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Add license text to each module
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‎portpy/__init__.py‎

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# Copyright 2025, the PortPy Authors
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#
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# Licensed under the Apache License, Version 2.0 with the Commons Clause restriction.
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# You may obtain a copy of the Apache 2 License at:
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# ----------------------------------------------------------------------
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# Commons Clause Restriction Notice:
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# PortPy is licensed under Apache 2.0 with the Commons Clause.
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# You may use, modify, and share the code for non-commercial
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# academic and research purposes only.
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# Commercial use — including offering PortPy as a service,
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# or incorporating it into a commercial product — requires
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# a separate commercial license.
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# ----------------------------------------------------------------------
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__version__ = "1.1.1"
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from portpy import photon

‎portpy/ai/__init__.py‎

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# Copyright 2025, the PortPy Authors
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#
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# Licensed under the Apache License, Version 2.0 with the Commons Clause restriction.
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# You may obtain a copy of the Apache 2 License at:
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# ----------------------------------------------------------------------
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# Commons Clause Restriction Notice:
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# PortPy is licensed under Apache 2.0 with the Commons Clause.
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# You may use, modify, and share the code for non-commercial
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# academic and research purposes only.
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# Commercial use — including offering PortPy as a service,
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# or incorporating it into a commercial product — requires
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# a separate commercial license.
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# ----------------------------------------------------------------------
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‎portpy/ai/data/__init__.py‎

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"""This package includes all the modules related to data loading and preprocessing
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To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
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You need to implement four functions:
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-- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt).
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-- <__len__>: return the size of dataset.
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-- <__getitem__>: get a data point from data loader.
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-- <modify_commandline_options>: (optionally) add dataset-specific options and set default options.
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Now you can use the dataset class by specifying flag '--dataset_mode dummy'.
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See our template dataset class 'template_dataset.py' for more details.
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"""
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import os
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import sys
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project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
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if project_root not in sys.path:
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sys.path.append(project_root)
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import importlib
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import torch.utils.data
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from portpy.ai.data.base_dataset import BaseDataset
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def find_dataset_using_name(dataset_name):
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"""Import the module "data/[dataset_name]_dataset.py".
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In the file, the class called DatasetNameDataset() will
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be instantiated. It has to be a subclass of BaseDataset,
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and it is case-insensitive.
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"""
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dataset_filename = "portpy.ai.data." + dataset_name + "_dataset"
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datasetlib = importlib.import_module(dataset_filename)
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dataset = None
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target_dataset_name = dataset_name.replace('_', '') + 'dataset'
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for name, cls in datasetlib.__dict__.items():
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if name.lower() == target_dataset_name.lower() \
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and issubclass(cls, BaseDataset):
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dataset = cls
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if dataset is None:
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raise NotImplementedError("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name))
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return dataset
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def get_option_setter(dataset_name):
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"""Return the static method <modify_commandline_options> of the dataset class."""
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dataset_class = find_dataset_using_name(dataset_name)
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return dataset_class.modify_commandline_options
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def create_dataset(opt):
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"""Create a dataset given the option.
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This function wraps the class CustomDatasetDataLoader.
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This is the main interface between this package and 'train.py'/'test.py'
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Example:
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from data import create_dataset
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dataset = create_dataset(opt)
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"""
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data_loader = CustomDatasetDataLoader(opt)
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dataset = data_loader.load_data()
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return dataset
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class CustomDatasetDataLoader():
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"""Wrapper class of Dataset class that performs multi-threaded data loading"""
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# Copyright 2025, the PortPy Authors
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#
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# Licensed under the Apache License, Version 2.0 with the Commons Clause restriction.
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# You may obtain a copy of the Apache 2 License at:
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# ----------------------------------------------------------------------
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# Commons Clause Restriction Notice:
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# PortPy is licensed under Apache 2.0 with the Commons Clause.
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# You may use, modify, and share the code for non-commercial
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# academic and research purposes only.
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# Commercial use — including offering PortPy as a service,
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# or incorporating it into a commercial product — requires
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# a separate commercial license.
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# ----------------------------------------------------------------------
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def __init__(self, opt):
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"""Initialize this class

‎portpy/ai/data/base_dataset.py‎

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"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
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It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
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"""
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import random
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import numpy as np
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import torch.utils.data as data
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from PIL import Image
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import torchvision.transforms as transforms
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import torchvision.transforms.functional as TF
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from abc import ABC, abstractmethod
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import torch
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import warnings
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from scipy.ndimage import affine_transform
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warnings.filterwarnings("ignore")
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class BaseDataset(data.Dataset, ABC):
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"""This class is an abstract base class (ABC) for datasets.
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To create a subclass, you need to implement the following four functions:
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-- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt).
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-- <__len__>: return the size of dataset.
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-- <__getitem__>: get a data point.
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-- <modify_commandline_options>: (optionally) add dataset-specific options and set default options.
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"""
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# Copyright 2025, the PortPy Authors
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#
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# Licensed under the Apache License, Version 2.0 with the Commons Clause restriction.
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# You may obtain a copy of the Apache 2 License at:
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# ----------------------------------------------------------------------
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# Commons Clause Restriction Notice:
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# PortPy is licensed under Apache 2.0 with the Commons Clause.
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# You may use, modify, and share the code for non-commercial
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# academic and research purposes only.
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# Commercial use — including offering PortPy as a service,
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# or incorporating it into a commercial product — requires
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# a separate commercial license.
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# ----------------------------------------------------------------------
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def __init__(self, opt):
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"""Initialize the class; save the options in the class

‎portpy/ai/data/dosepred3d_dataset.py‎

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# Copyright 2025, the PortPy Authors
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#
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# Licensed under the Apache License, Version 2.0 with the Commons Clause restriction.
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# You may obtain a copy of the Apache 2 License at:
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# ----------------------------------------------------------------------
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# Commons Clause Restriction Notice:
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# PortPy is licensed under Apache 2.0 with the Commons Clause.
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# You may use, modify, and share the code for non-commercial
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# academic and research purposes only.
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# Commercial use — including offering PortPy as a service,
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# or incorporating it into a commercial product — requires
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# a separate commercial license.
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# ----------------------------------------------------------------------
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""

‎portpy/ai/data/image_folder.py‎

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"""A modified image folder class
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We modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py)
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so that this class can load images from both current directory and its subdirectories.
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"""
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# Copyright 2025, the PortPy Authors
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#
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# Licensed under the Apache License, Version 2.0 with the Commons Clause restriction.
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# You may obtain a copy of the Apache 2 License at:
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# ----------------------------------------------------------------------
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# Commons Clause Restriction Notice:
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# PortPy is licensed under Apache 2.0 with the Commons Clause.
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# You may use, modify, and share the code for non-commercial
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# academic and research purposes only.
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# Commercial use — including offering PortPy as a service,
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# or incorporating it into a commercial product — requires
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# a separate commercial license.
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# ----------------------------------------------------------------------
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import torch.utils.data as data
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‎portpy/ai/data/single_dataset.py‎

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# Copyright 2025, the PortPy Authors
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#
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# Licensed under the Apache License, Version 2.0 with the Commons Clause restriction.
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# You may obtain a copy of the Apache 2 License at:
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# ----------------------------------------------------------------------
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# Commons Clause Restriction Notice:
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# PortPy is licensed under Apache 2.0 with the Commons Clause.
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# You may use, modify, and share the code for non-commercial
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# academic and research purposes only.
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# Commercial use — including offering PortPy as a service,
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# or incorporating it into a commercial product — requires
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# a separate commercial license.
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# ----------------------------------------------------------------------
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from portpy.ai.data.base_dataset import BaseDataset, get_transform
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from portpy.ai.data.image_folder import make_dataset
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import torch

‎portpy/ai/data/template_dataset.py‎

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# Copyright 2025, the PortPy Authors
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#
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# Licensed under the Apache License, Version 2.0 with the Commons Clause restriction.
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# You may obtain a copy of the Apache 2 License at:
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# ----------------------------------------------------------------------
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# Commons Clause Restriction Notice:
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# PortPy is licensed under Apache 2.0 with the Commons Clause.
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# You may use, modify, and share the code for non-commercial
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# academic and research purposes only.
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# Commercial use — including offering PortPy as a service,
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# or incorporating it into a commercial product — requires
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# a separate commercial license.
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# ----------------------------------------------------------------------
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"""Dataset class template
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This module provides a template for users to implement custom datasets.

‎portpy/ai/models/__init__.py‎

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# Copyright 2025, the PortPy Authors
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#
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# Licensed under the Apache License, Version 2.0 with the Commons Clause restriction.
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# You may obtain a copy of the Apache 2 License at:
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# ----------------------------------------------------------------------
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# Commons Clause Restriction Notice:
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# PortPy is licensed under Apache 2.0 with the Commons Clause.
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# You may use, modify, and share the code for non-commercial
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# academic and research purposes only.
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# Commercial use — including offering PortPy as a service,
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# or incorporating it into a commercial product — requires
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# a separate commercial license.
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# ----------------------------------------------------------------------
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"""This package contains modules related to objective functions, optimizations, and network architectures.
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To add a custom model class called 'dummy', you need to add a file called 'dummy_model.py' and define a subclass DummyModel inherited from BaseModel.

‎portpy/ai/models/base_model.py‎

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# Copyright 2025, the PortPy Authors
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#
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# Licensed under the Apache License, Version 2.0 with the Commons Clause restriction.
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# You may obtain a copy of the Apache 2 License at:
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# ----------------------------------------------------------------------
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# Commons Clause Restriction Notice:
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# PortPy is licensed under Apache 2.0 with the Commons Clause.
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# You may use, modify, and share the code for non-commercial
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# academic and research purposes only.
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# Commercial use — including offering PortPy as a service,
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# or incorporating it into a commercial product — requires
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# a separate commercial license.
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# ----------------------------------------------------------------------
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import os
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import torch
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from collections import OrderedDict

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