chore: import upstream snapshot with attribution

python/dgl/data/flickr.py

@@ -0,0 +1,178 @@
+"""Flickr Dataset"""
+import json
+import os
+
+import numpy as np
+import scipy.sparse as sp
+
+from .. import backend as F
+from ..convert import from_scipy
+from ..transforms import reorder_graph
+from .dgl_dataset import DGLBuiltinDataset
+from .utils import _get_dgl_url, generate_mask_tensor, load_graphs, save_graphs
+
+
+class FlickrDataset(DGLBuiltinDataset):
+    r"""Flickr dataset for node classification from `GraphSAINT: Graph Sampling Based Inductive
+    Learning Method <https://arxiv.org/abs/1907.04931>`_
+
+    The task of this dataset is categorizing types of images based on the descriptions and common
+    properties of online images.
+
+    Flickr dataset statistics:
+
+    - Nodes: 89,250
+    - Edges: 899,756
+    - Number of classes: 7
+    - Node feature size: 500
+
+    Parameters
+    ----------
+    raw_dir : str
+        Raw file directory to download/contains the input data directory.
+        Default: ~/.dgl/
+    force_reload : bool
+        Whether to reload the dataset.
+        Default: False
+    verbose : bool
+        Whether to print out progress information.
+        Default: False
+    transform : callable, optional
+        A transform that takes in a :class:`~dgl.DGLGraph` object and returns
+        a transformed version. The :class:`~dgl.DGLGraph` object will be
+        transformed before every access.
+    reorder : bool
+        Whether to reorder the graph using :func:`~dgl.reorder_graph`.
+        Default: False.
+
+    Attributes
+    ----------
+    num_classes : int
+        Number of node classes
+
+    Examples
+    --------
+    >>> from dgl.data import FlickrDataset
+    >>> dataset = FlickrDataset()
+    >>> dataset.num_classes
+    7
+    >>> g = dataset[0]
+    >>> # get node feature
+    >>> feat = g.ndata['feat']
+    >>> # get node labels
+    >>> labels = g.ndata['label']
+    >>> # get data split
+    >>> train_mask = g.ndata['train_mask']
+    >>> val_mask = g.ndata['val_mask']
+    >>> test_mask = g.ndata['test_mask']
+    """
+
+    def __init__(
+        self,
+        raw_dir=None,
+        force_reload=False,
+        verbose=False,
+        transform=None,
+        reorder=False,
+    ):
+        _url = _get_dgl_url("dataset/flickr.zip")
+        self._reorder = reorder
+        super(FlickrDataset, self).__init__(
+            name="flickr",
+            raw_dir=raw_dir,
+            url=_url,
+            force_reload=force_reload,
+            verbose=verbose,
+            transform=transform,
+        )
+
+    def process(self):
+        """process raw data to graph, labels and masks"""
+        coo_adj = sp.load_npz(os.path.join(self.raw_path, "adj_full.npz"))
+        g = from_scipy(coo_adj)
+
+        features = np.load(os.path.join(self.raw_path, "feats.npy"))
+        features = F.tensor(features, dtype=F.float32)
+
+        y = [-1] * features.shape[0]
+        with open(os.path.join(self.raw_path, "class_map.json")) as f:
+            class_map = json.load(f)
+            for key, item in class_map.items():
+                y[int(key)] = item
+        labels = F.tensor(np.array(y), dtype=F.int64)
+
+        with open(os.path.join(self.raw_path, "role.json")) as f:
+            role = json.load(f)
+
+        train_mask = np.zeros(features.shape[0], dtype=bool)
+        train_mask[role["tr"]] = True
+
+        val_mask = np.zeros(features.shape[0], dtype=bool)
+        val_mask[role["va"]] = True
+
+        test_mask = np.zeros(features.shape[0], dtype=bool)
+        test_mask[role["te"]] = True
+
+        g.ndata["feat"] = features
+        g.ndata["label"] = labels
+        g.ndata["train_mask"] = generate_mask_tensor(train_mask)
+        g.ndata["val_mask"] = generate_mask_tensor(val_mask)
+        g.ndata["test_mask"] = generate_mask_tensor(test_mask)
+
+        if self._reorder:
+            self._graph = reorder_graph(
+                g,
+                node_permute_algo="rcmk",
+                edge_permute_algo="dst",
+                store_ids=False,
+            )
+        else:
+            self._graph = g
+
+    def has_cache(self):
+        graph_path = os.path.join(self.save_path, "dgl_graph.bin")
+        return os.path.exists(graph_path)
+
+    def save(self):
+        graph_path = os.path.join(self.save_path, "dgl_graph.bin")
+        save_graphs(graph_path, self._graph)
+
+    def load(self):
+        graph_path = os.path.join(self.save_path, "dgl_graph.bin")
+        g, _ = load_graphs(graph_path)
+        self._graph = g[0]
+
+    @property
+    def num_classes(self):
+        return 7
+
+    def __len__(self):
+        r"""The number of graphs in the dataset."""
+        return 1
+
+    def __getitem__(self, idx):
+        r"""Get graph object
+
+        Parameters
+        ----------
+        idx : int
+            Item index, FlickrDataset has only one graph object
+
+        Returns
+        -------
+        :class:`dgl.DGLGraph`
+
+            The graph contains:
+
+            - ``ndata['label']``: node label
+            - ``ndata['feat']``: node feature
+            - ``ndata['train_mask']``: mask for training node set
+            - ``ndata['val_mask']``: mask for validation node set
+            - ``ndata['test_mask']``: mask for test node set
+
+        """
+        assert idx == 0, "This dataset has only one graph"
+        if self._transform is None:
+            return self._graph
+        else:
+            return self._transform(self._graph)