【Paddle Toolkit Development Competition No.3】 Paddle 适配 neuralop

jointContribution/neuraloperator-0.3.0-paddle/neuraloperator-0.3.0-paddle/.gitignore

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+# weights and biases
+.wandb/ 
+wandb_api_key.txt
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+*.DS_Store
+*.vscode
+
+# C extensions
+*.so
+*.py~
+
+# Pycharm
+.idea
+
+# vim temp files
+*.swp
+
+# Sphinx doc
+doc/_build/
+doc/source/auto_examples/
+doc/source/modules/generated/
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+.pytest_cache/
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+#Ipython Notebook
+.ipynb_checkpoints

jointContribution/neuraloperator-0.3.0-paddle/neuraloperator-0.3.0-paddle/LICENSE

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+MIT License
+
+Copyright (c) 2023 NeuralOperator developers
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

jointContribution/neuraloperator-0.3.0-paddle/neuraloperator-0.3.0-paddle/README.rst

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+.. image:: https://img.shields.io/pypi/v/neuraloperator
+   :target: https://pypi.org/project/neuraloperator/
+   :alt: PyPI
+
+.. image:: https://github.com/NeuralOperator/neuraloperator/actions/workflows/test.yml/badge.svg
+   :target: https://github.com/NeuralOperator/neuraloperator/actions/workflows/test.yml
+
+
+===============
+Neural Operator
+===============
+
+``neuraloperator`` is a comprehensive library for 
+learning neural operators in PyTorch.
+It is the official implementation for Fourier Neural Operators 
+and Tensorized Neural Operators.
+
+Unlike regular neural networks, neural operators
+enable learning mapping between function spaces, and this library
+provides all of the tools to do so on your own data.
+
+NeuralOperators are also resolution invariant, 
+so your trained operator can be applied on data of any resolution.
+
+
+Installation
+------------
+
+Just clone the repository and install locally (in editable mode so changes in the code are immediately reflected without having to reinstall):
+
+.. code::
+
+  git clone https://github.com/NeuralOperator/neuraloperator
+  cd neuraloperator
+  pip install -e .
+  pip install -r requirements.txt
+
+You can also just pip install the library:
+
+
+.. code::
+
+  pip install neuraloperator
+
+Quickstart
+----------
+
+After you've installed the library, you can start training operators seemlessly:
+
+
+.. code-block:: python
+
+   from neuralop.models import FNO
+
+   operator = FNO(n_modes=(16, 16), hidden_channels=64,
+                   in_channels=3, out_channels=1)
+
+Tensorization is also provided out of the box: you can improve the previous models
+by simply using a Tucker Tensorized FNO with just a few parameters:
+
+.. code-block:: python
+
+   from neuralop.models import TFNO
+
+   operator = TFNO(n_modes=(16, 16), hidden_channels=64,
+                   in_channels=3, 
+                   out_channels=1,
+                   factorization='tucker',
+                   implementation='factorized',
+                   rank=0.05)
+
+This will use a Tucker factorization of the weights. The forward pass
+will be efficient by contracting directly the inputs with the factors
+of the decomposition. The Fourier layers will have 5% of the parameters
+of an equivalent, dense Fourier Neural Operator!
+
+Checkout the `documentation <https://neuraloperator.github.io/neuraloperator/dev/index.html>`_ for more!
+
+Using with weights and biases
+-----------------------------
+
+Create a file in ``neuraloperator/config`` called ``wandb_api_key.txt`` and paste your Weights and Biases API key there.
+You can configure the project you want to use and your username in the main yaml configuration files.
+
+Contributing code
+-----------------
+
+All contributions are welcome! So if you spot a bug or even a typo or mistake in
+the documentation, please report it, and even better, open a Pull-Request on 
+`GitHub <https://github.com/neuraloperator/neuraloperator>`_. Before you submit
+your changes, you should make sure your code adheres to our style-guide. The
+easiest way to do this is with ``black``:
+
+.. code::
+
+   pip install black
+   black .
+
+Running the tests
+=================
+
+Testing and documentation are an essential part of this package and all
+functions come with uni-tests and documentation. The tests are ran using the
+pytest package. First install ``pytest``:
+
+.. code::
+
+    pip install pytest
+
+Then to run the test, simply run, in the terminal:
+
+.. code::
+
+    pytest -v neuralop
+
+Citing
+------
+
+If you use NeuralOperator in an academic paper, please cite [1]_, [2]_::
+
+   @misc{li2020fourier,
+      title={Fourier Neural Operator for Parametric Partial Differential Equations}, 
+      author={Zongyi Li and Nikola Kovachki and Kamyar Azizzadenesheli and Burigede Liu and Kaushik Bhattacharya and Andrew Stuart and Anima Anandkumar},
+      year={2020},
+      eprint={2010.08895},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+   }
+
+   @article{kovachki2021neural,
+      author    = {Nikola B. Kovachki and
+                     Zongyi Li and
+                     Burigede Liu and
+                     Kamyar Azizzadenesheli and
+                     Kaushik Bhattacharya and
+                     Andrew M. Stuart and
+                     Anima Anandkumar},
+      title     = {Neural Operator: Learning Maps Between Function Spaces},
+      journal   = {CoRR},
+      volume    = {abs/2108.08481},
+      year      = {2021},
+   }
+
+
+.. [1] Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar A., “Fourier Neural Operator for Parametric Partial Differential Equations”, ICLR, 2021. doi:10.48550/arXiv.2010.08895.
+
+.. [2] Kovachki, N., Li, Z., Liu, B., Azizzadenesheli, K., Bhattacharya, K., Stuart, A., and Anandkumar A., “Neural Operator: Learning Maps Between Function Spaces”, JMLR, 2021. doi:10.48550/arXiv.2108.08481.

jointContribution/neuraloperator-0.3.0-paddle/neuraloperator-0.3.0-paddle/config/burgers_config.yaml

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+default: &DEFAULT
+
+  #General
+  # For computing compression
+  n_params_baseline: None
+  verbose: True
+  arch: 'tfno2d'
+
+  #Distributed computing
+  distributed:
+    use_distributed: False
+    wireup_info: 'mpi'
+    wireup_store: 'tcp'
+    model_parallel_size: 2
+    seed: 666
+
+  # FNO related
+  tfno2d:
+    data_channels: 3
+    n_modes_height: 15
+    n_modes_width: 15
+    hidden_channels: 24
+    lifting_channels: 24
+    projection_channels: 24
+    n_layers: 5
+    domain_padding: None
+    domain_padding_mode: 'one-sided'
+    fft_norm: 'forward'
+    norm: 'group_norm'
+    skip: 'linear'
+    implementation: 'factorized'
+    separable: 0
+    preactivation: 0
+    half_prec_fourier: False
+    half_prec_inverse: False
+    stabilizer: None
+
+    use_mlp: 1
+    mlp:
+        expansion: 0.5
+        dropout: 0
+
+    factorization: None
+    rank: 0.05
+    fixed_rank_modes: None
+    dropout: 0.0
+    tensor_lasso_penalty: 0.0
+    joint_factorization: False
+
+  # Optimizer
+  opt:
+    n_epochs: 10000
+    learning_rate: 0.0001
+    training_loss: 'l2'
+    weight_decay: 1e-4
+    amp_autocast: False
+
+    scheduler_T_max: 500 # For cosine only, typically take n_epochs
+    scheduler_patience: 100 # For ReduceLROnPlateau only
+    scheduler: 'ReduceLROnPlateau' # Or 'CosineAnnealingLR' OR 'ReduceLROnPlateau'
+    step_size: 60
+    gamma: 0.5
+    precision_schedule: None
+
+  # Dataset related
+  data:
+    folder: '/home/ubuntu/data/burgers/burgers.npz' 
+    batch_size: 16
+    n_train: 800
+    test_batch_sizes: [16]
+    n_tests: [400]
+    spatial_length: 128
+    temporal_length: 101
+
+    positional_encoding: True
+    encode_input: False
+    encode_output: False
+    include_endpoint: [True, False]
+
+  # Patching
+  patching:
+    levels: 0
+    padding: 0
+    stitching: False
+
+  # Weights and biases
+  wandb:
+    log: False
+    name: None # If None, config will be used but you can override it here
+    group: ""
+    project: ""
+    entity: "" # put your username here
+    sweep: False
+    log_output: True
+    log_test_interval: 1