Add batch and ptr vectors for a list of tensors and nested dicts

CHANGELOG.md

@@ -5,6 +5,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ## [2.0.5] - 2022-MM-DD
 ### Added
+- Added support for `follow_batch` for lists or dictionaries of tensors ([#4837](https://github.com/pyg-team/pytorch_geometric/pull/4837))
 - Added `Data.validate()` and `HeteroData.validate()` functionality ([#4885](https://github.com/pyg-team/pytorch_geometric/pull/4885))
 - Added `LinkNeighborLoader` support to `LightningDataModule` ([#4868](https://github.com/pyg-team/pytorch_geometric/pull/4868))
 - Added `predict()` support to the `LightningNodeData` module ([#4884](https://github.com/pyg-team/pytorch_geometric/pull/4884))
@@ -47,6 +48,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for graph-level outputs in `to_hetero` ([#4582](https://github.com/pyg-team/pytorch_geometric/pull/4582))
 - Added `CHANGELOG.md` ([#4581](https://github.com/pyg-team/pytorch_geometric/pull/4581))
 ### Changed
+- Fixed `InMemoryDataset` inferring wrong `len` for lists of tensors ([#4837](https://github.com/pyg-team/pytorch_geometric/pull/4837))
+- Fixed `Batch.separate` when using it for lists of tensors ([#4837](https://github.com/pyg-team/pytorch_geometric/pull/4837))
 - Correct docstring for SAGEConv ([#4852](https://github.com/pyg-team/pytorch_geometric/pull/4852))
 - Fixed a bug in `TUDataset` where `pre_filter` was not applied whenever `pre_transform` was present
 - Renamed `RandomTranslate` to `RandomJitter` - the usage of `RandomTranslate` is now deprecated ([#4828](https://github.com/pyg-team/pytorch_geometric/pull/4828))

test/data/test_batch.py

@@ -387,3 +387,42 @@ def test_batch_with_empty_list():
     assert batch.nontensor == [[], []]
     assert batch[0].nontensor == []
     assert batch[1].nontensor == []
+
+
+def test_nested_follow_batch():
+    def tr(n, m):
+        return torch.rand((n, m))
+
+    d1 = Data(xs=[tr(4, 3), tr(11, 4), tr(1, 2)], a={"aa": tr(11, 3)},
+              x=tr(10, 5))
+    d2 = Data(xs=[tr(5, 3), tr(14, 4), tr(3, 2)], a={"aa": tr(2, 3)},
+              x=tr(11, 5))
+    d3 = Data(xs=[tr(6, 3), tr(15, 4), tr(2, 2)], a={"aa": tr(4, 3)},
+              x=tr(9, 5))
+    d4 = Data(xs=[tr(4, 3), tr(16, 4), tr(1, 2)], a={"aa": tr(8, 3)},
+              x=tr(8, 5))
+
+    # Dataset
+    data_list = [d1, d2, d3, d4]
+
+    batch = Batch.from_data_list(data_list, follow_batch=['xs', 'a'])
+
+    # assert shapes
+    assert batch.xs[0].shape == (19, 3)
+    assert batch.xs[1].shape == (56, 4)
+    assert batch.xs[2].shape == (7, 2)
+    assert batch.a['aa'].shape == (25, 3)
+
+    assert len(batch.xs_batch) == 3
+    assert len(batch.a_batch) == 1
+
+    # assert _batch
+    assert batch.xs_batch[0].tolist() == \
+           [0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3]
+    assert batch.xs_batch[1].tolist() == \
+           [0] * 11 + [1] * 14 + [2] * 15 + [3] * 16
+    assert batch.xs_batch[2].tolist() == \
+           [0] * 1 + [1] * 3 + [2] * 2 + [3] * 1
+
+    assert batch.a_batch['aa'].tolist() == \
+           [0] * 11 + [1] * 2 + [2] * 4 + [3] * 8

test/data/test_dataset.py

@@ -1,4 +1,5 @@
 import torch
+from torch_sparse import SparseTensor
 
 from torch_geometric.data import Data, Dataset, HeteroData, InMemoryDataset
 
@@ -158,3 +159,57 @@ def _process(self):
     ds = DS3()
     assert not ds.enter_download
     assert not ds.enter_process
+
+
+class MyTestDataset2(InMemoryDataset):
+    def __init__(self, data_list):
+        super().__init__('/tmp/MyTestDataset2')
+        self.data, self.slices = self.collate(data_list)
+
+
+def test_lists_of_tensors_in_memory_dataset():
+    def tr(n, m):
+        return torch.rand((n, m))
+
+    d1 = Data(xs=[tr(4, 3), tr(11, 4), tr(1, 2)])
+    d2 = Data(xs=[tr(5, 3), tr(14, 4), tr(3, 2)])
+    d3 = Data(xs=[tr(6, 3), tr(15, 4), tr(2, 2)])
+    d4 = Data(xs=[tr(4, 3), tr(16, 4), tr(1, 2)])
+
+    data_list = [d1, d2, d3, d4]
+
+    dataset = MyTestDataset2(data_list)
+    assert len(dataset) == 4
+    assert dataset[0].xs[1].shape == (11, 4)
+    assert dataset[0].xs[2].shape == (1, 2)
+    assert dataset[1].xs[0].shape == (5, 3)
+    assert dataset[2].xs[1].shape == (15, 4)
+    assert dataset[3].xs[1].shape == (16, 4)
+
+
+class MyTestDataset3(InMemoryDataset):
+    def __init__(self, data_list):
+        super().__init__('/tmp/MyTestDataset3')
+        self.data, self.slices = self.collate(data_list)
+
+
+def test_lists_of_SparseTensors():
+    e1 = torch.tensor([[4, 1, 3, 2, 2, 3], [1, 3, 2, 3, 3, 2]])
+    e2 = torch.tensor([[0, 1, 4, 7, 2, 9], [7, 2, 2, 1, 4, 7]])
+    e3 = torch.tensor([[3, 5, 1, 2, 3, 3], [5, 0, 2, 1, 3, 7]])
+    e4 = torch.tensor([[0, 1, 9, 2, 0, 3], [1, 1, 2, 1, 3, 2]])
+    adj1 = SparseTensor.from_edge_index(e1, sparse_sizes=(11, 11))
+    adj2 = SparseTensor.from_edge_index(e2, sparse_sizes=(22, 22))
+    adj3 = SparseTensor.from_edge_index(e3, sparse_sizes=(12, 12))
+    adj4 = SparseTensor.from_edge_index(e4, sparse_sizes=(15, 15))
+
+    d1 = Data(adj_test=[adj1, adj2])
+    d2 = Data(adj_test=[adj3, adj4])
+
+    data_list = [d1, d2]
+    dataset = MyTestDataset3(data_list)
+    assert len(dataset) == 2
+    assert dataset[0].adj_test[0].sparse_sizes() == (11, 11)
+    assert dataset[0].adj_test[1].sparse_sizes() == (22, 22)
+    assert dataset[1].adj_test[0].sparse_sizes() == (12, 12)
+    assert dataset[1].adj_test[1].sparse_sizes() == (15, 15)

torch_geometric/data/collate.py

@@ -96,12 +96,10 @@ def collate(
                 inc_dict[attr] = incs
 
             # Add an additional batch vector for the given attribute:
-            if (attr in follow_batch and isinstance(slices, Tensor)
-                    and slices.dim() == 1):
-                repeats = slices[1:] - slices[:-1]
-                batch = repeat_interleave(repeats.tolist(), device=device)
+            if attr in follow_batch:
+                batch, ptr = _batch_and_ptr(slices, device)
                 out_store[f'{attr}_batch'] = batch
-                out_store[f'{attr}_ptr'] = cumsum(repeats.to(device))
+                out_store[f'{attr}_ptr'] = ptr
 
         # In case the storage holds node, we add a top-level batch vector it:
         if (add_batch and isinstance(stores[0], NodeStorage)
@@ -199,6 +197,39 @@ def _collate(
         return values, slices, None
 
 
+def _batch_and_ptr(
+    slices: Any,
+    device: Optional[torch.device] = None,
+) -> Tuple[Any, Any]:
+    if (isinstance(slices, Tensor) and slices.dim() == 1):
+        # Default case, turn slices tensor into batch.
+        repeats = slices[1:] - slices[:-1]
+        batch = repeat_interleave(repeats.tolist(), device=device)
+        ptr = cumsum(repeats.to(device))
+        return batch, ptr
+
+    elif isinstance(slices, Mapping):
+        # Recursively batch elements of dictionaries.
+        batch, ptr = {}, {}
+        for k, v in slices.items():
+            batch[k], ptr[k] = _batch_and_ptr(v, device)
+        return batch, ptr
+
+    elif (isinstance(slices, Sequence) and not isinstance(slices, str)
+          and isinstance(slices[0], Tensor)):
+        # Recursively batch elements of lists.
+        batch, ptr = [], []
+        for s in slices:
+            sub_batch, sub_ptr = _batch_and_ptr(s, device)
+            batch.append(sub_batch)
+            ptr.append(sub_ptr)
+        return batch, ptr
+
+    else:
+        # Failure of batching, usually due to slices.dim() != 1
+        return None, None
+
+
 ###############################################################################
 
 

torch_geometric/data/in_memory_dataset.py

@@ -1,5 +1,5 @@
 import copy
-from collections.abc import Mapping
+from collections.abc import Mapping, Sequence
 from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union
 
 import torch
@@ -130,10 +130,13 @@ def copy(self, idx: Optional[IndexType] = None) -> 'InMemoryDataset':
         return dataset
 
 
-def nested_iter(mapping: Mapping) -> Iterable:
-    for key, value in mapping.items():
-        if isinstance(value, Mapping):
+def nested_iter(node: Union[Mapping, Sequence]) -> Iterable:
+    if isinstance(node, Mapping):
+        for key, value in node.items():
             for inner_key, inner_value in nested_iter(value):
                 yield inner_key, inner_value
-        else:
-            yield key, value
+    elif isinstance(node, Sequence):
+        for i, inner_value in enumerate(node):
+            yield i, inner_value
+    else:
+        yield None, node

torch_geometric/data/separate.py

@@ -90,6 +90,11 @@ def _separate(
           and not isinstance(value[0], str) and len(value[0]) > 0
           and isinstance(value[0][0], (Tensor, SparseTensor))):
         # Recursively separate elements of lists of lists.
+        return [elem[idx] for elem in value]
+
+    elif (isinstance(value, Sequence) and not isinstance(value, str)
+          and isinstance(value[0], (Tensor, SparseTensor))):
+        # Recursively separate elements of lists of Tensors/SparseTensors.
         return [
             _separate(key, elem, idx, slices[i],
                       incs[i] if decrement else None, batch, store, decrement)