Graph embedding and graph backbones

.github/workflows/ci-testing.yml

@@ -145,6 +145,7 @@ jobs:
         pip install torch==1.9.0+cpu torchvision==0.10.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
         pip install torch-scatter -f https://data.pyg.org/whl/torch-1.9.0+cpu.html
         pip install torch-sparse -f https://data.pyg.org/whl/torch-1.9.0+cpu.html
+        pip install torch-cluster -f https://data.pyg.org/whl/torch-1.9.0+cpu.html
 
     - name: Install dependencies
       run: |

CHANGELOG.md

@@ -8,6 +8,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added backbones for `GraphClassifier` ([#592](https://github.com/PyTorchLightning/lightning-flash/pull/592))
+
+- Added `GraphEmbedder` task ([#592](https://github.com/PyTorchLightning/lightning-flash/pull/592))
+
 ### Changed
 
 - Changed `Preprocess` to `InputTransform` ([#951](https://github.com/PyTorchLightning/lightning-flash/pull/951))

docs/source/api/graph.rst

@@ -22,6 +22,16 @@ ______________
 
     classification.data.GraphClassificationInputTransform
 
+Embedding
+_________
+
+.. autosummary::
+    :toctree: generated/
+    :nosignatures:
+    :template: classtemplate.rst
+
+    ~embedding.model.GraphEmbedder
+
 flash.graph.data
 ________________
 

docs/source/index.rst

@@ -85,6 +85,7 @@ Lightning Flash
    :caption: Graph
 
    reference/graph_classification
+   reference/graph_embedder
 
 .. toctree::
    :maxdepth: 1

docs/source/reference/graph_embedder.rst

@@ -0,0 +1,28 @@
+.. _graph_embedder:
+
+##############
+Graph Embedder
+##############
+
+********
+The Task
+********
+This task consists of creating an embedding of a graph. That is, a vector of features which can be used for a downstream task.
+The :class:`~flash.graph.classification.model.GraphEmbedder` and :class:`~flash.graph.classification.data.GraphClassificationData` classes internally rely on `pytorch-geometric <https://github.com/rusty1s/pytorch_geometric>`_.
+
+------
+
+*******
+Example
+*******
+
+Let's look at generating embeddings of graphs from the KKI data set from `TU Dortmund University <https://chrsmrrs.github.io/datasets>`_.
+
+We start by creating the `TUDataset <https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/datasets/tu_dataset.html#TUDataset>`.
+Next, we load a trained :class:`~flash.graph.classification.model.GraphEmbedder` (from a previously trained :class:`~flash.graph.classification.model.GraphClassifier`).
+Finally, we save the model.
+Here's the full example:
+
+.. literalinclude:: ../../../flash_examples/graph_embedder.py
+    :language: python
+    :lines: 14

flash/core/utilities/imports.py

@@ -93,6 +93,7 @@ def _compare_version(package: str, op, version) -> bool:
 _TORCH_SCATTER_AVAILABLE = _module_available("torch_scatter")
 _TORCH_SPARSE_AVAILABLE = _module_available("torch_sparse")
 _TORCH_GEOMETRIC_AVAILABLE = _module_available("torch_geometric")
+_NETWORKX_AVAILABLE = _module_available("networkx")
 _TORCHAUDIO_AVAILABLE = _module_available("torchaudio")
 _SENTENCEPIECE_AVAILABLE = _module_available("sentencepiece")
 _DATASETS_AVAILABLE = _module_available("datasets")
@@ -143,7 +144,9 @@ class Image:
 _SERVE_AVAILABLE = _FASTAPI_AVAILABLE and _PYDANTIC_AVAILABLE and _CYTOOLZ_AVAILABLE and _UVICORN_AVAILABLE
 _POINTCLOUD_AVAILABLE = _OPEN3D_AVAILABLE and _TORCHVISION_AVAILABLE
 _AUDIO_AVAILABLE = all([_TORCHAUDIO_AVAILABLE, _LIBROSA_AVAILABLE, _TRANSFORMERS_AVAILABLE])
-_GRAPH_AVAILABLE = _TORCH_SCATTER_AVAILABLE and _TORCH_SPARSE_AVAILABLE and _TORCH_GEOMETRIC_AVAILABLE
+_GRAPH_AVAILABLE = (
+    _TORCH_SCATTER_AVAILABLE and _TORCH_SPARSE_AVAILABLE and _TORCH_GEOMETRIC_AVAILABLE and _NETWORKX_AVAILABLE
+)
 
 _EXTRAS_AVAILABLE = {
     "image": _IMAGE_AVAILABLE,

flash/core/utilities/providers.py

@@ -47,3 +47,4 @@ def __str__(self):
 _PYTORCHVIDEO = Provider("Facebook Research/PyTorchVideo", "https://github.com/facebookresearch/pytorchvideo")
 _VISSL = Provider("Facebook Research/vissl", "https://github.com/facebookresearch/vissl")
 _PYTORCH_FORECASTING = Provider("jdb78/PyTorch-Forecasting", "https://github.com/jdb78/pytorch-forecasting")
+_PYTORCH_GEOMETRIC = Provider("PyG/PyTorch Geometric", "https://github.com/pyg-team/pytorch_geometric")

flash/graph/__init__.py

@@ -1 +1,2 @@
 from flash.graph.classification import GraphClassificationData, GraphClassifier  # noqa: F401
+from flash.graph.embedding import GraphEmbedder  # noqa: F401

flash/graph/backbones.py

@@ -0,0 +1,41 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from functools import partial
+
+from flash.core.registry import FlashRegistry
+from flash.core.utilities.imports import _GRAPH_AVAILABLE
+from flash.core.utilities.providers import _PYTORCH_GEOMETRIC
+
+if _GRAPH_AVAILABLE:
+    from torch_geometric.nn.models import GAT, GCN, GIN, GraphSAGE
+
+    MODELS = {"GCN": GCN, "GraphSAGE": GraphSAGE, "GAT": GAT, "GIN": GIN}
+else:
+    MODELS = {}
+
+GRAPH_BACKBONES = FlashRegistry("backbones")
+
+
+def _load_graph_backbone(
+    model_name: str,
+    in_channels: int,
+    hidden_channels: int = 512,
+    num_layers: int = 4,
+):
+    model = MODELS[model_name]
+    return model(in_channels, hidden_channels, num_layers)
+
+
+for model_name in MODELS.keys():
+    GRAPH_BACKBONES(name=model_name, providers=_PYTORCH_GEOMETRIC)(partial(_load_graph_backbone, model_name))

flash/graph/classification/model.py

@@ -11,157 +11,90 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Callable, List, Optional, Type, Union
+from typing import Any, Callable, Dict, Optional, Tuple, Union
 
 import torch
-from torch import nn, Tensor
+from torch import nn
 from torch.nn import functional as F
 from torch.nn import Linear
 
 from flash.core.classification import ClassificationTask
+from flash.core.registry import FlashRegistry
 from flash.core.utilities.imports import _GRAPH_AVAILABLE
 from flash.core.utilities.types import LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE
+from flash.graph.backbones import GRAPH_BACKBONES
 
 if _GRAPH_AVAILABLE:
-    from torch_geometric.nn import BatchNorm, GCNConv, global_mean_pool, MessagePassing
-else:
-    MessagePassing = object
-    GCNConv = object
-
-
-class GraphBlock(nn.Module):
-    """Graph convolutional block.
-
-    Args:
-        nc_input: number of input channels
-        nc_output: number of output channels
-        conv_cls: graph convolutional class to use
-        act: activation function to use
-        **conv_kwargs: additional kwargs used for initialization of convolutional operator
-    """
-
-    def __init__(
-        self,
-        nc_input: int,
-        nc_output: int,
-        conv_cls: nn.Module,
-        act: Union[Callable, nn.Module] = nn.ReLU(),
-        **conv_kwargs
-    ):
-        super().__init__()
-        self.conv = conv_cls(nc_input, nc_output, **conv_kwargs)
-        self.norm = BatchNorm(nc_output)
-        self.act = act
+    from torch_geometric.nn import global_add_pool, global_max_pool, global_mean_pool
 
-    def forward(self, x: Tensor, edge_index: Tensor, edge_weight: Optional[Tensor] = None) -> Tensor:
-        x = self.conv(x, edge_index, edge_weight=edge_weight)
-        x = self.norm(x)
-        return self.act(x)
-
-
-class BaseGraphModel(nn.Module):
-    """Base convolutional graph model.
-
-    Args:
-        num_features: number of input features
-        hidden_channels: list of integers with the number of channels in all the hidden layers.
-            The length of the list determines the depth of the network.
-        num_classes: integer determining the number of classes
-        conv_cls: graph convolutional class to use as building blocks
-        act: activation function to use between layers
-        **conv_kwargs: additional kwargs used for initialization of convolutional operator
-    """
-
-    def __init__(
-        self,
-        num_features: int,
-        hidden_channels: List[int],
-        num_classes: int,
-        conv_cls: Type[MessagePassing],
-        act: Union[Callable, nn.Module] = nn.ReLU(),
-        **conv_kwargs: Any
-    ):
-        super().__init__()
-
-        self.blocks = nn.ModuleList()
-        hidden_channels = [num_features] + hidden_channels
-
-        nc_output = num_features
-
-        for idx in range(len(hidden_channels) - 1):
-            nc_input = hidden_channels[idx]
-            nc_output = hidden_channels[idx + 1]
-            graph_block = GraphBlock(nc_input, nc_output, conv_cls, act, **conv_kwargs)
-            self.blocks.append(graph_block)
-
-        self.lin = Linear(nc_output, num_classes)
-
-    def forward(self, data: Any) -> Tensor:
-        x, edge_index, edge_weight = data.x, data.edge_index, data.edge_attr
-        # 1. Obtain node embeddings
-        for block in self.blocks:
-            x = block(x, edge_index, edge_weight)
-
-        # 2. Readout layer
-        x = global_mean_pool(x, data.batch)  # [batch_size, hidden_channels]
-
-        # 3. Apply a final classifier
-        x = F.dropout(x, p=0.5, training=self.training)
-        x = self.lin(x)
-        return x
+    POOLING_FUNCTIONS = {"mean": global_mean_pool, "add": global_add_pool, "max": global_max_pool}
+else:
+    POOLING_FUNCTIONS = {}
 
 
 class GraphClassifier(ClassificationTask):
     """The ``GraphClassifier`` is a :class:`~flash.Task` for classifying graphs. For more details, see
     :ref:`graph_classification`.
 
     Args:
-        num_features: Number of columns in table (not including target column).
-        num_classes: Number of classes to classify.
-        hidden_channels: Hidden dimension sizes.
-        learning_rate: Learning rate to use for training, defaults to `1e-3`
+        num_features (int): The number of features in the input.
+        num_classes (int): Number of classes to classify.
+        backbone: Name of the backbone to use.
+        backbone_kwargs: Dictionary dependent on the backbone, containing for example in_channels, out_channels,
+            hidden_channels or depth (number of layers).
+        pooling_fn: The global pooling operation to use (one of: "max", "max", "add" or a callable).
+        head: The head to use.
+        loss_fn: Loss function for training, defaults to cross entropy.
+        learning_rate: Learning rate to use for training.
         optimizer: Optimizer to use for training.
         lr_scheduler: The LR scheduler to use during training.
         metrics: Metrics to compute for training and evaluation.
-        model: GraphNN used, defaults to BaseGraphModel.
-        conv_cls: kind of convolution used in model, defaults to GCNConv
-        **conv_kwargs: additional kwargs used for initialization of convolutional operator
     """
 
-    required_extras = "graph"
+    backbones: FlashRegistry = GRAPH_BACKBONES
+
+    required_extras: str = "graph"
 
     def __init__(
         self,
         num_features: int,
         num_classes: int,
-        hidden_channels: Union[List[int], int] = 512,
-        model: torch.nn.Module = None,
+        backbone: Union[str, Tuple[nn.Module, int]] = "GCN",
+        backbone_kwargs: Optional[Dict] = {},
+        pooling_fn: Optional[Union[str, Callable]] = "mean",
+        head: Optional[Union[Callable, nn.Module]] = None,
         loss_fn: LOSS_FN_TYPE = F.cross_entropy,
         learning_rate: float = 1e-3,
         optimizer: OPTIMIZER_TYPE = "Adam",
         lr_scheduler: LR_SCHEDULER_TYPE = None,
         metrics: METRICS_TYPE = None,
-        conv_cls: Type[MessagePassing] = GCNConv,
-        **conv_kwargs
     ):
 
         self.save_hyperparameters()
 
-        if isinstance(hidden_channels, int):
-            hidden_channels = [hidden_channels]
-
-        if not model:
-            model = BaseGraphModel(num_features, hidden_channels, num_classes, conv_cls, **conv_kwargs)
-
         super().__init__(
-            model=model,
             loss_fn=loss_fn,
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
             metrics=metrics,
             learning_rate=learning_rate,
         )
 
+        self.save_hyperparameters()
+
+        if isinstance(backbone, tuple):
+            self.backbone, num_out_features = backbone
+        else:
+            self.backbone = self.backbones.get(backbone)(in_channels=num_features, **backbone_kwargs)
+            num_out_features = self.backbone.hidden_channels
+
+        self.pooling_fn = POOLING_FUNCTIONS[pooling_fn] if isinstance(pooling_fn, str) else pooling_fn
+
+        if head is not None:
+            self.head = head
+        else:
+            self.head = DefaultGraphHead(num_out_features, num_classes)
+
     def training_step(self, batch: Any, batch_idx: int) -> Any:
         batch = (batch, batch.y)
         return super().training_step(batch, batch_idx)
@@ -176,3 +109,25 @@ def test_step(self, batch: Any, batch_idx: int) -> Any:
 
     def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
         return super().predict_step(batch, batch_idx, dataloader_idx=dataloader_idx)
+
+    def forward(self, data) -> torch.Tensor:
+        x = self.backbone(data.x, data.edge_index)
+        x = self.pooling_fn(x, data.batch)
+        return self.head(x)
+
+
+class DefaultGraphHead(torch.nn.Module):
+    def __init__(self, hidden_channels, num_classes, dropout=0.5):
+        super().__init__()
+        self.lin1 = Linear(hidden_channels, hidden_channels)
+        self.lin2 = Linear(hidden_channels, num_classes)
+        self.dropout = dropout
+
+    def reset_parameters(self):
+        self.lin1.reset_parameters()
+        self.lin2.reset_parameters()
+
+    def forward(self, x):
+        x = F.relu(self.lin1(x))
+        x = F.dropout(x, p=self.dropout, training=self.training)
+        return self.lin2(x)

flash/graph/embedding/__init__.py

@@ -0,0 +1,2 @@
+from flash.graph.classification.data import GraphClassificationData  # noqa: F401
+from flash.graph.embedding.model import GraphEmbedder  # noqa: F401