Add copyright notice to every python script

benchmarks/inference.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 """
 Benchmark script for inference.
 This script compiles a model using torch.compile and measures the time it takes to run a forward and backward pass for a certain molecule.

benchmarks/neighbors.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import os
 import torch
 import numpy as np

docs/source/conf.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 # Configuration file for the Sphinx documentation builder.
 
 # -- Project information

setup.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import subprocess
 from setuptools import setup, find_packages
 import torch

tests/test_calculator.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import torch
 from torch.testing import assert_allclose
 import pytest

tests/test_cfconv.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import pytest
 from pytest import mark
 import torch as pt

tests/test_datamodule.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 from pytest import mark
 import torch
 from torchmdnet.data import DataModule

tests/test_dataset_comp6.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import torch as pt
 from tempfile import TemporaryDirectory
 from torch_geometric.loader import DataLoader

tests/test_datasets.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import pytest
 from pytest import mark, raises
 from os.path import join

tests/test_equivariance.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import torch
 from torchmdnet.models.model import create_model
 from utils import load_example_args

tests/test_examples.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 from pytest import mark
 import yaml
 import glob

tests/test_model.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import pytest
 from pytest import mark
 import pickle

tests/test_model_utils.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 from pytest import mark, raises
 import torch
 from torch.autograd import grad

tests/test_module.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 from pytest import mark
 from glob import glob
 from os.path import dirname, join

tests/test_neighbors.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import os
 import pytest
 import torch

tests/test_optimize.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import pytest
 from pytest import mark
 import torch as pt

tests/test_prior_d2.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 from torchmdnet.priors import D2
 import torch as pt
 from pytest import mark

tests/test_priors.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import pytest
 from pytest import mark
 import torch

tests/test_rbfs.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 from pytest import mark
 import torch
 from torchmdnet.models.utils import rbf_class_mapping

tests/test_utils.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 from os.path import join, exists
 from pytest import mark, raises
 import torch

tests/test_wrappers.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import pytest
 from pytest import mark
 from torchmdnet import models

tests/utils.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import yaml
 from os.path import dirname, join
 import torch

torchmdnet/calculators.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import torch
 from torchmdnet.models.model import load_model
 

torchmdnet/data.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 from os.path import join
 from tqdm import tqdm
 import torch

torchmdnet/datasets/__init__.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 from .ace import Ace
 from .ani import ANI1, ANI1CCX, ANI1X
 from .comp6 import ANIMD, DrugBank, GDB07to09, GDB10to13, Tripeptides, S66X8, COMP6v1

torchmdnet/datasets/ace.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import hashlib
 import h5py
 import numpy as np

torchmdnet/datasets/ani.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import h5py
 import numpy as np
 import os

torchmdnet/datasets/comp6.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import h5py
 import numpy as np
 import os

torchmdnet/datasets/custom.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import glob
 import numpy as np
 import torch

torchmdnet/datasets/hdf.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import torch
 from torch_geometric.data import Dataset, Data
 import h5py

torchmdnet/datasets/md17.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import os
 import os.path as osp
 from typing import Callable, List, Optional

torchmdnet/datasets/md22.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import os
 import os.path as osp
 from typing import Callable, List, Optional

torchmdnet/datasets/qm9.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import torch
 from torch_geometric.transforms import Compose
 from torch_geometric.datasets import QM9 as QM9_geometric

torchmdnet/datasets/qm9q.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import h5py
 import numpy as np
 import os

torchmdnet/datasets/spice.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import hashlib
 import h5py
 import numpy as np

torchmdnet/extensions/__init__.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 # Place here any short extensions to torch that you want to use in your code.
 # The extensions present in extensions.cpp will be automatically compiled in setup.py and loaded here.
 # The extensions will be available under torch.ops.torchmdnet_extensions, but you can add wrappers here to make them more convenient to use.

torchmdnet/extensions/extensions.cpp

@@ -1,4 +1,8 @@
-/* Raul P. Pelaez 2023. Torch extensions to the torchmdnet library.
+/*
+ * Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+ * Distributed under the MIT License.
+ *(See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+ * Raul P. Pelaez 2023. Torch extensions to the torchmdnet library.
  * You can expose functions to python here which will be compatible with TorchScript.
  * Add your exports to the TORCH_LIBRARY macro below, see __init__.py to see how to access them from python.
  * The WITH_CUDA macro will be defined when compiling with CUDA support.

torchmdnet/extensions/neighbors/common.cuh

@@ -1,4 +1,7 @@
-/* Raul P. Pelaez 2023. Common utilities for the CUDA neighbor operation.
+/* Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+ * Distributed under the MIT License.
+ *(See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+ * Raul P. Pelaez 2023. Common utilities for the CUDA neighbor operation.
  */
 #ifndef NEIGHBORS_COMMON_CUH
 #define NEIGHBORS_COMMON_CUH

torchmdnet/extensions/neighbors/neighbors_cpu.cpp

@@ -1,3 +1,7 @@
+/* Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+ * Distributed under the MIT License.
+ *(See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+*/
 #include <torch/extension.h>
 #include <tuple>
 

torchmdnet/extensions/neighbors/neighbors_cuda.cu

@@ -1,5 +1,8 @@
-/* Raul P. Pelaez 2023
-   Connection between the neighbor CUDA implementations and the torch extension.
+/*
+ * Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+ * Distributed under the MIT License.
+ * (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+ * Raul P. Pelaez 2023
  */
 #include "neighbors_cuda_brute.cuh"
 #include "neighbors_cuda_cell.cuh"

torchmdnet/extensions/neighbors/neighbors_cuda_brute.cuh

@@ -1,9 +1,13 @@
-/* Raul P. Pelaez 2023. Brute force neighbor list construction in CUDA.
-
-   A brute force approach that assigns a thread per each possible pair of particles in the system.
-   Based on an implementation by Raimondas Galvelis.
-   Works fantastically for small (less than 10K atoms) systems, but cannot handle more than 32K
-   atoms.
+/* Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+ * Distributed under the MIT License.
+ *(See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+ *
+ * Raul P. Pelaez 2023. Brute force neighbor list construction in CUDA.
+ *
+ * A brute force approach that assigns a thread per each possible pair of particles in the system.
+ * Based on an implementation by Raimondas Galvelis.
+ * Works fantastically for small (less than 10K atoms) systems, but cannot handle more than 32K
+ * atoms.
  */
 #ifndef NEIGHBORS_BRUTE_CUH
 #define NEIGHBORS_BRUTE_CUH

torchmdnet/extensions/neighbors/neighbors_cuda_cell.cuh

@@ -1,5 +1,7 @@
-/* Raul P. Pelaez 2023. Batched cell list neighbor list implementation for CUDA.
-
+/* Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+ * Distributed under the MIT License.
+ *(See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+ * Raul P. Pelaez 2023. Batched cell list neighbor list implementation for CUDA.
  */
 #ifndef NEIGHBOR_CUDA_CELL_H
 #define NEIGHBOR_CUDA_CELL_H

torchmdnet/extensions/neighbors/neighbors_cuda_shared.cuh

@@ -1,8 +1,11 @@
-/* Raul P. Pelaez 2023. Shared memory neighbor list construction for CUDA.
-   This brute force approach checks all pairs of atoms by collaborativelly loading and processing
-   tiles of atoms into shared memory.
-   This approach is tipically slower than the brute force approach, but can handle an arbitrarily
-   large number of atoms.
+/* Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+ * Distributed under the MIT License.
+ *(See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+ * Raul P. Pelaez 2023. Shared memory neighbor list construction for CUDA.
+ * This brute force approach checks all pairs of atoms by collaborativelly loading and processing
+ * tiles of atoms into shared memory.
+ * This approach is tipically slower than the brute force approach, but can handle an arbitrarily
+ * large number of atoms.
  */
 #ifndef NEIGHBORS_SHARED_CUH
 #define NEIGHBORS_SHARED_CUH

torchmdnet/models/__init__.py

@@ -1 +1,5 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 __all_models__ = ["graph-network", "transformer", "equivariant-transformer", "tensornet"]

torchmdnet/models/model.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 import re
 from typing import Optional, List, Tuple, Dict
 import torch

torchmdnet/models/output_modules.py

@@ -1,3 +1,7 @@
+# Copyright Universitat Pompeu Fabra 2020-2023  https://www.compscience.org
+# Distributed under the MIT License.
+# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
+
 from abc import abstractmethod, ABCMeta
 from typing import Optional
 import torch