update_name

graph_dit/datasets/abstract_dataset.py

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+from diffusion.distributions import DistributionNodes
+import utils as utils
+import torch
+import pytorch_lightning as pl
+from torch_geometric.loader import DataLoader
+
+
+class AbstractDataModule(pl.LightningDataModule):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+        self.dataloaders = None
+        self.input_dims = None
+        self.output_dims = None
+
+    def prepare_data(self, datasets) -> None:
+        batch_size = self.cfg.train.batch_size
+        num_workers = self.cfg.train.num_workers
+        self.dataloaders = {split: DataLoader(dataset, batch_size=batch_size, num_workers=num_workers,
+                                              shuffle='debug' not in self.cfg.general.name)
+                            for split, dataset in datasets.items()}
+
+    def train_dataloader(self):
+        return self.dataloaders["train"]
+
+    def val_dataloader(self):
+        return self.dataloaders["val"]
+
+    def test_dataloader(self):
+        return self.dataloaders["test"]
+
+    def __getitem__(self, idx):
+        return self.dataloaders['train'][idx]
+
+    def node_counts(self, max_nodes_possible=300):
+        all_counts = torch.zeros(max_nodes_possible)
+        for split in ['train', 'val', 'test']:
+            for i, data in enumerate(self.dataloaders[split]):
+                unique, counts = torch.unique(data.batch, return_counts=True)
+                for count in counts:
+                    all_counts[count] += 1
+        max_index = max(all_counts.nonzero())
+        all_counts = all_counts[:max_index + 1]
+        all_counts = all_counts / all_counts.sum()
+        return all_counts
+
+    def node_types(self):
+        num_classes = None
+        for data in self.dataloaders['train']:
+            num_classes = data.x.shape[1]
+            break
+
+        counts = torch.zeros(num_classes)
+
+        for split in ['train', 'val', 'test']:
+            for i, data in enumerate(self.dataloaders[split]):
+                counts += data.x.sum(dim=0)
+
+        counts = counts / counts.sum()
+        return counts
+
+    def edge_counts(self):
+        num_classes = None
+        for data in self.dataloaders['train']:
+            num_classes = 5
+            break
+
+        d = torch.Tensor(num_classes)
+
+        for split in ['train', 'val', 'test']:
+            for i, data in enumerate(self.dataloaders[split]):
+                unique, counts = torch.unique(data.batch, return_counts=True)
+
+                all_pairs = 0
+                for count in counts:
+                    all_pairs += count * (count - 1)
+
+                num_edges = data.edge_index.shape[1]
+                num_non_edges = all_pairs - num_edges
+
+                data_edge_attr = torch.nn.functional.one_hot(data.edge_attr, num_classes=5).float()
+                edge_types = data_edge_attr.sum(dim=0)
+                assert num_non_edges >= 0
+                d[0] += num_non_edges
+                d[1:] += edge_types[1:]
+
+        d = d / d.sum()
+        return d
+
+
+class MolecularDataModule(AbstractDataModule):
+    def valency_count(self, max_n_nodes):
+        valencies = torch.zeros(3 * max_n_nodes - 2)   # Max valency possible if everything is connected
+        multiplier = torch.Tensor([0, 1, 2, 3, 1.5])
+        for split in ['train', 'val', 'test']:
+            for i, data in enumerate(self.dataloaders[split]):
+                n = data.x.shape[0]
+                for atom in range(n):
+                    data_edge_attr = torch.nn.functional.one_hot(data.edge_attr, num_classes=5).float()
+                    edges = data_edge_attr[data.edge_index[0] == atom]
+                    edges_total = edges.sum(dim=0)
+                    valency = (edges_total * multiplier).sum()
+                    valencies[valency.long().item()] += 1
+        valencies = valencies / valencies.sum()
+        return valencies
+
+
+class AbstractDatasetInfos:
+    def complete_infos(self, n_nodes, node_types):
+        self.input_dims = None
+        self.output_dims = None
+        self.num_classes = len(node_types)
+        self.max_n_nodes = len(n_nodes) - 1
+        self.nodes_dist = DistributionNodes(n_nodes)
+
+    def compute_input_output_dims(self, datamodule):
+        example_batch = datamodule.example_batch()
+        example_batch_x = torch.nn.functional.one_hot(example_batch.x, num_classes=118).float()[:, self.active_index]
+        example_batch_edge_attr = torch.nn.functional.one_hot(example_batch.edge_attr, num_classes=5).float()
+
+        self.input_dims = {'X': example_batch_x.size(1), 
+                           'E': example_batch_edge_attr.size(1), 
+                           'y': example_batch['y'].size(1)}
+        self.output_dims = {'X': example_batch_x.size(1),
+                            'E': example_batch_edge_attr.size(1),
+                            'y': example_batch['y'].size(1)}