_label_encoding.num_classes = 2 transform = instantiate(cfg) if t == "none.yaml": @@ -25,6 +28,10 @@ def test_transform_call(example_batch): for t in TRANSFORMS: config_path = TRANSFORM_CONFIG_DIR / t cfg = omegaconf.OmegaConf.load(config_path) + + if t == "multihot_label_encoding.yaml": + cfg.multihot_label_encoding.num_classes = 2 + transform = instantiate(cfg) t = t.removesuffix(".yaml") diff --git a/tests/test_cli.py b/tests/test_cli.py index f9aa7964..07943a85 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -1,10 +1,8 @@ import shutil -import pytest import requests - def is_tool(name: str) -> bool: """Checks whether ``name`` is on ``PATH`` and is marked as an executable. @@ -31,13 +29,17 @@ def test_commands_found(): "install_pyg_workshop" ), "Workshop PyG installation CLI tool not found" + def test_download_urls(): """Assert downloads are found.""" - from proteinworkshop.scripts.download_processed_data import dataset_fname_map, _ZENODO_RECORD + from proteinworkshop.scripts.download_processed_data import ( + _ZENODO_RECORD, + dataset_fname_map, + ) fnames = list(set(dataset_fname_map.values())) for f in fnames: - url = f"https://zenodo.org/record/{_ZENODO_RECORD}/files/{f}.tar.gz?download=1" + url = f"https://zenodo.org/records/{_ZENODO_RECORD}/files/{f}.tar.gz?download=1" response = requests.head(url) assert response.status_code == 200, f"URL {url} not found." diff --git a/tests/test_package_install.py b/tests/test_package_install.py index 74cd4f20..1a08ffce 100644 --- a/tests/test_package_install.py +++ b/tests/test_package_install.py @@ -2,10 +2,8 @@ import sys -import pytest - # Import package, test suite, and other packages as needed -import proteinworkshop +import proteinworkshop # noqa: F401 def test_proteinworkshop_imported():