init

Author: CM-BF

.gitignore

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+.idea/**
+storage
+docs/build
+/docs/source/_autosummary/
+**/__pycace__/
+/GOOD/kernel/auto_launch.py
+/GOOD/kernel/auto_chart.py
+/GOOD/kernel/auto_chart_final.py
+/GOOD/kernel/auto_chart_paper.py
+/GOOD/kernel/auto_chart_tvt.py
+/GOOD/kernel/auto_checkpoint.py
+/GOOD/kernel/auto_curve.py
+/GOOD/kernel/auto_curve_chart.py
+/configs/auto_config_finetune/
+/graphEx/
+/debug_log.py
+/GOOD/kernel/launchers/ada_launcher.py

GOOD/__init__.py

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+from .utils import config_summoner, args_parser
+from .utils.register import register
+from . import data, networks, ood_algorithms

GOOD/data/__init__.py

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+r"""
+This data module includes 11 GOOD datasets and a dataloader for an organized data loading process.
+"""
+from GOOD.data.dataset_manager import load_dataset, create_dataloader
+from .good_datasets import *
+from .good_loaders import *

GOOD/data/dataset_manager.py

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+r"""A module that is consist of a dataset loading function and a PyTorch dataloader loading function.
+"""
+
+from torch_geometric.loader import DataLoader, GraphSAINTRandomWalkSampler
+
+from GOOD import register
+from GOOD.utils.config_reader import Union, CommonArgs, Munch
+from GOOD.utils.initial import reset_random_seed
+
+
+def read_meta_info(meta_info, config: Union[CommonArgs, Munch]):
+    config.dataset.dataset_type = meta_info.dataset_type
+    config.model.model_level = meta_info.model_level
+    config.dataset.dim_node = meta_info.dim_node
+    config.dataset.dim_edge = meta_info.dim_edge
+    config.dataset.num_envs = meta_info.num_envs
+    config.dataset.num_classes = meta_info.num_classes
+    config.dataset.num_train_nodes = meta_info.get('num_train_nodes')
+    config.dataset.num_domains = meta_info.get('num_domains')
+    config.dataset.feat_dims = meta_info.get('feat_dims')
+    config.dataset.edge_feat_dims = meta_info.get('edge_feat_dims')
+
+
+def load_dataset(name: str, config: Union[CommonArgs, Munch]) -> dir:
+    r"""
+    Load a dataset given the dataset name.
+
+    Args:
+        name (str): Dataset name.
+        config (Union[CommonArgs, Munch]): Required configs:
+            ``config.dataset.dataset_root``
+            ``config.dataset.domain``
+            ``config.dataset.shift_type``
+            ``config.dataset.generate``
+
+    Returns:
+        A dataset object and new configs
+            - config.dataset.dataset_type
+            - config.model.model_level
+            - config.dataset.dim_node
+            - config.dataset.dim_edge
+            - config.dataset.num_envs
+            - config.dataset.num_classes
+
+    """
+    try:
+        reset_random_seed(config)
+        dataset, meta_info = register.datasets[name].load(dataset_root=config.dataset.dataset_root,
+                                                          domain=config.dataset.domain,
+                                                          shift=config.dataset.shift_type,
+                                                          generate=config.dataset.generate)
+    except KeyError as e:
+        print('Dataset not found.')
+        raise e
+    read_meta_info(meta_info, config)
+
+    config.metric.set_score_func(dataset['metric'] if type(dataset) is dict else getattr(dataset, 'metric'))
+    config.metric.set_loss_func(dataset['task'] if type(dataset) is dict else getattr(dataset, 'task'))
+
+    return dataset
+
+
+def create_dataloader(dataset, config: Union[CommonArgs, Munch]):
+    r"""
+    Create a PyG data loader.
+
+    Args:
+        loader_name:
+        dataset: A GOOD dataset.
+        config: Required configs:
+            ``config.train.train_bs``
+            ``config.train.val_bs``
+            ``config.train.test_bs``
+            ``config.model.model_layer``
+            ``config.train.num_steps(for node prediction)``
+
+    Returns:
+        A PyG dataset loader.
+
+    """
+    loader_name = config.dataset.dataloader_name
+    try:
+        reset_random_seed(config)
+        loader = register.dataloader[loader_name].setup(dataset, config)
+    except KeyError as e:
+        print(f'DataLoader {loader_name} not found.')
+        raise e
+
+    return loader
+
+
+def domain_pair_dataloader(dataset, config: Union[CommonArgs, Munch]):
+    r"""
+    Create a PyG domain_pair data loader.
+
+    Args:
+        dataset: A GOOD dataset.
+        config: Required configs:
+            ``config.train.train_bs``
+            ``config.train.val_bs``
+            ``config.train.test_bs``
+            ``config.model.model_layer``
+            ``config.train.num_steps(for node prediction)``
+
+    Returns:
+        A PyG domain_pair dataset loader.
+
+    """
+    reset_random_seed(config)
+    if config.model.model_level == 'node':
+        graph = dataset[0]
+        loader = GraphSAINTRandomWalkSampler(graph, batch_size=config.train.train_bs,
+                                             walk_length=config.model.model_layer,
+                                             num_steps=config.train.num_steps, sample_coverage=100,
+                                             save_dir=dataset.processed_dir)
+        loader = {'train': loader, 'eval_train': [graph], 'id_val': [graph], 'id_test': [graph], 'val': [graph],
+                  'test': [graph]}
+    else:
+        loader = {'train': DataLoader(dataset['train'], batch_size=config.train.train_bs, shuffle=True),
+                  'eval_train': DataLoader(dataset['train'], batch_size=config.train.val_bs, shuffle=False),
+                  'id_val': DataLoader(dataset['id_val'], batch_size=config.train.val_bs, shuffle=False) if dataset.get(
+                      'id_val') else None,
+                  'id_test': DataLoader(dataset['id_test'], batch_size=config.train.test_bs,
+                                        shuffle=False) if dataset.get(
+                      'id_test') else None,
+                  'val': DataLoader(dataset['val'], batch_size=config.train.val_bs, shuffle=False),
+                  'test': DataLoader(dataset['test'], batch_size=config.train.test_bs, shuffle=False)}
+
+    return loader

GOOD/data/good_datasets/__init__.py

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+r"""
+This module includes 11 GOOD datasets.
+
+- Graph prediction datasets: GOOD-HIV, GOOD-PCBA, GOOD-ZINC, GOOD-SST2, GOOD-CMNIST, GOOD-Motif.
+- Node prediction datasets: GOOD-Cora, GOOD-Arxiv, GOOD-Twitch, GOOD-WebKB, GOOD-CBAS.
+"""
+
+import glob
+from os.path import dirname, basename, isfile, join
+
+modules = glob.glob(join(dirname(__file__), "*.py"))
+__all__ = [basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]
+
+from . import *
+